2023/08/03 - Amazon SageMaker Service - 7 updated api methods
Changes Amazon SageMaker now supports running training jobs on p5.48xlarge instance types.
{'TrainingSpecification': {'SupportedTrainingInstanceTypes': {'ml.p5.48xlarge'}},
'ValidationSpecification': {'ValidationProfiles': {'TrainingJobDefinition': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}}}}
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
client.create_algorithm(
AlgorithmName='string',
AlgorithmDescription='string',
TrainingSpecification={
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File'|'FastFile',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
]
},
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the algorithm.
string
A description of the algorithm.
dict
[REQUIRED]
Specifies details about training jobs run by this algorithm, including the following:
The Amazon ECR path of the container and the version digest of the algorithm.
The hyperparameters that the algorithm supports.
The instance types that the algorithm supports for training.
Whether the algorithm supports distributed training.
The metrics that the algorithm emits to Amazon CloudWatch.
Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
The input channels that the algorithm supports for training data. For example, an algorithm might support train , validation , and test channels.
TrainingImage (string) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters (list) --
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
(dict) --
Defines a hyperparameter to be used by an algorithm.
Name (string) -- [REQUIRED]
The name of this hyperparameter. The name must be unique.
Description (string) --
A brief description of the hyperparameter.
Type (string) -- [REQUIRED]
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
Range (dict) --
The allowed range for this hyperparameter.
IntegerParameterRangeSpecification (dict) --
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
MinValue (string) -- [REQUIRED]
The minimum integer value allowed.
MaxValue (string) -- [REQUIRED]
The maximum integer value allowed.
ContinuousParameterRangeSpecification (dict) --
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
MinValue (string) -- [REQUIRED]
The minimum floating-point value allowed.
MaxValue (string) -- [REQUIRED]
The maximum floating-point value allowed.
CategoricalParameterRangeSpecification (dict) --
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
Values (list) -- [REQUIRED]
The allowed categories for the hyperparameter.
(string) --
IsTunable (boolean) --
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired (boolean) --
Indicates whether this hyperparameter is required.
DefaultValue (string) --
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes (list) -- [REQUIRED]
A list of the instance types that this algorithm can use for training.
(string) --
SupportsDistributedTraining (boolean) --
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions (list) --
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
TrainingChannels (list) -- [REQUIRED]
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
(dict) --
Defines a named input source, called a channel, to be used by an algorithm.
Name (string) -- [REQUIRED]
The name of the channel.
Description (string) --
A brief description of the channel.
IsRequired (boolean) --
Indicates whether the channel is required by the algorithm.
SupportedContentTypes (list) -- [REQUIRED]
The supported MIME types for the data.
(string) --
SupportedCompressionTypes (list) --
The allowed compression types, if data compression is used.
(string) --
SupportedInputModes (list) -- [REQUIRED]
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
(string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
SupportedTuningJobObjectiveMetrics (list) --
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
(dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
dict
Specifies details about inference jobs that the algorithm runs, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the algorithm supports for inference.
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) -- [REQUIRED]
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) -- [REQUIRED]
The supported MIME types for the output data.
(string) --
dict
Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.
ValidationRole (string) -- [REQUIRED]
The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles (list) -- [REQUIRED]
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.
(dict) --
Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) -- [REQUIRED]
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition (dict) -- [REQUIRED]
The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
HyperParameters (dict) --
The hyperparameters used for the training job.
(string) --
(string) --
InputDataConfig (list) -- [REQUIRED]
An array of Channel objects, each of which specifies an input source.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) -- [REQUIRED]
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) -- [REQUIRED]
The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) -- [REQUIRED]
A description of the input source and the way the transform job consumes it.
DataSource (dict) -- [REQUIRED]
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) -- [REQUIRED]
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) -- [REQUIRED]
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) -- [REQUIRED]
Identifies the ML compute instances for the transform job.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
boolean
Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'AlgorithmArn': 'string'
}
Response Structure
(dict) --
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the new algorithm.
{'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}},
'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}}
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components .
Warning
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
See also: AWS API Documentation
Request Syntax
client.create_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string',
HyperParameterTuningJobConfig={
'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
'StrategyConfig': {
'HyperbandStrategyConfig': {
'MinResource': 123,
'MaxResource': 123
}
},
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...,
'BestObjectiveNotImproving': {
'MaxNumberOfTrainingJobsNotImproving': 123
},
'ConvergenceDetected': {
'CompleteOnConvergence': 'Disabled'|'Enabled'
}
},
'RandomSeed': 123
},
TrainingJobDefinition={
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'Environment': {
'string': 'string'
}
},
TrainingJobDefinitions=[
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'Environment': {
'string': 'string'
}
},
],
WarmStartConfig={
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
Autotune={
'Mode': 'Enabled'
}
)
string
[REQUIRED]
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
dict
[REQUIRED]
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works .
Strategy (string) -- [REQUIRED]
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .
StrategyConfig (dict) --
The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .
HyperbandStrategyConfig (dict) --
The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
MinResource (integer) --
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .
MaxResource (integer) --
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
HyperParameterTuningJobObjective (dict) --
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
ResourceLimits (dict) -- [REQUIRED]
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) -- [REQUIRED]
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges (dict) --
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) -- [REQUIRED]
The name of the hyperparameter to search.
MinValue (string) -- [REQUIRED]
The minimum value of the hyperparameter to search.
MaxValue (string) -- [REQUIRED]
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the continuous hyperparameter to tune.
MinValue (string) -- [REQUIRED]
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) -- [REQUIRED]
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the categorical hyperparameter to tune.
Values (list) -- [REQUIRED]
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) -- [REQUIRED]
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) -- [REQUIRED]
An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType (string) --
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
TuningJobCompletionCriteria (dict) --
The tuning job's completion criteria.
TargetObjectiveMetricValue (float) --
The value of the objective metric.
BestObjectiveNotImproving (dict) --
A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving (integer) --
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected (dict) --
A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence (string) --
A flag to stop a tuning job once AMT has detected that the job has converged.
RandomSeed (integer) --
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
dict
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) -- [REQUIRED]
The name of the hyperparameter to search.
MinValue (string) -- [REQUIRED]
The minimum value of the hyperparameter to search.
MaxValue (string) -- [REQUIRED]
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the continuous hyperparameter to tune.
MinValue (string) -- [REQUIRED]
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) -- [REQUIRED]
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the categorical hyperparameter to tune.
Values (list) -- [REQUIRED]
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) -- [REQUIRED]
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) -- [REQUIRED]
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) -- [REQUIRED]
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) -- [REQUIRED]
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) -- [REQUIRED]
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) -- [REQUIRED]
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) -- [REQUIRED]
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
list
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
(dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) -- [REQUIRED]
The name of the hyperparameter to search.
MinValue (string) -- [REQUIRED]
The minimum value of the hyperparameter to search.
MaxValue (string) -- [REQUIRED]
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the continuous hyperparameter to tune.
MinValue (string) -- [REQUIRED]
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) -- [REQUIRED]
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the categorical hyperparameter to tune.
Values (list) -- [REQUIRED]
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) -- [REQUIRED]
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) -- [REQUIRED]
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) -- [REQUIRED]
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) -- [REQUIRED]
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) -- [REQUIRED]
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) -- [REQUIRED]
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) -- [REQUIRED]
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
dict
Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
Note
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
ParentHyperParameterTuningJobs (list) -- [REQUIRED]
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
(dict) --
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType (string) -- [REQUIRED]
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
ParameterRanges : The names and ranges of parameters that a hyperparameter tuning job can optimize.
ResourceLimits : The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
TrainingJobEarlyStoppingType : A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
RetryStrategy : The number of times to retry a training job.
Strategy : Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
ConvergenceDetected : A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
Mode (string) -- [REQUIRED]
Set Mode to Enabled if you want to use Autotune.
dict
Response Syntax
{
'HyperParameterTuningJobArn': 'string'
}
Response Structure
(dict) --
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
{'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .
Warning
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training .
RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.
StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
Environment - The environment variables to set in the Docker container.
RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError .
For more information about SageMaker, see How It Works .
See also: AWS API Documentation
Request Syntax
client.create_training_job(
TrainingJobName='string',
HyperParameters={
'string': 'string'
},
AlgorithmSpecification={
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
RoleArn='string',
InputDataConfig=[
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
ResourceConfig={
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
EnableNetworkIsolation=True|False,
EnableInterContainerTrafficEncryption=True|False,
EnableManagedSpotTraining=True|False,
CheckpointConfig={
'S3Uri': 'string',
'LocalPath': 'string'
},
DebugHookConfig={
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
DebugRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
TensorBoardOutputConfig={
'LocalPath': 'string',
'S3OutputPath': 'string'
},
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
ProfilerConfig={
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
ProfilerRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
Environment={
'string': 'string'
},
RetryStrategy={
'MaximumRetryAttempts': 123
}
)
string
[REQUIRED]
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
dict
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint .
Warning
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
(string) --
(string) --
dict
[REQUIRED]
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms . For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker .
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) -- [REQUIRED]
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles .
Note
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
list
An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data . The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
dict
[REQUIRED]
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
dict
[REQUIRED]
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
dict
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
dict
[REQUIRED]
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
boolean
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
boolean
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job .
boolean
To train models using managed spot training, choose True . Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
dict
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
dict
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) -- [REQUIRED]
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
list
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) -- [REQUIRED]
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) -- [REQUIRED]
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
dict
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) -- [REQUIRED]
Path to Amazon S3 storage location for TensorBoard output.
dict
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
dict
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
list
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
(dict) --
Configuration information for profiling rules.
RuleConfigurationName (string) -- [REQUIRED]
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) -- [REQUIRED]
The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for profiling a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
dict
The environment variables to set in the Docker container.
(string) --
(string) --
dict
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) -- [REQUIRED]
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
dict
Response Syntax
{
'TrainingJobArn': 'string'
}
Response Structure
(dict) --
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
{'TrainingSpecification': {'SupportedTrainingInstanceTypes': {'ml.p5.48xlarge'}},
'ValidationSpecification': {'ValidationProfiles': {'TrainingJobDefinition': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}}}}
Returns a description of the specified algorithm that is in your account.
See also: AWS API Documentation
Request Syntax
client.describe_algorithm(
AlgorithmName='string'
)
string
[REQUIRED]
The name of the algorithm to describe.
dict
Response Syntax
{
'AlgorithmName': 'string',
'AlgorithmArn': 'string',
'AlgorithmDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingSpecification': {
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File'|'FastFile',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
]
},
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'AlgorithmStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'ProductId': 'string',
'CertifyForMarketplace': True|False
}
Response Structure
(dict) --
AlgorithmName (string) --
The name of the algorithm being described.
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm.
AlgorithmDescription (string) --
A brief summary about the algorithm.
CreationTime (datetime) --
A timestamp specifying when the algorithm was created.
TrainingSpecification (dict) --
Details about training jobs run by this algorithm.
TrainingImage (string) --
The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters (list) --
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
(dict) --
Defines a hyperparameter to be used by an algorithm.
Name (string) --
The name of this hyperparameter. The name must be unique.
Description (string) --
A brief description of the hyperparameter.
Type (string) --
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
Range (dict) --
The allowed range for this hyperparameter.
IntegerParameterRangeSpecification (dict) --
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
MinValue (string) --
The minimum integer value allowed.
MaxValue (string) --
The maximum integer value allowed.
ContinuousParameterRangeSpecification (dict) --
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
MinValue (string) --
The minimum floating-point value allowed.
MaxValue (string) --
The maximum floating-point value allowed.
CategoricalParameterRangeSpecification (dict) --
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
Values (list) --
The allowed categories for the hyperparameter.
(string) --
IsTunable (boolean) --
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired (boolean) --
Indicates whether this hyperparameter is required.
DefaultValue (string) --
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes (list) --
A list of the instance types that this algorithm can use for training.
(string) --
SupportsDistributedTraining (boolean) --
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions (list) --
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
TrainingChannels (list) --
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
(dict) --
Defines a named input source, called a channel, to be used by an algorithm.
Name (string) --
The name of the channel.
Description (string) --
A brief description of the channel.
IsRequired (boolean) --
Indicates whether the channel is required by the algorithm.
SupportedContentTypes (list) --
The supported MIME types for the data.
(string) --
SupportedCompressionTypes (list) --
The allowed compression types, if data compression is used.
(string) --
SupportedInputModes (list) --
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
(string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
SupportedTuningJobObjectiveMetrics (list) --
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
(dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
InferenceSpecification (dict) --
Details about inference jobs that the algorithm runs.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
ValidationSpecification (dict) --
Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.
ValidationRole (string) --
The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles (list) --
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.
(dict) --
Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition (dict) --
The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
HyperParameters (dict) --
The hyperparameters used for the training job.
(string) --
(string) --
InputDataConfig (list) --
An array of Channel objects, each of which specifies an input source.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
AlgorithmStatus (string) --
The current status of the algorithm.
AlgorithmStatusDetails (dict) --
Details about the current status of the algorithm.
ValidationStatuses (list) --
The status of algorithm validation.
(dict) --
Represents the overall status of an algorithm.
Name (string) --
The name of the algorithm for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the algorithm's Docker image container.
(dict) --
Represents the overall status of an algorithm.
Name (string) --
The name of the algorithm for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ProductId (string) --
The product identifier of the algorithm.
CertifyForMarketplace (boolean) --
Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
{'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}},
'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}}
Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
See also: AWS API Documentation
Request Syntax
client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string'
)
string
[REQUIRED]
The name of the tuning job.
dict
Response Syntax
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobConfig': {
'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
'StrategyConfig': {
'HyperbandStrategyConfig': {
'MinResource': 123,
'MaxResource': 123
}
},
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...,
'BestObjectiveNotImproving': {
'MaxNumberOfTrainingJobsNotImproving': 123
},
'ConvergenceDetected': {
'CompleteOnConvergence': 'Disabled'|'Enabled'
}
},
'RandomSeed': 123
},
'TrainingJobDefinition': {
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'Environment': {
'string': 'string'
}
},
'TrainingJobDefinitions': [
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'Environment': {
'string': 'string'
}
},
],
'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'CreationTime': datetime(2015, 1, 1),
'HyperParameterTuningEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatusCounters': {
'Completed': 123,
'InProgress': 123,
'RetryableError': 123,
'NonRetryableError': 123,
'Stopped': 123
},
'ObjectiveStatusCounters': {
'Succeeded': 123,
'Pending': 123,
'Failed': 123
},
'BestTrainingJob': {
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'OverallBestTrainingJob': {
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'WarmStartConfig': {
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
'FailureReason': 'string',
'TuningJobCompletionDetails': {
'NumberOfTrainingJobsObjectiveNotImproving': 123,
'ConvergenceDetectedTime': datetime(2015, 1, 1)
},
'ConsumedResources': {
'RuntimeInSeconds': 123
},
'Autotune': {
'Mode': 'Enabled'
}
}
Response Structure
(dict) --
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobConfig (dict) --
The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
Strategy (string) --
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .
StrategyConfig (dict) --
The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .
HyperbandStrategyConfig (dict) --
The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
MinResource (integer) --
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .
MaxResource (integer) --
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
HyperParameterTuningJobObjective (dict) --
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges (dict) --
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType (string) --
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
TuningJobCompletionCriteria (dict) --
The tuning job's completion criteria.
TargetObjectiveMetricValue (float) --
The value of the objective metric.
BestObjectiveNotImproving (dict) --
A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving (integer) --
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected (dict) --
A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence (string) --
A flag to stop a tuning job once AMT has detected that the job has converged.
RandomSeed (integer) --
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition (dict) --
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
TrainingJobDefinitions (list) --
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
(dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
HyperParameterTuningJobStatus (string) --
The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.
CreationTime (datetime) --
The date and time that the tuning job started.
HyperParameterTuningEndTime (datetime) --
The date and time that the tuning job ended.
LastModifiedTime (datetime) --
The date and time that the status of the tuning job was modified.
TrainingJobStatusCounters (dict) --
The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
BestTrainingJob (dict) --
A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective .
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
OverallBestTrainingJob (dict) --
If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM , this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
WarmStartConfig (dict) --
The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
ParentHyperParameterTuningJobs (list) --
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
(dict) --
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType (string) --
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
FailureReason (string) --
If the tuning job failed, the reason it failed.
TuningJobCompletionDetails (dict) --
Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.
NumberOfTrainingJobsObjectiveNotImproving (integer) --
The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
ConvergenceDetectedTime (datetime) --
The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConsumedResources (dict) --
The total resources consumed by your hyperparameter tuning job.
RuntimeInSeconds (integer) --
The wall clock runtime in seconds used by your hyperparameter tuning job.
Autotune (dict) --
A flag to indicate if autotune is enabled for the hyperparameter tuning job.
Mode (string) --
Set Mode to Enabled if you want to use Autotune.
{'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}
Returns information about a training job.
Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason , attributes like TrainingStartTime , TrainingTimeInSeconds , TrainingEndTime , and BillableTimeInSeconds may not be present in the response.
See also: AWS API Documentation
Request Syntax
client.describe_training_job(
TrainingJobName='string'
)
string
[REQUIRED]
The name of the training job.
dict
Response Syntax
{
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
'ProfilerRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'ProfilerRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilingStatus': 'Enabled'|'Disabled',
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Environment': {
'string': 'string'
},
'WarmPoolStatus': {
'Status': 'Available'|'Terminated'|'Reused'|'InUse',
'ResourceRetainedBillableTimeInSeconds': 123,
'ReusedByJob': 'string'
}
}
Response Structure
(dict) --
TrainingJobName (string) --
Name of the model training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of an AutoML job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Interrupted - The job stopped because the managed spot training instances were interrupted.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTraining
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) --
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of SecondaryStatusTransitions for DescribeTrainingJob . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True . If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply BillableTimeInSeconds by the number of instances (InstanceCount ) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100 . For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
DebugHookConfig (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
DebugRuleConfigurations (list) --
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilerConfig (dict) --
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
ProfilerRuleConfigurations (list) --
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
(dict) --
Configuration information for profiling rules.
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for profiling a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
ProfilerRuleEvaluationStatuses (list) --
Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilingStatus (string) --
Profiling status of a training job.
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
WarmPoolStatus (dict) --
The status of the warm pool associated with the training job.
Status (string) --
The status of the warm pool.
InUse : The warm pool is in use for the training job.
Available : The warm pool is available to reuse for a matching training job.
Reused : The warm pool moved to a matching training job for reuse.
Terminated : The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specified KeepAlivePeriodInSeconds .
ResourceRetainedBillableTimeInSeconds (integer) --
The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time.
Multiply ResourceRetainedBillableTimeInSeconds by the number of instances (InstanceCount ) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount .
ReusedByJob (string) --
The name of the matching training job that reused the warm pool.
{'Results': {'HyperParameterTuningJob': {'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}},
'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}},
'TrainingJob': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}},
'TrialComponent': {'SourceDetail': {'TrainingJob': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.p5.48xlarge'}},
'InstanceType': {'ml.p5.48xlarge'}}}}}}}
Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
Note
The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
See also: AWS API Documentation
Request Syntax
client.search(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup'|'Project'|'FeatureMetadata'|'HyperParameterTuningJob'|'ModelCard'|'Model',
SearchExpression={
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
],
'NestedFilters': [
{
'NestedPropertyName': 'string',
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
]
},
],
'SubExpressions': [
{'... recursive ...'},
],
'Operator': 'And'|'Or'
},
SortBy='string',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
CrossAccountFilterOption='SameAccount'|'CrossAccount'
)
string
[REQUIRED]
The name of the SageMaker resource to search for.
dict
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions , NestedFilters , and Filters that can be included in a SearchExpression object is 50.
Filters (list) --
A list of filter objects.
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
NestedFilters (list) --
A list of nested filter objects.
(dict) --
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',
'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'
NestedPropertyName (string) -- [REQUIRED]
The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .
Filters (list) -- [REQUIRED]
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
SubExpressions (list) --
A list of search expression objects.
(dict) --
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.
A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.
A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.
A Boolean operator: And or Or .
Operator (string) --
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .
string
The name of the resource property used to sort the SearchResults . The default is LastModifiedTime .
string
How SearchResults are ordered. Valid values are Ascending or Descending . The default is Descending .
string
If more than MaxResults resources match the specified SearchExpression , the response includes a NextToken . The NextToken can be passed to the next SearchRequest to continue retrieving results.
integer
The maximum number of results to return.
string
A cross account filter option. When the value is "CrossAccount" the search results will only include resources made discoverable to you from other accounts. When the value is "SameAccount" or null the search results will only include resources from your account. Default is null . For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of ResourceCatalog s viewable is 1000.
dict
Response Syntax
{
'Results': [
{
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Experiment': {
'ExperimentName': 'string',
'ExperimentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Trial': {
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
}
},
]
},
'TrialComponent': {
'TrialComponentName': 'string',
'DisplayName': 'string',
'TrialComponentArn': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
],
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'SourceDetail': {
'SourceArn': 'string',
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ProcessingJob': {
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'TransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
'LineageGroupArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'Parents': [
{
'TrialName': 'string',
'ExperimentName': 'string'
},
],
'RunName': 'string'
},
'Endpoint': {
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
}
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringSchedules': [
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
},
'BatchTransformInput': {
'DataCapturedDestinationS3Uri': 'string',
'DatasetFormat': {
'Csv': {
'Header': True|False
},
'Json': {
'Line': True|False
},
'Parquet': {}
},
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'ShadowProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
}
},
]
},
'ModelPackage': {
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ApprovalDescription': 'string',
'Domain': 'string',
'Task': 'string',
'SamplePayloadUrl': 'string',
'AdditionalInferenceSpecifications': [
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
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],
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],
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]
},
],
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{
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},
],
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},
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},
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},
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}
},
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},
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},
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},
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},
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}
}
}
},
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'CreationTime': datetime(2015, 1, 1),
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'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
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'PipelineDisplayName': 'string',
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'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
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}
},
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}
},
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},
'Tags': [
{
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},
]
},
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'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
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},
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'LastModifiedTime': datetime(2015, 1, 1),
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}
},
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}
},
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},
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},
],
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{
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},
]
}
},
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{
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},
],
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
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},
'EnableOnlineStore': True|False,
'TtlDuration': {
'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks',
'Value': 123
}
},
'OfflineStoreConfig': {
'S3StorageConfig': {
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'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
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'Catalog': 'string',
'Database': 'string'
},
'TableFormat': 'Glue'|'Iceberg'
},
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'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
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},
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},
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{
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]
},
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{
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},
]
},
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},
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}
},
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{
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},
],
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}
}
},
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]
},
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'MaxResource': 123
}
},
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},
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'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
},
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{
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},
],
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{
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},
],
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'Values': [
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]
},
],
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},
]
},
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'BestObjectiveNotImproving': {
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},
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}
},
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},
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},
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{
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},
],
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},
],
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'Values': [
'string',
]
},
],
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},
]
},
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},
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{
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'Regex': 'string'
},
]
},
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{
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},
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},
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}
},
],
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]
},
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'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
],
'KeepAlivePeriodInSeconds': 123
},
'StoppingCondition': {
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'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
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},
'HyperParameterTuningResourceConfig': {
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'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
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'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'Environment': {
'string': 'string'
}
},
'TrainingJobDefinitions': [
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
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]
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},
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'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'MonitoringAlertSummaries': [
{
'MonitoringAlertName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'AlertStatus': 'InAlert'|'OK',
'DatapointsToAlert': 123,
'EvaluationPeriod': 123,
'Actions': {
'ModelDashboardIndicator': {
'Enabled': True|False
}
}
},
],
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
}
},
],
'ModelCard': {
'ModelCardArn': 'string',
'ModelCardName': 'string',
'ModelCardVersion': 123,
'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived',
'SecurityConfig': {
'KmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'ModelId': 'string',
'RiskRating': 'string'
}
},
'ModelCard': {
'ModelCardArn': 'string',
'ModelCardName': 'string',
'ModelCardVersion': 123,
'Content': 'string',
'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived',
'SecurityConfig': {
'KmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'ModelId': 'string',
'RiskRating': 'string',
'ModelPackageGroupName': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Results (list) --
A list of SearchRecord objects.
(dict) --
A single resource returned as part of the Search API response.
TrainingJob (dict) --
The properties of a training job.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) --
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
Your input must be in the same Amazon Web Services region as your training job.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of SecondaryStatusTransitions for DescribeTrainingJob . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilerConfig (dict) --
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
Environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Experiment (dict) --
The properties of an experiment.
ExperimentName (string) --
The name of the experiment.
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
Source (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
Description (string) --
The description of the experiment.
CreationTime (datetime) --
When the experiment was created.
CreatedBy (dict) --
Who created the experiment.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
When the experiment was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Tags (list) --
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Trial (dict) --
The properties of a trial.
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Who created the trial.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
Who last modified the trial.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Tags (list) --
The list of tags that are associated with the trial. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TrialComponentSummaries (list) --
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
(dict) --
A short summary of a trial component.
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source Amazon Resource Name (ARN).
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
TrialComponent (dict) --
The properties of a trial component.
TrialComponentName (string) --
The name of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Source (dict) --
The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn (string) --
The source Amazon Resource Name (ARN).
SourceType (string) --
The source job type.
Status (dict) --
The status of the trial component.
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Who created the trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
SourceDetail (dict) --
Details of the source of the component.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TrainingJob (dict) --
Information about a training job that's the source of a trial component.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) --
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
Your input must be in the same Amazon Web Services region as your training job.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of SecondaryStatusTransitions for DescribeTrainingJob . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilerConfig (dict) --
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
Environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ProcessingJob (dict) --
Information about a processing job that's the source of a trial component.
ProcessingInputs (list) --
List of input configurations for the processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) --
The name for the processing job input.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri (string) --
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath (string) --
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
S3DataDistributionType (string) --
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
S3CompressionType (string) --
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) --
The name of the data catalog used in Athena query execution.
Database (string) --
The name of the database used in the Athena query execution.
QueryString (string) --
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) --
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) --
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) --
The Redshift cluster Identifier.
Database (string) --
The name of the Redshift database used in Redshift query execution.
DbUser (string) --
The database user name used in Redshift query execution.
QueryString (string) --
The SQL query statements to be executed.
ClusterRoleArn (string) --
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) --
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) --
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
ProcessingOutputConfig (dict) --
Configuration for uploading output from the processing container.
Outputs (list) --
An array of outputs configuring the data to upload from the processing container.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) --
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
StoppingCondition (dict) --
Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configuration to run a processing job in a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a processing job.
(string) --
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The ARN of the role used to create the processing job.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
ProcessingJobArn (string) --
The ARN of the processing job.
ProcessingJobStatus (string) --
The status of the processing job.
ExitMessage (string) --
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time that the processing job ended.
ProcessingStartTime (datetime) --
The time that the processing job started.
LastModifiedTime (datetime) --
The time the processing job was last modified.
CreationTime (datetime) --
The time the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of the training job associated with this processing job.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TransformJob (dict) --
Information about a transform job that's the source of a trial component.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.
Completed - The job has completed.
Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.
Stopping - The transform job is stopping.
Stopped - The transform job has stopped.
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
LineageGroupArn (string) --
The Amazon Resource Name (ARN) of the lineage group resource.
Tags (list) --
The list of tags that are associated with the component. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Parents (list) --
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
(dict) --
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
TrialName (string) --
The name of the trial.
ExperimentName (string) --
The name of the experiment.
RunName (string) --
The name of the experiment run.
Endpoint (dict) --
A hosted endpoint for real-time inference.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName (string) --
The endpoint configuration associated with the endpoint.
ProductionVariants (list) --
A list of the production variants hosted on the endpoint. Each production variant is a model.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for the endpoint update.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DataCaptureConfig (dict) --
The currently active data capture configuration used by your Endpoint.
EnableCapture (boolean) --
Whether data capture is enabled or disabled.
CaptureStatus (string) --
Whether data capture is currently functional.
CurrentSamplingPercentage (integer) --
The percentage of requests being captured by your Endpoint.
DestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
KmsKeyId (string) --
The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus (string) --
The status of the endpoint.
FailureReason (string) --
If the endpoint failed, the reason it failed.
CreationTime (datetime) --
The time that the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
MonitoringSchedules (list) --
A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor .
(dict) --
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor .
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule. This can be one of the following values.
PENDING - The schedule is pending being created.
FAILED - The schedule failed.
SCHEDULED - The schedule was successfully created.
STOPPED - The schedule was stopped.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
FailureReason (string) --
If the monitoring schedule failed, the reason it failed.
CreationTime (datetime) --
The time that the monitoring schedule was created.
LastModifiedTime (datetime) --
The last time the monitoring schedule was changed.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
If you want to set the job to start every hour, please use the following: Hourly: cron(0 * ? * * *)
If you want to start the job daily: cron(0 [00-23] ? * * *)
For example, the following are valid cron expressions:
Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)
Note
Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName (string) --
The name of the job that performs baselining for the monitoring job.
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
BatchTransformInput (dict) --
Input object for the batch transform job.
DataCapturedDestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
DatasetFormat (dict) --
The dataset format for your batch transform job.
Csv (dict) --
The CSV dataset used in the monitoring job.
Header (boolean) --
Indicates if the CSV data has a header.
Json (dict) --
The JSON dataset used in the monitoring job
Line (boolean) --
Indicates if the file should be read as a json object per line.
Parquet (dict) --
The Parquet dataset used in the monitoring job
LocalPath (string) --
Path to the filesystem where the batch transform data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
(string) --
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
(string) --
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition to schedule.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
EndpointName (string) --
The endpoint that hosts the model being monitored.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
Tags (list) --
A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Tags (list) --
A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ShadowProductionVariants (list) --
A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for the endpoint update.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ModelPackage (dict) --
A versioned model that can be deployed for SageMaker inference.
ModelPackageName (string) --
The name of the model.
ModelPackageGroupName (string) --
The model group to which the model belongs.
ModelPackageVersion (integer) --
The version number of a versioned model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
The description of the model package.
CreationTime (datetime) --
The time that the model package was created.
InferenceSpecification (dict) --
Defines how to perform inference generation after a training job is run.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SourceAlgorithmSpecification (dict) --
A list of algorithms that were used to create a model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Specifies batch transform jobs that SageMaker runs to validate your model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
ModelPackageStatus (string) --
The status of the model package. This can be one of the following values.
PENDING - The model package is pending being created.
IN_PROGRESS - The model package is in the process of being created.
COMPLETED - The model package was successfully created.
FAILED - The model package failed.
DELETING - The model package is in the process of being deleted.
ModelPackageStatusDetails (dict) --
Specifies the validation and image scan statuses of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace .
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
APPROVED - The model is approved
REJECTED - The model is rejected.
PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PreTrainingReport (dict) --
The pre-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingReport (dict) --
The post-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
LastModifiedTime (datetime) --
The last time the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ApprovalDescription (string) --
A description provided when the model approval is set.
Domain (string) --
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
Task (string) --
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
AdditionalInferenceSpecifications (list) --
An array of additional Inference Specification objects.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) --
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
Tags (list) --
A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
CustomerMetadataProperties (dict) --
The metadata properties for the model package.
(string) --
(string) --
DriftCheckBaselines (dict) --
Represents the drift check baselines that can be used when the model monitor is set using the model package.
Bias (dict) --
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile (dict) --
The bias config file for a model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
PreTrainingConstraints (dict) --
The pre-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingConstraints (dict) --
The post-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints (dict) --
The drift check explainability constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ConfigFile (dict) --
The explainability config file for the model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
ModelQuality (dict) --
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model data quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model data quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelPackageGroup (dict) --
A group of versioned models in the model registry.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
The description for the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ModelPackageGroupStatus (string) --
The status of the model group. This can be one of the following values.
PENDING - The model group is pending being created.
IN_PROGRESS - The model group is in the process of being created.
COMPLETED - The model group was successfully created.
FAILED - The model group failed.
DELETING - The model group is in the process of being deleted.
DELETE_FAILED - SageMaker failed to delete the model group.
Tags (list) --
A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Pipeline (dict) --
A SageMaker Model Building Pipeline instance.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that created the pipeline.
PipelineStatus (string) --
The status of the pipeline.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ParallelismConfiguration (dict) --
The parallelism configuration applied to the pipeline.
MaxParallelExecutionSteps (integer) --
The max number of steps that can be executed in parallel.
Tags (list) --
A list of tags that apply to the pipeline.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
PipelineExecution (dict) --
An execution of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline status.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExperimentConfig (dict) --
Specifies the names of the experiment and trial created by a pipeline.
ExperimentName (string) --
The name of the experiment.
TrialName (string) --
The name of the trial.
FailureReason (string) --
If the execution failed, a message describing why.
CreationTime (datetime) --
The creation time of the pipeline execution.
LastModifiedTime (datetime) --
The time that the pipeline execution was last modified.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ParallelismConfiguration (dict) --
The parallelism configuration applied to the pipeline execution.
MaxParallelExecutionSteps (integer) --
The max number of steps that can be executed in parallel.
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
SelectiveExecutionConfig (dict) --
The selective execution configuration applied to the pipeline run.
SourcePipelineExecutionArn (string) --
The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either Failed or Success .
SelectedSteps (list) --
A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
(dict) --
A step selected to run in selective execution mode.
StepName (string) --
The name of the pipeline step.
FeatureGroup (dict) --
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of a FeatureGroup .
FeatureGroupName (string) --
The name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions .
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
FeatureDefinitions (list) --
A list of Feature s. Each Feature must include a FeatureName and a FeatureType .
Valid FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time .
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CreationTime (datetime) --
The time a FeatureGroup was created.
LastModifiedTime (datetime) --
A timestamp indicating the last time you updated the feature group.
OnlineStoreConfig (dict) --
Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly.
The default value is False .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
TtlDuration (dict) --
Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration . For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
Unit (string) --
TtlDuration time unit.
Value (integer) --
TtlDuration time value.
OfflineStoreConfig (dict) --
The configuration of an OfflineStore .
Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore .
To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig .
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore . If set to False , Feature Store will name the OfflineStore Glue table following Athena's naming recommendations .
The default value is False .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
TableFormat (string) --
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
FeatureGroupStatus (string) --
A FeatureGroup status.
OfflineStoreStatus (dict) --
The status of OfflineStore .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
LastUpdateStatus (dict) --
A value that indicates whether the feature group was updated successfully.
Status (string) --
A value that indicates whether the update was made successful.
FailureReason (string) --
If the update wasn't successful, indicates the reason why it failed.
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore .
Description (string) --
A free form description of a FeatureGroup .
Tags (list) --
Tags used to define a FeatureGroup .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Project (dict) --
The properties of a project.
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectName (string) --
The name of the project.
ProjectId (string) --
The ID of the project.
ProjectDescription (string) --
The description of the project.
ServiceCatalogProvisioningDetails (dict) --
Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog .
ProductId (string) --
The ID of the product to provision.
ProvisioningArtifactId (string) --
The ID of the provisioning artifact.
PathId (string) --
The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters (list) --
A list of key value pairs that you specify when you provision a product.
(dict) --
A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog .
Key (string) --
The key that identifies a provisioning parameter.
Value (string) --
The value of the provisioning parameter.
ServiceCatalogProvisionedProductDetails (dict) --
Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog .
ProvisionedProductId (string) --
The ID of the provisioned product.
ProvisionedProductStatusMessage (string) --
The current status of the product.
AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.
UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.
TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.
ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.
PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.
ProjectStatus (string) --
The status of the project.
CreatedBy (dict) --
Who created the project.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
CreationTime (datetime) --
A timestamp specifying when the project was created.
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
LastModifiedTime (datetime) --
A timestamp container for when the project was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
FeatureMetadata (dict) --
The feature metadata used to search through the features.
FeatureGroupArn (string) --
The Amazon Resource Number (ARN) of the feature group.
FeatureGroupName (string) --
The name of the feature group containing the feature.
FeatureName (string) --
The name of feature.
FeatureType (string) --
The data type of the feature.
CreationTime (datetime) --
A timestamp indicating when the feature was created.
LastModifiedTime (datetime) --
A timestamp indicating when the feature was last modified.
Description (string) --
An optional description that you specify to better describe the feature.
Parameters (list) --
Optional key-value pairs that you specify to better describe the feature.
(dict) --
A key-value pair that you specify to describe the feature.
Key (string) --
A key that must contain a value to describe the feature.
Value (string) --
The value that belongs to a key.
HyperParameterTuningJob (dict) --
The properties of a hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of a hyperparameter tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of a hyperparameter tuning job.
HyperParameterTuningJobConfig (dict) --
Configures a hyperparameter tuning job.
Strategy (string) --
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .
StrategyConfig (dict) --
The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .
HyperbandStrategyConfig (dict) --
The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
MinResource (integer) --
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .
MaxResource (integer) --
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
HyperParameterTuningJobObjective (dict) --
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges (dict) --
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType (string) --
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
TuningJobCompletionCriteria (dict) --
The tuning job's completion criteria.
TargetObjectiveMetricValue (float) --
The value of the objective metric.
BestObjectiveNotImproving (dict) --
A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving (integer) --
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected (dict) --
A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence (string) --
A flag to stop a tuning job once AMT has detected that the job has converged.
RandomSeed (integer) --
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition (dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
TrainingJobDefinitions (list) --
The job definitions included in a hyperparameter tuning job.
(dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
HyperParameterTuningJobStatus (string) --
The status of a hyperparameter tuning job.
CreationTime (datetime) --
The time that a hyperparameter tuning job was created.
HyperParameterTuningEndTime (datetime) --
The time that a hyperparameter tuning job ended.
LastModifiedTime (datetime) --
The time that a hyperparameter tuning job was last modified.
TrainingJobStatusCounters (dict) --
The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
BestTrainingJob (dict) --
The container for the summary information about a training job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
OverallBestTrainingJob (dict) --
The container for the summary information about a training job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
WarmStartConfig (dict) --
Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
Note
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
ParentHyperParameterTuningJobs (list) --
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
(dict) --
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType (string) --
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
FailureReason (string) --
The error that was created when a hyperparameter tuning job failed.
Tags (list) --
The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TuningJobCompletionDetails (dict) --
Information about either a current or completed hyperparameter tuning job.
NumberOfTrainingJobsObjectiveNotImproving (integer) --
The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
ConvergenceDetectedTime (datetime) --
The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConsumedResources (dict) --
The total amount of resources consumed by a hyperparameter tuning job.
RuntimeInSeconds (integer) --
The wall clock runtime in seconds used by your hyperparameter tuning job.
Model (dict) --
A model displayed in the Amazon SageMaker Model Dashboard.
Model (dict) --
A model displayed in the Model Dashboard.
ModelName (string) --
The name of the model.
PrimaryContainer (dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot (. )
A double dot (.. )
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
Containers (list) --
The containers in the inference pipeline.
(dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot (. )
A double dot (.. )
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
InferenceExecutionConfig (dict) --
Specifies details about how containers in a multi-container endpoint are run.
Mode (string) --
How containers in a multi-container are run. The following values are valid.
SERIAL - Containers run as a serial pipeline.
DIRECT - Only the individual container that you specify is run.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
CreationTime (datetime) --
A timestamp that indicates when the model was created.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation (boolean) --
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
Tags (list) --
A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
DeploymentRecommendation (dict) --
A set of recommended deployment configurations for the model.
RecommendationStatus (string) --
Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS , retry your API call after a few seconds to get a COMPLETED deployment recommendation.
RealTimeInferenceRecommendations (list) --
A list of RealTimeInferenceRecommendation items.
(dict) --
The recommended configuration to use for Real-Time Inference.
RecommendationId (string) --
The recommendation ID which uniquely identifies each recommendation.
InstanceType (string) --
The recommended instance type for Real-Time Inference.
Environment (dict) --
The recommended environment variables to set in the model container for Real-Time Inference.
(string) --
(string) --
Endpoints (list) --
The endpoints that host a model.
(dict) --
An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.
EndpointName (string) --
The endpoint name.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
CreationTime (datetime) --
A timestamp that indicates when the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
EndpointStatus (string) --
The endpoint status.
LastBatchTransformJob (dict) --
A batch transform job. For information about SageMaker batch transform, see Use Batch Transform .
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.
Completed - The job has completed.
Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.
Stopping - The transform job is stopping.
Stopped - The transform job has stopped.
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
MonitoringSchedules (list) --
The monitoring schedules for a model.
(dict) --
A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of a monitoring schedule.
MonitoringScheduleName (string) --
The name of a monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule.
MonitoringType (string) --
The monitor type of a model monitor.
FailureReason (string) --
If a monitoring job failed, provides the reason.
CreationTime (datetime) --
A timestamp that indicates when the monitoring schedule was created.
LastModifiedTime (datetime) --
A timestamp that indicates when the monitoring schedule was last updated.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
If you want to set the job to start every hour, please use the following: Hourly: cron(0 * ? * * *)
If you want to start the job daily: cron(0 [00-23] ? * * *)
For example, the following are valid cron expressions:
Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)
Note
Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName (string) --
The name of the job that performs baselining for the monitoring job.
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
BatchTransformInput (dict) --
Input object for the batch transform job.
DataCapturedDestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
DatasetFormat (dict) --
The dataset format for your batch transform job.
Csv (dict) --
The CSV dataset used in the monitoring job.
Header (boolean) --
Indicates if the CSV data has a header.
Json (dict) --
The JSON dataset used in the monitoring job
Line (boolean) --
Indicates if the file should be read as a json object per line.
Parquet (dict) --
The Parquet dataset used in the monitoring job
LocalPath (string) --
Path to the filesystem where the batch transform data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
(string) --
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
(string) --
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition to schedule.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
EndpointName (string) --
The endpoint which is monitored.
MonitoringAlertSummaries (list) --
A JSON array where each element is a summary for a monitoring alert.
(dict) --
Provides summary information about a monitor alert.
MonitoringAlertName (string) --
The name of a monitoring alert.
CreationTime (datetime) --
A timestamp that indicates when a monitor alert was created.
LastModifiedTime (datetime) --
A timestamp that indicates when a monitor alert was last updated.
AlertStatus (string) --
The current status of an alert.
DatapointsToAlert (integer) --
Within EvaluationPeriod , how many execution failures will raise an alert.
EvaluationPeriod (integer) --
The number of most recent monitoring executions to consider when evaluating alert status.
Actions (dict) --
A list of alert actions taken in response to an alert going into InAlert status.
ModelDashboardIndicator (dict) --
An alert action taken to light up an icon on the Model Dashboard when an alert goes into InAlert status.
Enabled (boolean) --
Indicates whether the alert action is turned on.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
ModelCard (dict) --
The model card for a model.
ModelCardArn (string) --
The Amazon Resource Name (ARN) for a model card.
ModelCardName (string) --
The name of a model card.
ModelCardVersion (integer) --
The model card version.
ModelCardStatus (string) --
The model card status.
SecurityConfig (dict) --
The KMS Key ID (KMSKeyId ) for encryption of model card information.
KmsKeyId (string) --
A Key Management Service key ID to use for encrypting a model card.
CreationTime (datetime) --
A timestamp that indicates when the model card was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
A timestamp that indicates when the model card was last updated.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Tags (list) --
The tags associated with a model card.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelId (string) --
For models created in SageMaker, this is the model ARN. For models created outside of SageMaker, this is a user-customized string.
RiskRating (string) --
A model card's risk rating. Can be low, medium, or high.
ModelCard (dict) --
An Amazon SageMaker Model Card that documents details about a machine learning model.
ModelCardArn (string) --
The Amazon Resource Name (ARN) of the model card.
ModelCardName (string) --
The unique name of the model card.
ModelCardVersion (integer) --
The version of the model card.
Content (string) --
The content of the model card. Content uses the model card JSON schema and provided as a string.
ModelCardStatus (string) --
The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft : The model card is a work in progress.
PendingReview : The model card is pending review.
Approved : The model card is approved.
Archived : The model card is archived. No more updates should be made to the model card, but it can still be exported.
SecurityConfig (dict) --
The security configuration used to protect model card data.
KmsKeyId (string) --
A Key Management Service key ID to use for encrypting a model card.
CreationTime (datetime) --
The date and time that the model card was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
The date and time that the model card was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Tags (list) --
Key-value pairs used to manage metadata for the model card.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelId (string) --
The unique name (ID) of the model.
RiskRating (string) --
The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings .
ModelPackageGroupName (string) --
The model package group that contains the model package. Only relevant for model cards created for model packages in the Amazon SageMaker Model Registry.
NextToken (string) --
If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.