Amazon SageMaker Service

2023/11/29 - Amazon SageMaker Service - 13 new 20 updated api methods

Changes  This release adds following support 1/ Improved SDK tooling for model deployment. 2/ New Inference Component based features to lower inference costs and latency 3/ SageMaker HyperPod management. 4/ Additional parameters for FM Fine Tuning in Autopilot

DeleteCluster (new) Link ¶

Delete a SageMaker HyperPod cluster.

See also: AWS API Documentation

Request Syntax

client.delete_cluster(
    ClusterName='string'
)
type ClusterName

string

param ClusterName

[REQUIRED]

The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.

rtype

dict

returns

Response Syntax

{
    'ClusterArn': 'string'
}

Response Structure

  • (dict) --

    • ClusterArn (string) --

      The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.

DescribeInferenceComponent (new) Link ¶

Returns information about an inference component.

See also: AWS API Documentation

Request Syntax

client.describe_inference_component(
    InferenceComponentName='string'
)
type InferenceComponentName

string

param InferenceComponentName

[REQUIRED]

The name of the inference component.

rtype

dict

returns

Response Syntax

{
    'InferenceComponentName': 'string',
    'InferenceComponentArn': 'string',
    'EndpointName': 'string',
    'EndpointArn': 'string',
    'VariantName': 'string',
    'FailureReason': 'string',
    'Specification': {
        'ModelName': 'string',
        'Container': {
            'DeployedImage': {
                'SpecifiedImage': 'string',
                'ResolvedImage': 'string',
                'ResolutionTime': datetime(2015, 1, 1)
            },
            'ArtifactUrl': 'string',
            'Environment': {
                'string': 'string'
            }
        },
        'StartupParameters': {
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123
        },
        'ComputeResourceRequirements': {
            'NumberOfCpuCoresRequired': ...,
            'NumberOfAcceleratorDevicesRequired': ...,
            'MinMemoryRequiredInMb': 123,
            'MaxMemoryRequiredInMb': 123
        }
    },
    'RuntimeConfig': {
        'DesiredCopyCount': 123,
        'CurrentCopyCount': 123
    },
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'InferenceComponentStatus': 'InService'|'Creating'|'Updating'|'Failed'|'Deleting'
}

Response Structure

  • (dict) --

    • InferenceComponentName (string) --

      The name of the inference component.

    • InferenceComponentArn (string) --

      The Amazon Resource Name (ARN) of the inference component.

    • EndpointName (string) --

      The name of the endpoint that hosts the inference component.

    • EndpointArn (string) --

      The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.

    • VariantName (string) --

      The name of the production variant that hosts the inference component.

    • FailureReason (string) --

      If the inference component status is Failed , the reason for the failure.

    • Specification (dict) --

      Details about the resources that are deployed with this inference component.

      • ModelName (string) --

        The name of the SageMaker model object that is deployed with the inference component.

      • Container (dict) --

        Details about the container that provides the runtime environment for the model that is deployed with the inference component.

        • DeployedImage (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

        • ArtifactUrl (string) --

          The Amazon S3 path where the model artifacts are stored.

        • Environment (dict) --

          The environment variables to set in the Docker container.

          • (string) --

            • (string) --

      • StartupParameters (dict) --

        Settings that take effect while the model container starts up.

        • ModelDataDownloadTimeoutInSeconds (integer) --

          The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

        • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

          The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

      • ComputeResourceRequirements (dict) --

        The compute resources allocated to run the model assigned to the inference component.

        • NumberOfCpuCoresRequired (float) --

          The number of CPU cores to allocate to run a model that you assign to an inference component.

        • NumberOfAcceleratorDevicesRequired (float) --

          The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.

        • MinMemoryRequiredInMb (integer) --

          The minimum MB of memory to allocate to run a model that you assign to an inference component.

        • MaxMemoryRequiredInMb (integer) --

          The maximum MB of memory to allocate to run a model that you assign to an inference component.

    • RuntimeConfig (dict) --

      Details about the runtime settings for the model that is deployed with the inference component.

      • DesiredCopyCount (integer) --

        The number of runtime copies of the model container that you requested to deploy with the inference component.

      • CurrentCopyCount (integer) --

        The number of runtime copies of the model container that are currently deployed.

    • CreationTime (datetime) --

      The time when the inference component was created.

    • LastModifiedTime (datetime) --

      The time when the inference component was last updated.

    • InferenceComponentStatus (string) --

      The status of the inference component.

DescribeClusterNode (new) Link ¶

Retrieves information of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.

See also: AWS API Documentation

Request Syntax

client.describe_cluster_node(
    ClusterName='string',
    NodeId='string'
)
type ClusterName

string

param ClusterName

[REQUIRED]

The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the instance is.

type NodeId

string

param NodeId

[REQUIRED]

The ID of the instance.

rtype

dict

returns

Response Syntax

{
    'NodeDetails': {
        'InstanceGroupName': 'string',
        'InstanceId': 'string',
        'InstanceStatus': {
            'Status': 'Running'|'Failure'|'Pending'|'ShuttingDown'|'SystemUpdating',
            'Message': 'string'
        },
        'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.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.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge',
        'LaunchTime': datetime(2015, 1, 1),
        'LifeCycleConfig': {
            'SourceS3Uri': 'string',
            'OnCreate': 'string'
        },
        'ThreadsPerCore': 123
    }
}

Response Structure

  • (dict) --

    • NodeDetails (dict) --

      The details of the instance.

      • InstanceGroupName (string) --

        The instance group name in which the instance is.

      • InstanceId (string) --

        The ID of the instance.

      • InstanceStatus (dict) --

        The status of the instance.

        • Status (string) --

          The status of an instance in a SageMaker HyperPod cluster.

        • Message (string) --

          The message from an instance in a SageMaker HyperPod cluster.

      • InstanceType (string) --

        The type of the instance.

      • LaunchTime (datetime) --

        The time when the instance is launched.

      • LifeCycleConfig (dict) --

        The LifeCycle configuration applied to the instance.

        • SourceS3Uri (string) --

          An Amazon S3 bucket path where your LifeCycle scripts are stored.

        • OnCreate (string) --

          The directory of the LifeCycle script under SourceS3Uri . This LifeCycle script runs during cluster creation.

      • ThreadsPerCore (integer) --

        The number of threads per CPU core you specified under CreateCluster .

ListClusterNodes (new) Link ¶

Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.

See also: AWS API Documentation

Request Syntax

client.list_cluster_nodes(
    ClusterName='string',
    CreationTimeAfter=datetime(2015, 1, 1),
    CreationTimeBefore=datetime(2015, 1, 1),
    InstanceGroupNameContains='string',
    MaxResults=123,
    NextToken='string',
    SortBy='CREATION_TIME'|'NAME',
    SortOrder='Ascending'|'Descending'
)
type ClusterName

string

param ClusterName

[REQUIRED]

The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes.

type CreationTimeAfter

datetime

param CreationTimeAfter

A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard.

Acceptable formats include:

  • YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z

  • YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00

  • YYYY-MM-DD , for example, 2014-10-01

  • Unix time in seconds, for example, 1412195400 . This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.

For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide .

type CreationTimeBefore

datetime

param CreationTimeBefore

A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter . For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide .

type InstanceGroupNameContains

string

param InstanceGroupNameContains

A filter that returns the instance groups whose name contain a specified string.

type MaxResults

integer

param MaxResults

The maximum number of nodes to return in the response.

type NextToken

string

param NextToken

If the result of the previous ListClusterNodes request was truncated, the response includes a NextToken . To retrieve the next set of cluster nodes, use the token in the next request.

type SortBy

string

param SortBy

The field by which to sort results. The default value is CREATION_TIME .

type SortOrder

string

param SortOrder

The sort order for results. The default value is Ascending .

rtype

dict

returns

Response Syntax

{
    'NextToken': 'string',
    'ClusterNodeSummaries': [
        {
            'InstanceGroupName': 'string',
            'InstanceId': 'string',
            'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.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.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge',
            'LaunchTime': datetime(2015, 1, 1),
            'InstanceStatus': {
                'Status': 'Running'|'Failure'|'Pending'|'ShuttingDown'|'SystemUpdating',
                'Message': 'string'
            }
        },
    ]
}

Response Structure

  • (dict) --

    • NextToken (string) --

      The next token specified for listing instances in a SageMaker HyperPod cluster.

    • ClusterNodeSummaries (list) --

      The summaries of listed instances in a SageMaker HyperPod cluster

      • (dict) --

        Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.

        • InstanceGroupName (string) --

          The name of the instance group in which the instance is.

        • InstanceId (string) --

          The ID of the instance.

        • InstanceType (string) --

          The type of the instance.

        • LaunchTime (datetime) --

          The time when the instance is launched.

        • InstanceStatus (dict) --

          The status of the instance.

          • Status (string) --

            The status of an instance in a SageMaker HyperPod cluster.

          • Message (string) --

            The message from an instance in a SageMaker HyperPod cluster.

DescribeCluster (new) Link ¶

Retrieves information of a SageMaker HyperPod cluster.

See also: AWS API Documentation

Request Syntax

client.describe_cluster(
    ClusterName='string'
)
type ClusterName

string

param ClusterName

[REQUIRED]

The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

rtype

dict

returns

Response Syntax

{
    'ClusterArn': 'string',
    'ClusterName': 'string',
    'ClusterStatus': 'Creating'|'Deleting'|'Failed'|'InService'|'RollingBack'|'SystemUpdating'|'Updating',
    'CreationTime': datetime(2015, 1, 1),
    'FailureMessage': 'string',
    'InstanceGroups': [
        {
            'CurrentCount': 123,
            'TargetCount': 123,
            'InstanceGroupName': 'string',
            'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.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.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge',
            'LifeCycleConfig': {
                'SourceS3Uri': 'string',
                'OnCreate': 'string'
            },
            'ExecutionRole': 'string',
            'ThreadsPerCore': 123
        },
    ],
    'VpcConfig': {
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    }
}

Response Structure

  • (dict) --

    • ClusterArn (string) --

      The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

    • ClusterName (string) --

      The name of the SageMaker HyperPod cluster.

    • ClusterStatus (string) --

      The status of the SageMaker HyperPod cluster.

    • CreationTime (datetime) --

      The time when the SageMaker Cluster is created.

    • FailureMessage (string) --

      The failure message of the SageMaker HyperPod cluster.

    • InstanceGroups (list) --

      The instance groups of the SageMaker HyperPod cluster.

      • (dict) --

        Details of an instance group in a SageMaker HyperPod cluster.

        • CurrentCount (integer) --

          The number of instances that are currently in the instance group of a SageMaker HyperPod cluster.

        • TargetCount (integer) --

          The number of instances you specified to add to the instance group of a SageMaker HyperPod cluster.

        • InstanceGroupName (string) --

          The name of the instance group of a SageMaker HyperPod cluster.

        • InstanceType (string) --

          The instance type of the instance group of a SageMaker HyperPod cluster.

        • LifeCycleConfig (dict) --

          Details of LifeCycle configuration for the instance group.

          • SourceS3Uri (string) --

            An Amazon S3 bucket path where your LifeCycle scripts are stored.

          • OnCreate (string) --

            The directory of the LifeCycle script under SourceS3Uri . This LifeCycle script runs during cluster creation.

        • ExecutionRole (string) --

          The execution role for the instance group to assume.

        • ThreadsPerCore (integer) --

          The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide .

    • VpcConfig (dict) --

      Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

      • 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) --

UpdateCluster (new) Link ¶

Update a SageMaker HyperPod cluster.

See also: AWS API Documentation

Request Syntax

client.update_cluster(
    ClusterName='string',
    InstanceGroups=[
        {
            'InstanceCount': 123,
            'InstanceGroupName': 'string',
            'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.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.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge',
            'LifeCycleConfig': {
                'SourceS3Uri': 'string',
                'OnCreate': 'string'
            },
            'ExecutionRole': 'string',
            'ThreadsPerCore': 123
        },
    ]
)
type ClusterName

string

param ClusterName

[REQUIRED]

Specify the name of the SageMaker HyperPod cluster you want to update.

type InstanceGroups

list

param InstanceGroups

[REQUIRED]

Specify the instance groups to update.

  • (dict) --

    The specifications of an instance group that you need to define.

    • InstanceCount (integer) -- [REQUIRED]

      Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.

    • InstanceGroupName (string) -- [REQUIRED]

      Specifies the name of the instance group.

    • InstanceType (string) -- [REQUIRED]

      Specifies the instance type of the instance group.

    • LifeCycleConfig (dict) -- [REQUIRED]

      Specifies the LifeCycle configuration for the instance group.

      • SourceS3Uri (string) -- [REQUIRED]

        An Amazon S3 bucket path where your LifeCycle scripts are stored.

      • OnCreate (string) -- [REQUIRED]

        The directory of the LifeCycle script under SourceS3Uri . This LifeCycle script runs during cluster creation.

    • ExecutionRole (string) -- [REQUIRED]

      Specifies an IAM execution role to be assumed by the instance group.

    • ThreadsPerCore (integer) --

      Specifies the value for Threads per core . For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn't support multithreading, specify 1 . For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide .

rtype

dict

returns

Response Syntax

{
    'ClusterArn': 'string'
}

Response Structure

  • (dict) --

    • ClusterArn (string) --

      The Amazon Resource Name (ARN) of the updated SageMaker HyperPod cluster.

UpdateInferenceComponentRuntimeConfig (new) Link ¶

Runtime settings for a model that is deployed with an inference component.

See also: AWS API Documentation

Request Syntax

client.update_inference_component_runtime_config(
    InferenceComponentName='string',
    DesiredRuntimeConfig={
        'CopyCount': 123
    }
)
type InferenceComponentName

string

param InferenceComponentName

[REQUIRED]

The name of the inference component to update.

type DesiredRuntimeConfig

dict

param DesiredRuntimeConfig

[REQUIRED]

Runtime settings for a model that is deployed with an inference component.

  • CopyCount (integer) -- [REQUIRED]

    The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.

rtype

dict

returns

Response Syntax

{
    'InferenceComponentArn': 'string'
}

Response Structure

  • (dict) --

    • InferenceComponentArn (string) --

      The Amazon Resource Name (ARN) of the inference component.

ListClusters (new) Link ¶

Retrieves the list of SageMaker HyperPod clusters.

See also: AWS API Documentation

Request Syntax

client.list_clusters(
    CreationTimeAfter=datetime(2015, 1, 1),
    CreationTimeBefore=datetime(2015, 1, 1),
    MaxResults=123,
    NameContains='string',
    NextToken='string',
    SortBy='CREATION_TIME'|'NAME',
    SortOrder='Ascending'|'Descending'
)
type CreationTimeAfter

datetime

param CreationTimeAfter

Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard.

Acceptable formats include:

  • YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z

  • YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00

  • YYYY-MM-DD , for example, 2014-10-01

  • Unix time in seconds, for example, 1412195400 . This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.

For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide .

type CreationTimeBefore

datetime

param CreationTimeBefore

Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter . For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide .

type MaxResults

integer

param MaxResults

Set the maximum number of SageMaker HyperPod clusters to list.

type NameContains

string

param NameContains

Set the maximum number of instances to print in the list.

type NextToken

string

param NextToken

Set the next token to retrieve the list of SageMaker HyperPod clusters.

type SortBy

string

param SortBy

The field by which to sort results. The default value is CREATION_TIME .

type SortOrder

string

param SortOrder

The sort order for results. The default value is Ascending .

rtype

dict

returns

Response Syntax

{
    'NextToken': 'string',
    'ClusterSummaries': [
        {
            'ClusterArn': 'string',
            'ClusterName': 'string',
            'CreationTime': datetime(2015, 1, 1),
            'ClusterStatus': 'Creating'|'Deleting'|'Failed'|'InService'|'RollingBack'|'SystemUpdating'|'Updating'
        },
    ]
}

Response Structure

  • (dict) --

    • NextToken (string) --

      If the result of the previous ListClusters request was truncated, the response includes a NextToken . To retrieve the next set of clusters, use the token in the next request.

    • ClusterSummaries (list) --

      The summaries of listed SageMaker HyperPod clusters.

      • (dict) --

        Lists a summary of the properties of a SageMaker HyperPod cluster.

        • ClusterArn (string) --

          The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

        • ClusterName (string) --

          The name of the SageMaker HyperPod cluster.

        • CreationTime (datetime) --

          The time when the SageMaker HyperPod cluster is created.

        • ClusterStatus (string) --

          The status of the SageMaker HyperPod cluster.

CreateInferenceComponent (new) Link ¶

Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.

See also: AWS API Documentation

Request Syntax

client.create_inference_component(
    InferenceComponentName='string',
    EndpointName='string',
    VariantName='string',
    Specification={
        'ModelName': 'string',
        'Container': {
            'Image': 'string',
            'ArtifactUrl': 'string',
            'Environment': {
                'string': 'string'
            }
        },
        'StartupParameters': {
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123
        },
        'ComputeResourceRequirements': {
            'NumberOfCpuCoresRequired': ...,
            'NumberOfAcceleratorDevicesRequired': ...,
            'MinMemoryRequiredInMb': 123,
            'MaxMemoryRequiredInMb': 123
        }
    },
    RuntimeConfig={
        'CopyCount': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type InferenceComponentName

string

param InferenceComponentName

[REQUIRED]

A unique name to assign to the inference component.

type EndpointName

string

param EndpointName

[REQUIRED]

The name of an existing endpoint where you host the inference component.

type VariantName

string

param VariantName

[REQUIRED]

The name of an existing production variant where you host the inference component.

type Specification

dict

param Specification

[REQUIRED]

Details about the resources to deploy with this inference component, including the model, container, and compute resources.

  • ModelName (string) --

    The name of an existing SageMaker model object in your account that you want to deploy with the inference component.

  • Container (dict) --

    Defines a container that provides the runtime environment for a model that you deploy with an inference component.

    • Image (string) --

      The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

    • ArtifactUrl (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).

    • 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) --

  • StartupParameters (dict) --

    Settings that take effect while the model container starts up.

    • ModelDataDownloadTimeoutInSeconds (integer) --

      The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

    • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

      The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

  • ComputeResourceRequirements (dict) -- [REQUIRED]

    The compute resources allocated to run the model assigned to the inference component.

    • NumberOfCpuCoresRequired (float) --

      The number of CPU cores to allocate to run a model that you assign to an inference component.

    • NumberOfAcceleratorDevicesRequired (float) --

      The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.

    • MinMemoryRequiredInMb (integer) -- [REQUIRED]

      The minimum MB of memory to allocate to run a model that you assign to an inference component.

    • MaxMemoryRequiredInMb (integer) --

      The maximum MB of memory to allocate to run a model that you assign to an inference component.

type RuntimeConfig

dict

param RuntimeConfig

[REQUIRED]

Runtime settings for a model that is deployed with an inference component.

  • CopyCount (integer) -- [REQUIRED]

    The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.

type Tags

list

param Tags

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 .

  • (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.

rtype

dict

returns

Response Syntax

{
    'InferenceComponentArn': 'string'
}

Response Structure

  • (dict) --

    • InferenceComponentArn (string) --

      The Amazon Resource Name (ARN) of the inference component.

DeleteInferenceComponent (new) Link ¶

Deletes an inference component.

See also: AWS API Documentation

Request Syntax

client.delete_inference_component(
    InferenceComponentName='string'
)
type InferenceComponentName

string

param InferenceComponentName

[REQUIRED]

The name of the inference component to delete.

returns

None

ListInferenceComponents (new) Link ¶

Lists the inference components in your account and their properties.

See also: AWS API Documentation

Request Syntax

client.list_inference_components(
    SortBy='Name'|'CreationTime'|'Status',
    SortOrder='Ascending'|'Descending',
    NextToken='string',
    MaxResults=123,
    NameContains='string',
    CreationTimeBefore=datetime(2015, 1, 1),
    CreationTimeAfter=datetime(2015, 1, 1),
    LastModifiedTimeBefore=datetime(2015, 1, 1),
    LastModifiedTimeAfter=datetime(2015, 1, 1),
    StatusEquals='InService'|'Creating'|'Updating'|'Failed'|'Deleting',
    EndpointNameEquals='string',
    VariantNameEquals='string'
)
type SortBy

string

param SortBy

The field by which to sort the inference components in the response. The default is CreationTime .

type SortOrder

string

param SortOrder

The sort order for results. The default is Descending .

type NextToken

string

param NextToken

A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

type MaxResults

integer

param MaxResults

The maximum number of inference components to return in the response. This value defaults to 10.

type NameContains

string

param NameContains

Filters the results to only those inference components with a name that contains the specified string.

type CreationTimeBefore

datetime

param CreationTimeBefore

Filters the results to only those inference components that were created before the specified time.

type CreationTimeAfter

datetime

param CreationTimeAfter

Filters the results to only those inference components that were created after the specified time.

type LastModifiedTimeBefore

datetime

param LastModifiedTimeBefore

Filters the results to only those inference components that were updated before the specified time.

type LastModifiedTimeAfter

datetime

param LastModifiedTimeAfter

Filters the results to only those inference components that were updated after the specified time.

type StatusEquals

string

param StatusEquals

Filters the results to only those inference components with the specified status.

type EndpointNameEquals

string

param EndpointNameEquals

An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.

type VariantNameEquals

string

param VariantNameEquals

A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.

rtype

dict

returns

Response Syntax

{
    'InferenceComponents': [
        {
            'CreationTime': datetime(2015, 1, 1),
            'InferenceComponentArn': 'string',
            'InferenceComponentName': 'string',
            'EndpointArn': 'string',
            'EndpointName': 'string',
            'VariantName': 'string',
            'InferenceComponentStatus': 'InService'|'Creating'|'Updating'|'Failed'|'Deleting',
            'LastModifiedTime': datetime(2015, 1, 1)
        },
    ],
    'NextToken': 'string'
}

Response Structure

  • (dict) --

    • InferenceComponents (list) --

      A list of inference components and their properties that matches any of the filters you specified in the request.

      • (dict) --

        A summary of the properties of an inference component.

        • CreationTime (datetime) --

          The time when the inference component was created.

        • InferenceComponentArn (string) --

          The Amazon Resource Name (ARN) of the inference component.

        • InferenceComponentName (string) --

          The name of the inference component.

        • EndpointArn (string) --

          The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.

        • EndpointName (string) --

          The name of the endpoint that hosts the inference component.

        • VariantName (string) --

          The name of the production variant that hosts the inference component.

        • InferenceComponentStatus (string) --

          The status of the inference component.

        • LastModifiedTime (datetime) --

          The time when the inference component was last updated.

    • NextToken (string) --

      The token to use in a subsequent request to get the next set of results following a truncated response.

CreateCluster (new) Link ¶

Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide .

See also: AWS API Documentation

Request Syntax

client.create_cluster(
    ClusterName='string',
    InstanceGroups=[
        {
            'InstanceCount': 123,
            'InstanceGroupName': 'string',
            'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.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.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge',
            'LifeCycleConfig': {
                'SourceS3Uri': 'string',
                'OnCreate': 'string'
            },
            'ExecutionRole': 'string',
            'ThreadsPerCore': 123
        },
    ],
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type ClusterName

string

param ClusterName

[REQUIRED]

The name for the new SageMaker HyperPod cluster.

type InstanceGroups

list

param InstanceGroups

[REQUIRED]

The instance groups to be created in the SageMaker HyperPod cluster.

  • (dict) --

    The specifications of an instance group that you need to define.

    • InstanceCount (integer) -- [REQUIRED]

      Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.

    • InstanceGroupName (string) -- [REQUIRED]

      Specifies the name of the instance group.

    • InstanceType (string) -- [REQUIRED]

      Specifies the instance type of the instance group.

    • LifeCycleConfig (dict) -- [REQUIRED]

      Specifies the LifeCycle configuration for the instance group.

      • SourceS3Uri (string) -- [REQUIRED]

        An Amazon S3 bucket path where your LifeCycle scripts are stored.

      • OnCreate (string) -- [REQUIRED]

        The directory of the LifeCycle script under SourceS3Uri . This LifeCycle script runs during cluster creation.

    • ExecutionRole (string) -- [REQUIRED]

      Specifies an IAM execution role to be assumed by the instance group.

    • ThreadsPerCore (integer) --

      Specifies the value for Threads per core . For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn't support multithreading, specify 1 . For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide .

type VpcConfig

dict

param VpcConfig

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

  • 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) --

type Tags

list

param Tags

Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources 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) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'ClusterArn': 'string'
}

Response Structure

  • (dict) --

    • ClusterArn (string) --

      The Amazon Resource Name (ARN) of the cluster.

UpdateInferenceComponent (new) Link ¶

Updates an inference component.

See also: AWS API Documentation

Request Syntax

client.update_inference_component(
    InferenceComponentName='string',
    Specification={
        'ModelName': 'string',
        'Container': {
            'Image': 'string',
            'ArtifactUrl': 'string',
            'Environment': {
                'string': 'string'
            }
        },
        'StartupParameters': {
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123
        },
        'ComputeResourceRequirements': {
            'NumberOfCpuCoresRequired': ...,
            'NumberOfAcceleratorDevicesRequired': ...,
            'MinMemoryRequiredInMb': 123,
            'MaxMemoryRequiredInMb': 123
        }
    },
    RuntimeConfig={
        'CopyCount': 123
    }
)
type InferenceComponentName

string

param InferenceComponentName

[REQUIRED]

The name of the inference component.

type Specification

dict

param Specification

Details about the resources to deploy with this inference component, including the model, container, and compute resources.

  • ModelName (string) --

    The name of an existing SageMaker model object in your account that you want to deploy with the inference component.

  • Container (dict) --

    Defines a container that provides the runtime environment for a model that you deploy with an inference component.

    • Image (string) --

      The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

    • ArtifactUrl (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).

    • 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) --

  • StartupParameters (dict) --

    Settings that take effect while the model container starts up.

    • ModelDataDownloadTimeoutInSeconds (integer) --

      The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

    • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

      The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

  • ComputeResourceRequirements (dict) -- [REQUIRED]

    The compute resources allocated to run the model assigned to the inference component.

    • NumberOfCpuCoresRequired (float) --

      The number of CPU cores to allocate to run a model that you assign to an inference component.

    • NumberOfAcceleratorDevicesRequired (float) --

      The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.

    • MinMemoryRequiredInMb (integer) -- [REQUIRED]

      The minimum MB of memory to allocate to run a model that you assign to an inference component.

    • MaxMemoryRequiredInMb (integer) --

      The maximum MB of memory to allocate to run a model that you assign to an inference component.

type RuntimeConfig

dict

param RuntimeConfig

Runtime settings for a model that is deployed with an inference component.

  • CopyCount (integer) -- [REQUIRED]

    The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.

rtype

dict

returns

Response Syntax

{
    'InferenceComponentArn': 'string'
}

Response Structure

  • (dict) --

    • InferenceComponentArn (string) --

      The Amazon Resource Name (ARN) of the inference component.

CreateApp (updated) Link ¶
Changes (request)
{'ResourceSpec': {'SageMakerImageVersionAlias': 'string'}}

Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

See also: AWS API Documentation

Request Syntax

client.create_app(
    DomainId='string',
    UserProfileName='string',
    AppType='JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
    AppName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ResourceSpec={
        'SageMakerImageArn': 'string',
        'SageMakerImageVersionArn': 'string',
        'SageMakerImageVersionAlias': 'string',
        'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
        'LifecycleConfigArn': 'string'
    },
    SpaceName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

The user profile name. If this value is not set, then SpaceName must be set.

type AppType

string

param AppType

[REQUIRED]

The type of app.

type AppName

string

param AppName

[REQUIRED]

The name of the app.

type Tags

list

param Tags

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • (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.

type ResourceSpec

dict

param ResourceSpec

The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

Note

The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

  • SageMakerImageArn (string) --

    The ARN of the SageMaker image that the image version belongs to.

  • SageMakerImageVersionArn (string) --

    The ARN of the image version created on the instance.

  • SageMakerImageVersionAlias (string) --

    The SageMakerImageVersionAlias.

  • InstanceType (string) --

    The instance type that the image version runs on.

    Note

    JupyterServer apps only support the system value.

    For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

  • LifecycleConfigArn (string) --

    The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

type SpaceName

string

param SpaceName

The name of the space. If this value is not set, then UserProfileName must be set.

rtype

dict

returns

Response Syntax

{
    'AppArn': 'string'
}

Response Structure

  • (dict) --

    • AppArn (string) --

      The Amazon Resource Name (ARN) of the app.

CreateAutoMLJobV2 (updated) Link ¶
Changes (request)
{'AutoMLProblemTypeConfig': {'TextGenerationJobConfig': {'TextGenerationHyperParameters': {'string': 'string'}}}}

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

Note

CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob , as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2 .

For the list of available problem types supported by CreateAutoMLJobV2 , see AutoMLProblemTypeConfig .

You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2 .

See also: AWS API Documentation

Request Syntax

client.create_auto_ml_job_v2(
    AutoMLJobName='string',
    AutoMLJobInputDataConfig=[
        {
            'ChannelType': 'training'|'validation',
            'ContentType': 'string',
            'CompressionType': 'None'|'Gzip',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string'
                }
            }
        },
    ],
    OutputDataConfig={
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    AutoMLProblemTypeConfig={
        'ImageClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            }
        },
        'TextClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ContentColumn': 'string',
            'TargetLabelColumn': 'string'
        },
        'TabularJobConfig': {
            'CandidateGenerationConfig': {
                'AlgorithmsConfig': [
                    {
                        'AutoMLAlgorithms': [
                            'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
                        ]
                    },
                ]
            },
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'FeatureSpecificationS3Uri': 'string',
            'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
            'GenerateCandidateDefinitionsOnly': True|False,
            'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
            'TargetAttributeName': 'string',
            'SampleWeightAttributeName': 'string'
        },
        'TimeSeriesForecastingJobConfig': {
            'FeatureSpecificationS3Uri': 'string',
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ForecastFrequency': 'string',
            'ForecastHorizon': 123,
            'ForecastQuantiles': [
                'string',
            ],
            'Transformations': {
                'Filling': {
                    'string': {
                        'string': 'string'
                    }
                },
                'Aggregation': {
                    'string': 'sum'|'avg'|'first'|'min'|'max'
                }
            },
            'TimeSeriesConfig': {
                'TargetAttributeName': 'string',
                'TimestampAttributeName': 'string',
                'ItemIdentifierAttributeName': 'string',
                'GroupingAttributeNames': [
                    'string',
                ]
            },
            'HolidayConfig': [
                {
                    'CountryCode': 'string'
                },
            ]
        },
        'TextGenerationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'BaseModelName': 'string',
            'TextGenerationHyperParameters': {
                'string': 'string'
            }
        }
    },
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    SecurityConfig={
        'VolumeKmsKeyId': 'string',
        'EnableInterContainerTrafficEncryption': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    AutoMLJobObjective={
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
    },
    ModelDeployConfig={
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    },
    DataSplitConfig={
        'ValidationFraction': ...
    }
)
type AutoMLJobName

string

param AutoMLJobName

[REQUIRED]

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

type AutoMLJobInputDataConfig

list

param AutoMLJobInputDataConfig

[REQUIRED]

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

  • For tabular problem types: S3Prefix , ManifestFile .

  • For image classification: S3Prefix , ManifestFile , AugmentedManifestFile .

  • For text classification: S3Prefix .

  • For time-series forecasting: S3Prefix .

  • For text generation (LLMs fine-tuning): S3Prefix .

  • (dict) --

    A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).

    • ChannelType (string) --

      The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType

      Note

      The type of channel defaults to training for the time-series forecasting problem type.

    • ContentType (string) --

      The content type of the data from the input source. The following are the allowed content types for different problems:

      • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • For image classification: image/png , image/jpeg , or image/* . The default value is image/* .

      • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

    • CompressionType (string) --

      The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .

    • DataSource (dict) --

      The data source for an AutoML channel (Required).

      • S3DataSource (dict) -- [REQUIRED]

        The Amazon S3 location of the input data.

        • S3DataType (string) -- [REQUIRED]

          The data type.

          • If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

          • 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. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

          • 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 is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .

        • S3Uri (string) -- [REQUIRED]

          The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

type OutputDataConfig

dict

param OutputDataConfig

[REQUIRED]

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

  • KmsKeyId (string) --

    The Key Management Service (KMS) encryption key ID.

  • S3OutputPath (string) -- [REQUIRED]

    The Amazon S3 output path. Must be 128 characters or less.

type AutoMLProblemTypeConfig

dict

param AutoMLProblemTypeConfig

[REQUIRED]

Defines the configuration settings of one of the supported problem types.

  • ImageClassificationJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the image classification problem type.

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

  • TextClassificationJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the text classification problem type.

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • ContentColumn (string) -- [REQUIRED]

      The name of the column used to provide the sentences to be classified. It should not be the same as the target column.

    • TargetLabelColumn (string) -- [REQUIRED]

      The name of the column used to provide the class labels. It should not be same as the content column.

  • TabularJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

    • CandidateGenerationConfig (dict) --

      The configuration information of how model candidates are generated.

      • AlgorithmsConfig (list) --

        Stores the configuration information for the selection of algorithms used to train model candidates on tabular data.

        The list of available algorithms to choose from depends on the training mode set in ` TabularJobConfig.Mode https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html`__ .

        • AlgorithmsConfig should not be set in AUTO training mode.

        • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

        • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

        For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig .

        For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

        • (dict) --

          The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.

          • AutoMLAlgorithms (list) -- [REQUIRED]

            The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

            Note

            Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.

            • In ENSEMBLING mode:

              • "catboost"

              • "extra-trees"

              • "fastai"

              • "lightgbm"

              • "linear-learner"

              • "nn-torch"

              • "randomforest"

              • "xgboost"

            • In HYPERPARAMETER_TUNING mode:

              • "linear-learner"

              • "mlp"

              • "xgboost"

            • (string) --

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • FeatureSpecificationS3Uri (string) --

      A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

      { "FeatureAttributeNames":["col1", "col2", ...] } .

      You can also specify the data type of the feature (optional) in the format shown below:

      { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

      Note

      These column keys may not include the target column.

      In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .

      If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.

      If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .

      The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

    • Mode (string) --

      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

    • GenerateCandidateDefinitionsOnly (boolean) --

      Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    • ProblemType (string) --

      The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types .

      Note

      You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

    • TargetAttributeName (string) -- [REQUIRED]

      The name of the target variable in supervised learning, usually represented by 'y'.

    • SampleWeightAttributeName (string) --

      If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation .

      Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

      Support for sample weights is available in Ensembling mode only.

  • TimeSeriesForecastingJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

    • FeatureSpecificationS3Uri (string) --

      A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .

      You can input FeatureAttributeNames (optional) in JSON format as shown below:

      { "FeatureAttributeNames":["col1", "col2", ...] } .

      You can also specify the data type of the feature (optional) in the format shown below:

      { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

      Autopilot supports the following data types: numeric , categorical , text , and datetime .

      Note

      These column keys must not include any column set in TimeSeriesConfig .

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • ForecastFrequency (string) -- [REQUIRED]

      The frequency of predictions in a forecast.

      Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .

      The valid values for each frequency are the following:

      • Minute - 1-59

      • Hour - 1-23

      • Day - 1-6

      • Week - 1-4

      • Month - 1-11

      • Year - 1

    • ForecastHorizon (integer) -- [REQUIRED]

      The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.

    • ForecastQuantiles (list) --

      The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

      • (string) --

    • Transformations (dict) --

      The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

      • Filling (dict) --

        A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.

        The supported filling methods and their corresponding options are:

        • frontfill : none (Supported only for target column)

        • middlefill : zero , value , median , mean , min , max

        • backfill : zero , value , median , mean , min , max

        • futurefill : zero , value , median , mean , min , max

        To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .

        • (string) --

          • (dict) --

            • (string) --

              • (string) --

      • Aggregation (dict) --

        A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.

        The supported aggregation methods are sum (default), avg , first , min , max .

        Note

        Aggregation is only supported for the target column.

        • (string) --

          • (string) --

    • TimeSeriesConfig (dict) -- [REQUIRED]

      The collection of components that defines the time-series.

      • TargetAttributeName (string) -- [REQUIRED]

        The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.

      • TimestampAttributeName (string) -- [REQUIRED]

        The name of the column indicating a point in time at which the target value of a given item is recorded.

      • ItemIdentifierAttributeName (string) -- [REQUIRED]

        The name of the column that represents the set of item identifiers for which you want to predict the target value.

      • GroupingAttributeNames (list) --

        A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.

        • (string) --

    • HolidayConfig (list) --

      The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

      • (dict) --

        Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.

        • CountryCode (string) --

          The country code for the holiday calendar.

          For the list of public holiday calendars supported by AutoML job V2, see Country Codes . Use the country code corresponding to the country of your choice.

  • TextGenerationJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.

    Note

    The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions .

    • CompletionCriteria (dict) --

      How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • BaseModelName (string) --

      The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot . If no BaseModelName is provided, the default model used is Falcon7BInstruct .

    • TextGenerationHyperParameters (dict) --

      The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters .

      • "epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

      • "batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

      • "learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

      • "learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

      Here is an example where all four hyperparameters are configured.

      { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

      • (string) --

        • (string) --

type RoleArn

string

param RoleArn

[REQUIRED]

The ARN of the role that is used to access the data.

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources . Tag keys must be unique per resource.

  • (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.

type SecurityConfig

dict

param SecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.

  • VolumeKmsKeyId (string) --

    The key used to encrypt stored data.

  • EnableInterContainerTrafficEncryption (boolean) --

    Whether to use traffic encryption between the container layers.

  • VpcConfig (dict) --

    The VPC configuration.

    • 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) --

type AutoMLJobObjective

dict

param AutoMLJobObjective

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective .

Note

  • For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType ), or none at all.

  • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

  • MetricName (string) -- [REQUIRED]

    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

    The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

    • For tabular problem types:

      • List of available metrics:

        • Regression: InferenceLatency , MAE , MSE , R2 , RMSE

        • Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall

        • Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro

      For a description of each metric, see Autopilot metrics for classification and regression .

      • Default objective metrics:

        • Regression: MSE .

        • Binary classification: F1 .

        • Multiclass classification: Accuracy .

    • For image or text classification problem types:

    • For time-series forecasting problem types:

    • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

type ModelDeployConfig

dict

param ModelDeployConfig

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

  • AutoGenerateEndpointName (boolean) --

    Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

    Note

    If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

  • EndpointName (string) --

    Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

    Note

    Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

type DataSplitConfig

dict

param DataSplitConfig

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob , the validation dataset must be less than 2 GB in size.

Note

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

  • ValidationFraction (float) --

    The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

rtype

dict

returns

Response Syntax

{
    'AutoMLJobArn': 'string'
}

Response Structure

  • (dict) --

    • AutoMLJobArn (string) --

      The unique ARN assigned to the AutoMLJob when it is created.

CreateDomain (updated) Link ¶
Changes (request)
{'DefaultSpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                          'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}},
 'DefaultUserSettings': {'DefaultLandingUri': 'string',
                         'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                         'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                         'RSessionAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                         'StudioWebPortal': 'ENABLED | DISABLED',
                         'TensorBoardAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}},
 'DomainSettings': {'RStudioServerProDomainSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}}}

Creates a Domain . A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.

EFS storage

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption .

VPC configuration

All traffic between the domain and the EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:

  • PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.

  • VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.

Warning

NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.

For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC .

See also: AWS API Documentation

Request Syntax

client.create_domain(
    DomainName='string',
    AuthMode='SSO'|'IAM',
    DefaultUserSettings={
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'SharingSettings': {
            'NotebookOutputOption': 'Allowed'|'Disabled',
            'S3OutputPath': 'string',
            'S3KmsKeyId': 'string'
        },
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        },
        'TensorBoardAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'RStudioServerProAppSettings': {
            'AccessStatus': 'ENABLED'|'DISABLED',
            'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
        },
        'RSessionAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ]
        },
        'CanvasAppSettings': {
            'TimeSeriesForecastingSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'AmazonForecastRoleArn': 'string'
            },
            'ModelRegisterSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'CrossAccountModelRegisterRoleArn': 'string'
            },
            'WorkspaceSettings': {
                'S3ArtifactPath': 'string',
                'S3KmsKeyId': 'string'
            },
            'IdentityProviderOAuthSettings': [
                {
                    'DataSourceName': 'SalesforceGenie'|'Snowflake',
                    'Status': 'ENABLED'|'DISABLED',
                    'SecretArn': 'string'
                },
            ],
            'KendraSettings': {
                'Status': 'ENABLED'|'DISABLED'
            },
            'DirectDeploySettings': {
                'Status': 'ENABLED'|'DISABLED'
            }
        },
        'DefaultLandingUri': 'string',
        'StudioWebPortal': 'ENABLED'|'DISABLED'
    },
    SubnetIds=[
        'string',
    ],
    VpcId='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
    HomeEfsFileSystemKmsKeyId='string',
    KmsKeyId='string',
    AppSecurityGroupManagement='Service'|'Customer',
    DomainSettings={
        'SecurityGroupIds': [
            'string',
        ],
        'RStudioServerProDomainSettings': {
            'DomainExecutionRoleArn': 'string',
            'RStudioConnectUrl': 'string',
            'RStudioPackageManagerUrl': 'string',
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED'
    },
    DefaultSpaceSettings={
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        }
    }
)
type DomainName

string

param DomainName

[REQUIRED]

A name for the domain.

type AuthMode

string

param AuthMode

[REQUIRED]

The mode of authentication that members use to access the domain.

type DefaultUserSettings

dict

param DefaultUserSettings

[REQUIRED]

The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.

SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings , the values specified in CreateUserProfile take precedence over those specified in CreateDomain .

  • ExecutionRole (string) --

    The execution role for the user.

  • SecurityGroups (list) --

    The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

    Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .

    Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.

    Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.

    • (string) --

  • SharingSettings (dict) --

    Specifies options for sharing Amazon SageMaker Studio notebooks.

    • NotebookOutputOption (string) --

      Whether to include the notebook cell output when sharing the notebook. The default is Disabled .

    • S3OutputPath (string) --

      When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.

    • S3KmsKeyId (string) --

      When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.

  • JupyterServerAppSettings (dict) --

    The Jupyter server's app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

    • CodeRepositories (list) --

      A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

      • (dict) --

        A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • RepositoryUrl (string) -- [REQUIRED]

          The URL of the Git repository.

  • KernelGatewayAppSettings (dict) --

    The kernel gateway app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

      Note

      The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a KernelGateway app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

  • TensorBoardAppSettings (dict) --

    The TensorBoard app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

  • RStudioServerProAppSettings (dict) --

    A collection of settings that configure user interaction with the RStudioServerPro app.

    • AccessStatus (string) --

      Indicates whether the current user has access to the RStudioServerPro app.

    • UserGroup (string) --

      The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.

  • RSessionAppSettings (dict) --

    A collection of settings that configure the RSessionGateway app.

    • DefaultResourceSpec (dict) --

      Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a RSession app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

  • CanvasAppSettings (dict) --

    The Canvas app settings.

    • TimeSeriesForecastingSettings (dict) --

      Time series forecast settings for the SageMaker Canvas application.

      • Status (string) --

        Describes whether time series forecasting is enabled or disabled in the Canvas application.

      • AmazonForecastRoleArn (string) --

        The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.

    • ModelRegisterSettings (dict) --

      The model registry settings for the SageMaker Canvas application.

      • Status (string) --

        Describes whether the integration to the model registry is enabled or disabled in the Canvas application.

      • CrossAccountModelRegisterRoleArn (string) --

        The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.

    • WorkspaceSettings (dict) --

      The workspace settings for the SageMaker Canvas application.

      • S3ArtifactPath (string) --

        The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.

      • S3KmsKeyId (string) --

        The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.

    • IdentityProviderOAuthSettings (list) --

      The settings for connecting to an external data source with OAuth.

      • (dict) --

        The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

        • DataSourceName (string) --

          The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.

        • Status (string) --

          Describes whether OAuth for a data source is enabled or disabled in the Canvas application.

        • SecretArn (string) --

          The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.

    • KendraSettings (dict) --

      The settings for document querying.

      • Status (string) --

        Describes whether the document querying feature is enabled or disabled in the Canvas application.

    • DirectDeploySettings (dict) --

      The model deployment settings for the SageMaker Canvas application.

      • Status (string) --

        Describes whether model deployment permissions are enabled or disabled in the Canvas application.

  • DefaultLandingUri (string) --

    The default experience that the user is directed to when accessing the domain. The supported values are:

    • studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .

    • app:JupyterServer: : Indicates that Studio Classic is the default experience.

  • StudioWebPortal (string) --

    Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.

type SubnetIds

list

param SubnetIds

[REQUIRED]

The VPC subnets that the domain uses for communication.

  • (string) --

type VpcId

string

param VpcId

[REQUIRED]

The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

type Tags

list

param Tags

Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

Tags that you specify for the Domain are also added to all Apps that the Domain 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.

type AppNetworkAccessType

string

param AppNetworkAccessType

Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .

  • PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access

  • VpcOnly - All traffic is through the specified VPC and subnets

type HomeEfsFileSystemKmsKeyId

string

param HomeEfsFileSystemKmsKeyId

Use KmsKeyId .

type KmsKeyId

string

param KmsKeyId

SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.

type AppSecurityGroupManagement

string

param AppSecurityGroupManagement

The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service .

type DomainSettings

dict

param DomainSettings

A collection of Domain settings.

  • SecurityGroupIds (list) --

    The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

    • (string) --

  • RStudioServerProDomainSettings (dict) --

    A collection of settings that configure the RStudioServerPro Domain-level app.

    • DomainExecutionRoleArn (string) -- [REQUIRED]

      The ARN of the execution role for the RStudioServerPro Domain-level app.

    • RStudioConnectUrl (string) --

      A URL pointing to an RStudio Connect server.

    • RStudioPackageManagerUrl (string) --

      A URL pointing to an RStudio Package Manager server.

    • DefaultResourceSpec (dict) --

      Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

  • ExecutionRoleIdentityConfig (string) --

    The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key .

type DefaultSpaceSettings

dict

param DefaultSpaceSettings

The default settings used to create a space.

  • ExecutionRole (string) --

    The ARN of the execution role for the space.

  • SecurityGroups (list) --

    The security group IDs for the Amazon Virtual Private Cloud that the space uses for communication.

    • (string) --

  • JupyterServerAppSettings (dict) --

    The JupyterServer app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

    • CodeRepositories (list) --

      A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

      • (dict) --

        A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • RepositoryUrl (string) -- [REQUIRED]

          The URL of the Git repository.

  • KernelGatewayAppSettings (dict) --

    The KernelGateway app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

      Note

      The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a KernelGateway app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

rtype

dict

returns

Response Syntax

{
    'DomainArn': 'string',
    'Url': 'string'
}

Response Structure

  • (dict) --

    • DomainArn (string) --

      The Amazon Resource Name (ARN) of the created domain.

    • Url (string) --

      The URL to the created domain.

CreateEndpointConfig (updated) Link ¶
Changes (request)
{'EnableNetworkIsolation': 'boolean',
 'ExecutionRoleArn': 'string',
 'ProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                   'MinInstanceCount': 'integer',
                                                   'Status': 'ENABLED | '
                                                             'DISABLED'},
                        'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                             '| RANDOM'}},
 'ShadowProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                         'MinInstanceCount': 'integer',
                                                         'Status': 'ENABLED | '
                                                                   'DISABLED'},
                              'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                                   '| RANDOM'}},
 'VpcConfig': {'SecurityGroupIds': ['string'], 'Subnets': ['string']}}

Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.

Note

Use this API if you want to use SageMaker hosting services to deploy models into production.

In the request, you define a ProductionVariant , for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

Note

When you call CreateEndpoint , a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__ , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

See also: AWS API Documentation

Request Syntax

client.create_endpoint_config(
    EndpointConfigName='string',
    ProductionVariants=[
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': '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'|'ml.p5.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    DataCaptureConfig={
        'EnableCapture': True|False,
        'InitialSamplingPercentage': 123,
        'DestinationS3Uri': 'string',
        'KmsKeyId': 'string',
        'CaptureOptions': [
            {
                'CaptureMode': 'Input'|'Output'
            },
        ],
        'CaptureContentTypeHeader': {
            'CsvContentTypes': [
                'string',
            ],
            'JsonContentTypes': [
                'string',
            ]
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    KmsKeyId='string',
    AsyncInferenceConfig={
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string',
                'IncludeInferenceResponseIn': [
                    'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
                ]
            },
            'S3FailurePath': 'string'
        }
    },
    ExplainerConfig={
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    ShadowProductionVariants=[
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': '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'|'ml.p5.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    ExecutionRoleArn='string',
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    EnableNetworkIsolation=True|False
)
type EndpointConfigName

string

param EndpointConfigName

[REQUIRED]

The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

type ProductionVariants

list

param ProductionVariants

[REQUIRED]

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

  • (dict) --

    Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

    • VariantName (string) -- [REQUIRED]

      The name of the production variant.

    • ModelName (string) --

      The name of the model that you want to host. This is the name that you specified when creating the model.

    • InitialInstanceCount (integer) --

      Number of instances to launch initially.

    • InstanceType (string) --

      The ML compute instance type.

    • InitialVariantWeight (float) --

      Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

    • AcceleratorType (string) --

      The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

    • CoreDumpConfig (dict) --

      Specifies configuration for a core dump from the model container when the process crashes.

      • DestinationS3Uri (string) -- [REQUIRED]

        The Amazon S3 bucket to send the core dump to.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data 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 CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

    • ServerlessConfig (dict) --

      The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

      • MemorySizeInMB (integer) -- [REQUIRED]

        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) -- [REQUIRED]

        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 .

    • VolumeSizeInGB (integer) --

      The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

    • ModelDataDownloadTimeoutInSeconds (integer) --

      The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

    • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

      The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

    • EnableSSMAccess (boolean) --

      You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

    • ManagedInstanceScaling (dict) --

      Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

      • Status (string) --

        Indicates whether managed instance scaling is enabled.

      • MinInstanceCount (integer) --

        The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

      • MaxInstanceCount (integer) --

        The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

    • RoutingConfig (dict) --

      Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

      • RoutingStrategy (string) -- [REQUIRED]

        Sets how the endpoint routes incoming traffic:

        • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

        • RANDOM : The endpoint routes each request to a randomly chosen instance.

type DataCaptureConfig

dict

param DataCaptureConfig

Configuration to control how SageMaker captures inference data.

  • EnableCapture (boolean) --

    Whether data capture should be enabled or disabled (defaults to enabled).

  • InitialSamplingPercentage (integer) -- [REQUIRED]

    The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.

  • DestinationS3Uri (string) -- [REQUIRED]

    The Amazon S3 location used to capture the data.

  • KmsKeyId (string) --

    The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data 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

  • CaptureOptions (list) -- [REQUIRED]

    Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

    • (dict) --

      Specifies data Model Monitor will capture.

      • CaptureMode (string) -- [REQUIRED]

        Specify the boundary of data to capture.

  • CaptureContentTypeHeader (dict) --

    Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.

    • CsvContentTypes (list) --

      The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.

      • (string) --

    • JsonContentTypes (list) --

      The list of all content type headers that SageMaker will treat as JSON and capture accordingly.

      • (string) --

type Tags

list

param Tags

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.

type KmsKeyId

string

param KmsKeyId

The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

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

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint , UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section`Using Key Policies in Amazon Web Services KMS <https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html>`__

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 KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.

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 .

type AsyncInferenceConfig

dict

param AsyncInferenceConfig

Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync .

  • ClientConfig (dict) --

    Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

    • MaxConcurrentInvocationsPerInstance (integer) --

      The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

  • OutputConfig (dict) -- [REQUIRED]

    Specifies the configuration for asynchronous inference invocation outputs.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

    • S3OutputPath (string) --

      The Amazon S3 location to upload inference responses to.

    • NotificationConfig (dict) --

      Specifies the configuration for notifications of inference results for asynchronous inference.

      • SuccessTopic (string) --

        Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

      • ErrorTopic (string) --

        Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

      • IncludeInferenceResponseIn (list) --

        The Amazon SNS topics where you want the inference response to be included.

        Note

        The inference response is included only if the response size is less than or equal to 128 KB.

        • (string) --

    • S3FailurePath (string) --

      The Amazon S3 location to upload failure inference responses to.

type ExplainerConfig

dict

param ExplainerConfig

A member of CreateEndpointConfig that enables explainers.

  • ClarifyExplainerConfig (dict) --

    A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

    • EnableExplanations (string) --

      A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

    • InferenceConfig (dict) --

      The inference configuration parameter for the model container.

      • FeaturesAttribute (string) --

        Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

      • ContentTemplate (string) --

        A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

      • MaxRecordCount (integer) --

        The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

      • MaxPayloadInMB (integer) --

        The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

      • ProbabilityIndex (integer) --

        A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

        Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

        Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

      • LabelIndex (integer) --

        A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

        Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .

      • ProbabilityAttribute (string) --

        A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

        Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .

      • LabelAttribute (string) --

        A JMESPath expression used to locate the list of label headers in the model container output.

        Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

      • LabelHeaders (list) --

        For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

        • (string) --

      • FeatureHeaders (list) --

        The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

        • (string) --

      • FeatureTypes (list) --

        A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

        • (string) --

    • ShapConfig (dict) -- [REQUIRED]

      The configuration for SHAP analysis.

      • ShapBaselineConfig (dict) -- [REQUIRED]

        The configuration for the SHAP baseline of the Kernal SHAP algorithm.

        • MimeType (string) --

          The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

        • ShapBaseline (string) --

          The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

        • ShapBaselineUri (string) --

          The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .

      • NumberOfSamples (integer) --

        The number of samples to be used for analysis by the Kernal SHAP algorithm.

        Note

        The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .

      • UseLogit (boolean) --

        A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

      • Seed (integer) --

        The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

      • TextConfig (dict) --

        A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

        • Language (string) -- [REQUIRED]

          Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

          Note

          For a mix of multiple languages, use code 'xx' .

        • Granularity (string) -- [REQUIRED]

          The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

type ShadowProductionVariants

list

param ShadowProductionVariants

An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants . If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants .

  • (dict) --

    Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

    • VariantName (string) -- [REQUIRED]

      The name of the production variant.

    • ModelName (string) --

      The name of the model that you want to host. This is the name that you specified when creating the model.

    • InitialInstanceCount (integer) --

      Number of instances to launch initially.

    • InstanceType (string) --

      The ML compute instance type.

    • InitialVariantWeight (float) --

      Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

    • AcceleratorType (string) --

      The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

    • CoreDumpConfig (dict) --

      Specifies configuration for a core dump from the model container when the process crashes.

      • DestinationS3Uri (string) -- [REQUIRED]

        The Amazon S3 bucket to send the core dump to.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data 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 CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

    • ServerlessConfig (dict) --

      The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

      • MemorySizeInMB (integer) -- [REQUIRED]

        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) -- [REQUIRED]

        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 .

    • VolumeSizeInGB (integer) --

      The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

    • ModelDataDownloadTimeoutInSeconds (integer) --

      The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

    • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

      The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

    • EnableSSMAccess (boolean) --

      You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

    • ManagedInstanceScaling (dict) --

      Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

      • Status (string) --

        Indicates whether managed instance scaling is enabled.

      • MinInstanceCount (integer) --

        The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

      • MaxInstanceCount (integer) --

        The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

    • RoutingConfig (dict) --

      Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

      • RoutingStrategy (string) -- [REQUIRED]

        Sets how the endpoint routes incoming traffic:

        • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

        • RANDOM : The endpoint routes each request to a randomly chosen instance.

type ExecutionRoleArn

string

param ExecutionRoleArn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see SageMaker Roles .

Note

To be able to pass this role to Amazon SageMaker, the caller of this action must have the iam:PassRole permission.

type VpcConfig

dict

param VpcConfig

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

  • 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) --

type EnableNetworkIsolation

boolean

param EnableNetworkIsolation

Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

rtype

dict

returns

Response Syntax

{
    'EndpointConfigArn': 'string'
}

Response Structure

  • (dict) --

    • EndpointConfigArn (string) --

      The Amazon Resource Name (ARN) of the endpoint configuration.

CreatePresignedDomainUrl (updated) Link ¶
Changes (request)
{'LandingUri': 'string'}

Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.

The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.

You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .

Note

The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds . If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.

See also: AWS API Documentation

Request Syntax

client.create_presigned_domain_url(
    DomainId='string',
    UserProfileName='string',
    SessionExpirationDurationInSeconds=123,
    ExpiresInSeconds=123,
    SpaceName='string',
    LandingUri='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

[REQUIRED]

The name of the UserProfile to sign-in as.

type SessionExpirationDurationInSeconds

integer

param SessionExpirationDurationInSeconds

The session expiration duration in seconds. This value defaults to 43200.

type ExpiresInSeconds

integer

param ExpiresInSeconds

The number of seconds until the pre-signed URL expires. This value defaults to 300.

type SpaceName

string

param SpaceName

The name of the space.

type LandingUri

string

param LandingUri

The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:

  • studio::relative/path : Directs users to the relative path in Studio.

  • app:JupyterServer:relative/path : Directs users to the relative path in the Studio Classic application.

  • app:JupyterLab:relative/path : Directs users to the relative path in the JupyterLab application.

  • app:RStudioServerPro:relative/path : Directs users to the relative path in the RStudio application.

  • app:Canvas:relative/path : Directs users to the relative path in the Canvas application.

rtype

dict

returns

Response Syntax

{
    'AuthorizedUrl': 'string'
}

Response Structure

  • (dict) --

    • AuthorizedUrl (string) --

      The presigned URL.

CreateSpace (updated) Link ¶
Changes (request)
{'SpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                   'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}}}

Creates a space used for real time collaboration in a Domain.

See also: AWS API Documentation

Request Syntax

client.create_space(
    DomainId='string',
    SpaceName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    SpaceSettings={
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        }
    }
)
type DomainId

string

param DomainId

[REQUIRED]

The ID of the associated Domain.

type SpaceName

string

param SpaceName

[REQUIRED]

The name of the space.

type Tags

list

param Tags

Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.

  • (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.

type SpaceSettings

dict

param SpaceSettings

A collection of space settings.

  • JupyterServerAppSettings (dict) --

    The JupyterServer app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

    • CodeRepositories (list) --

      A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

      • (dict) --

        A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • RepositoryUrl (string) -- [REQUIRED]

          The URL of the Git repository.

  • KernelGatewayAppSettings (dict) --

    The KernelGateway app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

      Note

      The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a KernelGateway app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

rtype

dict

returns

Response Syntax

{
    'SpaceArn': 'string'
}

Response Structure

  • (dict) --

    • SpaceArn (string) --

      The space's Amazon Resource Name (ARN).

CreateTrainingJob (updated) Link ¶
Changes (request)
{'InfraCheckConfig': {'EnableInfraCheck': 'boolean'}}

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,
        'MaxPendingTimeInSeconds': 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
    },
    InfraCheckConfig={
        'EnableInfraCheck': True|False
    }
)
type TrainingJobName

string

param TrainingJobName

[REQUIRED]

The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

type HyperParameters

dict

param HyperParameters

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) --

type AlgorithmSpecification

dict

param AlgorithmSpecification

[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:

  • 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.

type RoleArn

string

param RoleArn

[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.

type InputDataConfig

list

param InputDataConfig

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.

type OutputDataConfig

dict

param OutputDataConfig

[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.

type ResourceConfig

dict

param ResourceConfig

[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.

type VpcConfig

dict

param VpcConfig

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) --

type StoppingCondition

dict

param StoppingCondition

[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.

  • MaxPendingTimeInSeconds (integer) --

    The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

type Tags

list

param Tags

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.

type EnableNetworkIsolation

boolean

param EnableNetworkIsolation

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.

type EnableInterContainerTrafficEncryption

boolean

param EnableInterContainerTrafficEncryption

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 .

type EnableManagedSpotTraining

boolean

param EnableManagedSpotTraining

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.

type CheckpointConfig

dict

param CheckpointConfig

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/ .

type DebugHookConfig

dict

param DebugHookConfig

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) --

type DebugRuleConfigurations

list

param DebugRuleConfigurations

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) --

type TensorBoardOutputConfig

dict

param TensorBoardOutputConfig

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.

type ExperimentConfig

dict

param ExperimentConfig

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.

type ProfilerConfig

dict

param ProfilerConfig

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 .

type ProfilerRuleConfigurations

list

param ProfilerRuleConfigurations

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) --

type Environment

dict

param Environment

The environment variables to set in the Docker container.

  • (string) --

    • (string) --

type RetryStrategy

dict

param RetryStrategy

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 .

type InfraCheckConfig

dict

param InfraCheckConfig

Contains information about the infrastructure health check configuration for the training job.

  • EnableInfraCheck (boolean) --

    Enables an infrastructure health check.

rtype

dict

returns

Response Syntax

{
    'TrainingJobArn': 'string'
}

Response Structure

  • (dict) --

    • TrainingJobArn (string) --

      The Amazon Resource Name (ARN) of the training job.

CreateUserProfile (updated) Link ¶
Changes (request)
{'UserSettings': {'DefaultLandingUri': 'string',
                  'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                  'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                  'RSessionAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                  'StudioWebPortal': 'ENABLED | DISABLED',
                  'TensorBoardAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}}}

Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.

See also: AWS API Documentation

Request Syntax

client.create_user_profile(
    DomainId='string',
    UserProfileName='string',
    SingleSignOnUserIdentifier='string',
    SingleSignOnUserValue='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    UserSettings={
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'SharingSettings': {
            'NotebookOutputOption': 'Allowed'|'Disabled',
            'S3OutputPath': 'string',
            'S3KmsKeyId': 'string'
        },
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        },
        'TensorBoardAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'RStudioServerProAppSettings': {
            'AccessStatus': 'ENABLED'|'DISABLED',
            'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
        },
        'RSessionAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ]
        },
        'CanvasAppSettings': {
            'TimeSeriesForecastingSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'AmazonForecastRoleArn': 'string'
            },
            'ModelRegisterSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'CrossAccountModelRegisterRoleArn': 'string'
            },
            'WorkspaceSettings': {
                'S3ArtifactPath': 'string',
                'S3KmsKeyId': 'string'
            },
            'IdentityProviderOAuthSettings': [
                {
                    'DataSourceName': 'SalesforceGenie'|'Snowflake',
                    'Status': 'ENABLED'|'DISABLED',
                    'SecretArn': 'string'
                },
            ],
            'KendraSettings': {
                'Status': 'ENABLED'|'DISABLED'
            },
            'DirectDeploySettings': {
                'Status': 'ENABLED'|'DISABLED'
            }
        },
        'DefaultLandingUri': 'string',
        'StudioWebPortal': 'ENABLED'|'DISABLED'
    }
)
type DomainId

string

param DomainId

[REQUIRED]

The ID of the associated Domain.

type UserProfileName

string

param UserProfileName

[REQUIRED]

A name for the UserProfile. This value is not case sensitive.

type SingleSignOnUserIdentifier

string

param SingleSignOnUserIdentifier

A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.

type SingleSignOnUserValue

string

param SingleSignOnUserValue

The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.

type Tags

list

param Tags

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Tags that you specify for the User Profile are also added to all Apps that the User Profile 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.

type UserSettings

dict

param UserSettings

A collection of settings.

  • ExecutionRole (string) --

    The execution role for the user.

  • SecurityGroups (list) --

    The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

    Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .

    Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.

    Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.

    • (string) --

  • SharingSettings (dict) --

    Specifies options for sharing Amazon SageMaker Studio notebooks.

    • NotebookOutputOption (string) --

      Whether to include the notebook cell output when sharing the notebook. The default is Disabled .

    • S3OutputPath (string) --

      When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.

    • S3KmsKeyId (string) --

      When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.

  • JupyterServerAppSettings (dict) --

    The Jupyter server's app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

    • CodeRepositories (list) --

      A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

      • (dict) --

        A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • RepositoryUrl (string) -- [REQUIRED]

          The URL of the Git repository.

  • KernelGatewayAppSettings (dict) --

    The kernel gateway app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

      Note

      The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a KernelGateway app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

  • TensorBoardAppSettings (dict) --

    The TensorBoard app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

  • RStudioServerProAppSettings (dict) --

    A collection of settings that configure user interaction with the RStudioServerPro app.

    • AccessStatus (string) --

      Indicates whether the current user has access to the RStudioServerPro app.

    • UserGroup (string) --

      The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.

  • RSessionAppSettings (dict) --

    A collection of settings that configure the RSessionGateway app.

    • DefaultResourceSpec (dict) --

      Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a RSession app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

  • CanvasAppSettings (dict) --

    The Canvas app settings.

    • TimeSeriesForecastingSettings (dict) --

      Time series forecast settings for the SageMaker Canvas application.

      • Status (string) --

        Describes whether time series forecasting is enabled or disabled in the Canvas application.

      • AmazonForecastRoleArn (string) --

        The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.

    • ModelRegisterSettings (dict) --

      The model registry settings for the SageMaker Canvas application.

      • Status (string) --

        Describes whether the integration to the model registry is enabled or disabled in the Canvas application.

      • CrossAccountModelRegisterRoleArn (string) --

        The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.

    • WorkspaceSettings (dict) --

      The workspace settings for the SageMaker Canvas application.

      • S3ArtifactPath (string) --

        The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.

      • S3KmsKeyId (string) --

        The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.

    • IdentityProviderOAuthSettings (list) --

      The settings for connecting to an external data source with OAuth.

      • (dict) --

        The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

        • DataSourceName (string) --

          The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.

        • Status (string) --

          Describes whether OAuth for a data source is enabled or disabled in the Canvas application.

        • SecretArn (string) --

          The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.

    • KendraSettings (dict) --

      The settings for document querying.

      • Status (string) --

        Describes whether the document querying feature is enabled or disabled in the Canvas application.

    • DirectDeploySettings (dict) --

      The model deployment settings for the SageMaker Canvas application.

      • Status (string) --

        Describes whether model deployment permissions are enabled or disabled in the Canvas application.

  • DefaultLandingUri (string) --

    The default experience that the user is directed to when accessing the domain. The supported values are:

    • studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .

    • app:JupyterServer: : Indicates that Studio Classic is the default experience.

  • StudioWebPortal (string) --

    Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.

rtype

dict

returns

Response Syntax

{
    'UserProfileArn': 'string'
}

Response Structure

  • (dict) --

    • UserProfileArn (string) --

      The user profile Amazon Resource Name (ARN).

DescribeApp (updated) Link ¶
Changes (response)
{'ResourceSpec': {'SageMakerImageVersionAlias': 'string'}}

Describes the app.

See also: AWS API Documentation

Request Syntax

client.describe_app(
    DomainId='string',
    UserProfileName='string',
    AppType='JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
    AppName='string',
    SpaceName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

The user profile name. If this value is not set, then SpaceName must be set.

type AppType

string

param AppType

[REQUIRED]

The type of app.

type AppName

string

param AppName

[REQUIRED]

The name of the app.

type SpaceName

string

param SpaceName

The name of the space.

rtype

dict

returns

Response Syntax

{
    'AppArn': 'string',
    'AppType': 'JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
    'AppName': 'string',
    'DomainId': 'string',
    'UserProfileName': 'string',
    'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
    'LastHealthCheckTimestamp': datetime(2015, 1, 1),
    'LastUserActivityTimestamp': datetime(2015, 1, 1),
    'CreationTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'ResourceSpec': {
        'SageMakerImageArn': 'string',
        'SageMakerImageVersionArn': 'string',
        'SageMakerImageVersionAlias': 'string',
        'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
        'LifecycleConfigArn': 'string'
    },
    'SpaceName': 'string'
}

Response Structure

  • (dict) --

    • AppArn (string) --

      The Amazon Resource Name (ARN) of the app.

    • AppType (string) --

      The type of app.

    • AppName (string) --

      The name of the app.

    • DomainId (string) --

      The domain ID.

    • UserProfileName (string) --

      The user profile name.

    • Status (string) --

      The status.

    • LastHealthCheckTimestamp (datetime) --

      The timestamp of the last health check.

    • LastUserActivityTimestamp (datetime) --

      The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp .

    • CreationTime (datetime) --

      The creation time.

    • FailureReason (string) --

      The failure reason.

    • ResourceSpec (dict) --

      The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • SpaceName (string) --

      The name of the space. If this value is not set, then UserProfileName must be set.

DescribeAutoMLJobV2 (updated) Link ¶
Changes (response)
{'AutoMLProblemTypeConfig': {'TextGenerationJobConfig': {'TextGenerationHyperParameters': {'string': 'string'}}}}

Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob .

See also: AWS API Documentation

Request Syntax

client.describe_auto_ml_job_v2(
    AutoMLJobName='string'
)
type AutoMLJobName

string

param AutoMLJobName

[REQUIRED]

Requests information about an AutoML job V2 using its unique name.

rtype

dict

returns

Response Syntax

{
    'AutoMLJobName': 'string',
    'AutoMLJobArn': 'string',
    'AutoMLJobInputDataConfig': [
        {
            'ChannelType': 'training'|'validation',
            'ContentType': 'string',
            'CompressionType': 'None'|'Gzip',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string'
                }
            }
        },
    ],
    'OutputDataConfig': {
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    'RoleArn': 'string',
    'AutoMLJobObjective': {
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
    },
    'AutoMLProblemTypeConfig': {
        'ImageClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            }
        },
        'TextClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ContentColumn': 'string',
            'TargetLabelColumn': 'string'
        },
        'TabularJobConfig': {
            'CandidateGenerationConfig': {
                'AlgorithmsConfig': [
                    {
                        'AutoMLAlgorithms': [
                            'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
                        ]
                    },
                ]
            },
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'FeatureSpecificationS3Uri': 'string',
            'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
            'GenerateCandidateDefinitionsOnly': True|False,
            'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
            'TargetAttributeName': 'string',
            'SampleWeightAttributeName': 'string'
        },
        'TimeSeriesForecastingJobConfig': {
            'FeatureSpecificationS3Uri': 'string',
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ForecastFrequency': 'string',
            'ForecastHorizon': 123,
            'ForecastQuantiles': [
                'string',
            ],
            'Transformations': {
                'Filling': {
                    'string': {
                        'string': 'string'
                    }
                },
                'Aggregation': {
                    'string': 'sum'|'avg'|'first'|'min'|'max'
                }
            },
            'TimeSeriesConfig': {
                'TargetAttributeName': 'string',
                'TimestampAttributeName': 'string',
                'ItemIdentifierAttributeName': 'string',
                'GroupingAttributeNames': [
                    'string',
                ]
            },
            'HolidayConfig': [
                {
                    'CountryCode': 'string'
                },
            ]
        },
        'TextGenerationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'BaseModelName': 'string',
            'TextGenerationHyperParameters': {
                'string': 'string'
            }
        }
    },
    'CreationTime': datetime(2015, 1, 1),
    'EndTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'PartialFailureReasons': [
        {
            'PartialFailureMessage': 'string'
        },
    ],
    'BestCandidate': {
        'CandidateName': 'string',
        'FinalAutoMLJobObjectiveMetric': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
            'Value': ...,
            'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
        },
        'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
        'CandidateSteps': [
            {
                'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
                'CandidateStepArn': 'string',
                'CandidateStepName': 'string'
            },
        ],
        'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
        'InferenceContainers': [
            {
                'Image': 'string',
                'ModelDataUrl': 'string',
                'Environment': {
                    'string': 'string'
                }
            },
        ],
        'CreationTime': datetime(2015, 1, 1),
        'EndTime': datetime(2015, 1, 1),
        'LastModifiedTime': datetime(2015, 1, 1),
        'FailureReason': 'string',
        'CandidateProperties': {
            'CandidateArtifactLocations': {
                'Explainability': 'string',
                'ModelInsights': 'string',
                'BacktestResults': 'string'
            },
            'CandidateMetrics': [
                {
                    'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
                    'Value': ...,
                    'Set': 'Train'|'Validation'|'Test',
                    'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss'
                },
            ]
        },
        'InferenceContainerDefinitions': {
            'string': [
                {
                    'Image': 'string',
                    'ModelDataUrl': 'string',
                    'Environment': {
                        'string': 'string'
                    }
                },
            ]
        }
    },
    'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
    'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'TrainingModels'|'PreTraining',
    'ModelDeployConfig': {
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    },
    'ModelDeployResult': {
        'EndpointName': 'string'
    },
    'DataSplitConfig': {
        'ValidationFraction': ...
    },
    'SecurityConfig': {
        'VolumeKmsKeyId': 'string',
        'EnableInterContainerTrafficEncryption': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    'AutoMLJobArtifacts': {
        'CandidateDefinitionNotebookLocation': 'string',
        'DataExplorationNotebookLocation': 'string'
    },
    'ResolvedAttributes': {
        'AutoMLJobObjective': {
            'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
        },
        'CompletionCriteria': {
            'MaxCandidates': 123,
            'MaxRuntimePerTrainingJobInSeconds': 123,
            'MaxAutoMLJobRuntimeInSeconds': 123
        },
        'AutoMLProblemTypeResolvedAttributes': {
            'TabularResolvedAttributes': {
                'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression'
            },
            'TextGenerationResolvedAttributes': {
                'BaseModelName': 'string'
            }
        }
    },
    'AutoMLProblemTypeConfigName': 'ImageClassification'|'TextClassification'|'Tabular'|'TimeSeriesForecasting'|'TextGeneration'
}

Response Structure

  • (dict) --

    • AutoMLJobName (string) --

      Returns the name of the AutoML job V2.

    • AutoMLJobArn (string) --

      Returns the Amazon Resource Name (ARN) of the AutoML job V2.

    • AutoMLJobInputDataConfig (list) --

      Returns an array of channel objects describing the input data and their location.

      • (dict) --

        A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).

        • ChannelType (string) --

          The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType

          Note

          The type of channel defaults to training for the time-series forecasting problem type.

        • ContentType (string) --

          The content type of the data from the input source. The following are the allowed content types for different problems:

          • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

          • For image classification: image/png , image/jpeg , or image/* . The default value is image/* .

          • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

          • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

          • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

        • CompressionType (string) --

          The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .

        • DataSource (dict) --

          The data source for an AutoML channel (Required).

          • S3DataSource (dict) --

            The Amazon S3 location of the input data.

            • S3DataType (string) --

              The data type.

              • If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

              • 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. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

              • 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 is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .

            • S3Uri (string) --

              The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

    • OutputDataConfig (dict) --

      Returns the job's output data config.

      • KmsKeyId (string) --

        The Key Management Service (KMS) encryption key ID.

      • S3OutputPath (string) --

        The Amazon S3 output path. Must be 128 characters or less.

    • RoleArn (string) --

      The ARN of the Identity and Access Management role that has read permission to the input data location and write permission to the output data location in Amazon S3.

    • AutoMLJobObjective (dict) --

      Returns the job's objective.

      • MetricName (string) --

        The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

        The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

        • For tabular problem types:

          • List of available metrics:

            • Regression: InferenceLatency , MAE , MSE , R2 , RMSE

            • Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall

            • Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro

          For a description of each metric, see Autopilot metrics for classification and regression .

          • Default objective metrics:

            • Regression: MSE .

            • Binary classification: F1 .

            • Multiclass classification: Accuracy .

        • For image or text classification problem types:

        • For time-series forecasting problem types:

        • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

    • AutoMLProblemTypeConfig (dict) --

      Returns the configuration settings of the problem type set for the AutoML job V2.

      • ImageClassificationJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the image classification problem type.

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • TextClassificationJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the text classification problem type.

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • ContentColumn (string) --

          The name of the column used to provide the sentences to be classified. It should not be the same as the target column.

        • TargetLabelColumn (string) --

          The name of the column used to provide the class labels. It should not be same as the content column.

      • TabularJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

        • CandidateGenerationConfig (dict) --

          The configuration information of how model candidates are generated.

          • AlgorithmsConfig (list) --

            Stores the configuration information for the selection of algorithms used to train model candidates on tabular data.

            The list of available algorithms to choose from depends on the training mode set in ` TabularJobConfig.Mode https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html`__ .

            • AlgorithmsConfig should not be set in AUTO training mode.

            • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig .

            For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

            • (dict) --

              The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.

              • AutoMLAlgorithms (list) --

                The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

                Note

                Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.

                • In ENSEMBLING mode:

                  • "catboost"

                  • "extra-trees"

                  • "fastai"

                  • "lightgbm"

                  • "linear-learner"

                  • "nn-torch"

                  • "randomforest"

                  • "xgboost"

                • In HYPERPARAMETER_TUNING mode:

                  • "linear-learner"

                  • "mlp"

                  • "xgboost"

                • (string) --

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • FeatureSpecificationS3Uri (string) --

          A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

          { "FeatureAttributeNames":["col1", "col2", ...] } .

          You can also specify the data type of the feature (optional) in the format shown below:

          { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

          Note

          These column keys may not include the target column.

          In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .

          If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.

          If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .

          The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

        • Mode (string) --

          The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

          The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

          The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

        • GenerateCandidateDefinitionsOnly (boolean) --

          Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

        • ProblemType (string) --

          The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types .

          Note

          You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

        • TargetAttributeName (string) --

          The name of the target variable in supervised learning, usually represented by 'y'.

        • SampleWeightAttributeName (string) --

          If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation .

          Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

          Support for sample weights is available in Ensembling mode only.

      • TimeSeriesForecastingJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

        • FeatureSpecificationS3Uri (string) --

          A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .

          You can input FeatureAttributeNames (optional) in JSON format as shown below:

          { "FeatureAttributeNames":["col1", "col2", ...] } .

          You can also specify the data type of the feature (optional) in the format shown below:

          { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

          Autopilot supports the following data types: numeric , categorical , text , and datetime .

          Note

          These column keys must not include any column set in TimeSeriesConfig .

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • ForecastFrequency (string) --

          The frequency of predictions in a forecast.

          Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .

          The valid values for each frequency are the following:

          • Minute - 1-59

          • Hour - 1-23

          • Day - 1-6

          • Week - 1-4

          • Month - 1-11

          • Year - 1

        • ForecastHorizon (integer) --

          The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.

        • ForecastQuantiles (list) --

          The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

          • (string) --

        • Transformations (dict) --

          The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

          • Filling (dict) --

            A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.

            The supported filling methods and their corresponding options are:

            • frontfill : none (Supported only for target column)

            • middlefill : zero , value , median , mean , min , max

            • backfill : zero , value , median , mean , min , max

            • futurefill : zero , value , median , mean , min , max

            To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .

            • (string) --

              • (dict) --

                • (string) --

                  • (string) --

          • Aggregation (dict) --

            A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.

            The supported aggregation methods are sum (default), avg , first , min , max .

            Note

            Aggregation is only supported for the target column.

            • (string) --

              • (string) --

        • TimeSeriesConfig (dict) --

          The collection of components that defines the time-series.

          • TargetAttributeName (string) --

            The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.

          • TimestampAttributeName (string) --

            The name of the column indicating a point in time at which the target value of a given item is recorded.

          • ItemIdentifierAttributeName (string) --

            The name of the column that represents the set of item identifiers for which you want to predict the target value.

          • GroupingAttributeNames (list) --

            A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.

            • (string) --

        • HolidayConfig (list) --

          The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

          • (dict) --

            Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.

            • CountryCode (string) --

              The country code for the holiday calendar.

              For the list of public holiday calendars supported by AutoML job V2, see Country Codes . Use the country code corresponding to the country of your choice.

      • TextGenerationJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.

        Note

        The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions .

        • CompletionCriteria (dict) --

          How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • BaseModelName (string) --

          The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot . If no BaseModelName is provided, the default model used is Falcon7BInstruct .

        • TextGenerationHyperParameters (dict) --

          The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters .

          • "epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

          • "batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

          • "learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

          • "learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

          Here is an example where all four hyperparameters are configured.

          { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

          • (string) --

            • (string) --

    • CreationTime (datetime) --

      Returns the creation time of the AutoML job V2.

    • EndTime (datetime) --

      Returns the end time of the AutoML job V2.

    • LastModifiedTime (datetime) --

      Returns the job's last modified time.

    • FailureReason (string) --

      Returns the reason for the failure of the AutoML job V2, when applicable.

    • PartialFailureReasons (list) --

      Returns a list of reasons for partial failures within an AutoML job V2.

      • (dict) --

        The reason for a partial failure of an AutoML job.

        • PartialFailureMessage (string) --

          The message containing the reason for a partial failure of an AutoML job.

    • BestCandidate (dict) --

      Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.

      • CandidateName (string) --

        The name of the candidate.

      • FinalAutoMLJobObjectiveMetric (dict) --

        The best candidate result from an AutoML training job.

        • Type (string) --

          The type of metric with the best result.

        • MetricName (string) --

          The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .

        • Value (float) --

          The value of the metric with the best result.

        • StandardMetricName (string) --

          The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics .

      • ObjectiveStatus (string) --

        The objective's status.

      • CandidateSteps (list) --

        Information about the candidate's steps.

        • (dict) --

          Information about the steps for a candidate and what step it is working on.

          • CandidateStepType (string) --

            Whether the candidate is at the transform, training, or processing step.

          • CandidateStepArn (string) --

            The ARN for the candidate's step.

          • CandidateStepName (string) --

            The name for the candidate's step.

      • CandidateStatus (string) --

        The candidate's status.

      • InferenceContainers (list) --

        Information about the recommended inference container definitions.

        • (dict) --

          A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .

          • Image (string) --

            The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .

          • ModelDataUrl (string) --

            The location of the model artifacts. For more information, see ContainerDefinition .

          • Environment (dict) --

            The environment variables to set in the container. For more information, see ContainerDefinition .

            • (string) --

              • (string) --

      • CreationTime (datetime) --

        The creation time.

      • EndTime (datetime) --

        The end time.

      • LastModifiedTime (datetime) --

        The last modified time.

      • FailureReason (string) --

        The failure reason.

      • CandidateProperties (dict) --

        The properties of an AutoML candidate job.

        • CandidateArtifactLocations (dict) --

          The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

          • Explainability (string) --

            The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

          • ModelInsights (string) --

            The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.

          • BacktestResults (string) --

            The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.

        • CandidateMetrics (list) --

          Information about the candidate metrics for an AutoML job.

          • (dict) --

            Information about the metric for a candidate produced by an AutoML job.

      • InferenceContainerDefinitions (dict) --

        The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2 ) related to image or text classification problem types only.

        • (string) --

          Processing unit for an inference container. Currently Autopilot only supports CPU or GPU .

          • (list) --

            Information about the recommended inference container definitions.

            • (dict) --

              A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .

              • Image (string) --

                The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .

              • ModelDataUrl (string) --

                The location of the model artifacts. For more information, see ContainerDefinition .

              • Environment (dict) --

                The environment variables to set in the container. For more information, see ContainerDefinition .

                • (string) --

                  • (string) --

    • AutoMLJobStatus (string) --

      Returns the status of the AutoML job V2.

    • AutoMLJobSecondaryStatus (string) --

      Returns the secondary status of the AutoML job V2.

    • ModelDeployConfig (dict) --

      Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

      • AutoGenerateEndpointName (boolean) --

        Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

        Note

        If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

      • EndpointName (string) --

        Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

        Note

        Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

    • ModelDeployResult (dict) --

      Provides information about endpoint for the model deployment.

      • EndpointName (string) --

        The name of the endpoint to which the model has been deployed.

        Note

        If model deployment fails, this field is omitted from the response.

    • DataSplitConfig (dict) --

      Returns the configuration settings of how the data are split into train and validation datasets.

      • ValidationFraction (float) --

        The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

    • SecurityConfig (dict) --

      Returns the security configuration for traffic encryption or Amazon VPC settings.

      • VolumeKmsKeyId (string) --

        The key used to encrypt stored data.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to use traffic encryption between the container layers.

      • VpcConfig (dict) --

        The VPC configuration.

        • 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) --

    • AutoMLJobArtifacts (dict) --

      The artifacts that are generated during an AutoML job.

      • CandidateDefinitionNotebookLocation (string) --

        The URL of the notebook location.

      • DataExplorationNotebookLocation (string) --

        The URL of the notebook location.

    • ResolvedAttributes (dict) --

      Returns the resolved attributes used by the AutoML job V2.

      • AutoMLJobObjective (dict) --

        Specifies a metric to minimize or maximize as the objective of an AutoML job.

        • MetricName (string) --

          The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

          The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

          • For tabular problem types:

            • List of available metrics:

              • Regression: InferenceLatency , MAE , MSE , R2 , RMSE

              • Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall

              • Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro

            For a description of each metric, see Autopilot metrics for classification and regression .

            • Default objective metrics:

              • Regression: MSE .

              • Binary classification: F1 .

              • Multiclass classification: Accuracy .

          • For image or text classification problem types:

          • For time-series forecasting problem types:

          • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

      • CompletionCriteria (dict) --

        How long a job is allowed to run, or how many candidates a job is allowed to generate.

        • MaxCandidates (integer) --

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds (integer) --

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds (integer) --

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • AutoMLProblemTypeResolvedAttributes (dict) --

        Defines the resolved attributes specific to a problem type.

        • TabularResolvedAttributes (dict) --

          The resolved attributes for the tabular problem type.

          • ProblemType (string) --

            The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see Amazon SageMaker Autopilot problem types .

        • TextGenerationResolvedAttributes (dict) --

          The resolved attributes for the text generation problem type.

          • BaseModelName (string) --

            The name of the base model to fine-tune.

    • AutoMLProblemTypeConfigName (string) --

      Returns the name of the problem type configuration set for the AutoML job V2.

DescribeDomain (updated) Link ¶
Changes (response)
{'DefaultSpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                          'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}},
 'DefaultUserSettings': {'DefaultLandingUri': 'string',
                         'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                         'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                         'RSessionAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                         'StudioWebPortal': 'ENABLED | DISABLED',
                         'TensorBoardAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}},
 'DomainSettings': {'RStudioServerProDomainSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}}}

The description of the domain.

See also: AWS API Documentation

Request Syntax

client.describe_domain(
    DomainId='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

rtype

dict

returns

Response Syntax

{
    'DomainArn': 'string',
    'DomainId': 'string',
    'DomainName': 'string',
    'HomeEfsFileSystemId': 'string',
    'SingleSignOnManagedApplicationInstanceId': 'string',
    'SingleSignOnApplicationArn': 'string',
    'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'AuthMode': 'SSO'|'IAM',
    'DefaultUserSettings': {
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'SharingSettings': {
            'NotebookOutputOption': 'Allowed'|'Disabled',
            'S3OutputPath': 'string',
            'S3KmsKeyId': 'string'
        },
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        },
        'TensorBoardAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'RStudioServerProAppSettings': {
            'AccessStatus': 'ENABLED'|'DISABLED',
            'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
        },
        'RSessionAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ]
        },
        'CanvasAppSettings': {
            'TimeSeriesForecastingSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'AmazonForecastRoleArn': 'string'
            },
            'ModelRegisterSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'CrossAccountModelRegisterRoleArn': 'string'
            },
            'WorkspaceSettings': {
                'S3ArtifactPath': 'string',
                'S3KmsKeyId': 'string'
            },
            'IdentityProviderOAuthSettings': [
                {
                    'DataSourceName': 'SalesforceGenie'|'Snowflake',
                    'Status': 'ENABLED'|'DISABLED',
                    'SecretArn': 'string'
                },
            ],
            'KendraSettings': {
                'Status': 'ENABLED'|'DISABLED'
            },
            'DirectDeploySettings': {
                'Status': 'ENABLED'|'DISABLED'
            }
        },
        'DefaultLandingUri': 'string',
        'StudioWebPortal': 'ENABLED'|'DISABLED'
    },
    'AppNetworkAccessType': 'PublicInternetOnly'|'VpcOnly',
    'HomeEfsFileSystemKmsKeyId': 'string',
    'SubnetIds': [
        'string',
    ],
    'Url': 'string',
    'VpcId': 'string',
    'KmsKeyId': 'string',
    'DomainSettings': {
        'SecurityGroupIds': [
            'string',
        ],
        'RStudioServerProDomainSettings': {
            'DomainExecutionRoleArn': 'string',
            'RStudioConnectUrl': 'string',
            'RStudioPackageManagerUrl': 'string',
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED'
    },
    'AppSecurityGroupManagement': 'Service'|'Customer',
    'SecurityGroupIdForDomainBoundary': 'string',
    'DefaultSpaceSettings': {
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        }
    }
}

Response Structure

  • (dict) --

    • DomainArn (string) --

      The domain's Amazon Resource Name (ARN).

    • DomainId (string) --

      The domain ID.

    • DomainName (string) --

      The domain name.

    • HomeEfsFileSystemId (string) --

      The ID of the Amazon Elastic File System (EFS) managed by this Domain.

    • SingleSignOnManagedApplicationInstanceId (string) --

      The IAM Identity Center managed application instance ID.

    • SingleSignOnApplicationArn (string) --

      The ARN of the application managed by SageMaker in IAM Identity Center. This value is only returned for domains created after September 19, 2023.

    • Status (string) --

      The status.

    • CreationTime (datetime) --

      The creation time.

    • LastModifiedTime (datetime) --

      The last modified time.

    • FailureReason (string) --

      The failure reason.

    • AuthMode (string) --

      The domain's authentication mode.

    • DefaultUserSettings (dict) --

      Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.

      • ExecutionRole (string) --

        The execution role for the user.

      • SecurityGroups (list) --

        The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

        Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .

        Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.

        Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.

        • (string) --

      • SharingSettings (dict) --

        Specifies options for sharing Amazon SageMaker Studio notebooks.

        • NotebookOutputOption (string) --

          Whether to include the notebook cell output when sharing the notebook. The default is Disabled .

        • S3OutputPath (string) --

          When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.

        • S3KmsKeyId (string) --

          When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.

      • JupyterServerAppSettings (dict) --

        The Jupyter server's app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

        • CodeRepositories (list) --

          A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

          • (dict) --

            A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

            • RepositoryUrl (string) --

              The URL of the Git repository.

      • KernelGatewayAppSettings (dict) --

        The kernel gateway app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

          Note

          The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • CustomImages (list) --

          A list of custom SageMaker images that are configured to run as a KernelGateway app.

          • (dict) --

            A custom SageMaker image. For more information, see Bring your own SageMaker image .

            • ImageName (string) --

              The name of the CustomImage. Must be unique to your account.

            • ImageVersionNumber (integer) --

              The version number of the CustomImage.

            • AppImageConfigName (string) --

              The name of the AppImageConfig.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

      • TensorBoardAppSettings (dict) --

        The TensorBoard app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • RStudioServerProAppSettings (dict) --

        A collection of settings that configure user interaction with the RStudioServerPro app.

        • AccessStatus (string) --

          Indicates whether the current user has access to the RStudioServerPro app.

        • UserGroup (string) --

          The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.

      • RSessionAppSettings (dict) --

        A collection of settings that configure the RSessionGateway app.

        • DefaultResourceSpec (dict) --

          Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • CustomImages (list) --

          A list of custom SageMaker images that are configured to run as a RSession app.

          • (dict) --

            A custom SageMaker image. For more information, see Bring your own SageMaker image .

            • ImageName (string) --

              The name of the CustomImage. Must be unique to your account.

            • ImageVersionNumber (integer) --

              The version number of the CustomImage.

            • AppImageConfigName (string) --

              The name of the AppImageConfig.

      • CanvasAppSettings (dict) --

        The Canvas app settings.

        • TimeSeriesForecastingSettings (dict) --

          Time series forecast settings for the SageMaker Canvas application.

          • Status (string) --

            Describes whether time series forecasting is enabled or disabled in the Canvas application.

          • AmazonForecastRoleArn (string) --

            The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.

        • ModelRegisterSettings (dict) --

          The model registry settings for the SageMaker Canvas application.

          • Status (string) --

            Describes whether the integration to the model registry is enabled or disabled in the Canvas application.

          • CrossAccountModelRegisterRoleArn (string) --

            The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.

        • WorkspaceSettings (dict) --

          The workspace settings for the SageMaker Canvas application.

          • S3ArtifactPath (string) --

            The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.

          • S3KmsKeyId (string) --

            The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.

        • IdentityProviderOAuthSettings (list) --

          The settings for connecting to an external data source with OAuth.

          • (dict) --

            The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

            • DataSourceName (string) --

              The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.

            • Status (string) --

              Describes whether OAuth for a data source is enabled or disabled in the Canvas application.

            • SecretArn (string) --

              The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.

        • KendraSettings (dict) --

          The settings for document querying.

          • Status (string) --

            Describes whether the document querying feature is enabled or disabled in the Canvas application.

        • DirectDeploySettings (dict) --

          The model deployment settings for the SageMaker Canvas application.

          • Status (string) --

            Describes whether model deployment permissions are enabled or disabled in the Canvas application.

      • DefaultLandingUri (string) --

        The default experience that the user is directed to when accessing the domain. The supported values are:

        • studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .

        • app:JupyterServer: : Indicates that Studio Classic is the default experience.

      • StudioWebPortal (string) --

        Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.

    • AppNetworkAccessType (string) --

      Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .

      • PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access

      • VpcOnly - All traffic is through the specified VPC and subnets

    • HomeEfsFileSystemKmsKeyId (string) --

      Use KmsKeyId .

    • SubnetIds (list) --

      The VPC subnets that the domain uses for communication.

      • (string) --

    • Url (string) --

      The domain's URL.

    • VpcId (string) --

      The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

    • KmsKeyId (string) --

      The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.

    • DomainSettings (dict) --

      A collection of Domain settings.

      • SecurityGroupIds (list) --

        The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

        • (string) --

      • RStudioServerProDomainSettings (dict) --

        A collection of settings that configure the RStudioServerPro Domain-level app.

        • DomainExecutionRoleArn (string) --

          The ARN of the execution role for the RStudioServerPro Domain-level app.

        • RStudioConnectUrl (string) --

          A URL pointing to an RStudio Connect server.

        • RStudioPackageManagerUrl (string) --

          A URL pointing to an RStudio Package Manager server.

        • DefaultResourceSpec (dict) --

          Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • ExecutionRoleIdentityConfig (string) --

        The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key .

    • AppSecurityGroupManagement (string) --

      The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.

    • SecurityGroupIdForDomainBoundary (string) --

      The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.

    • DefaultSpaceSettings (dict) --

      The default settings used to create a space.

      • ExecutionRole (string) --

        The ARN of the execution role for the space.

      • SecurityGroups (list) --

        The security group IDs for the Amazon Virtual Private Cloud that the space uses for communication.

        • (string) --

      • JupyterServerAppSettings (dict) --

        The JupyterServer app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

        • CodeRepositories (list) --

          A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

          • (dict) --

            A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

            • RepositoryUrl (string) --

              The URL of the Git repository.

      • KernelGatewayAppSettings (dict) --

        The KernelGateway app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

          Note

          The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • CustomImages (list) --

          A list of custom SageMaker images that are configured to run as a KernelGateway app.

          • (dict) --

            A custom SageMaker image. For more information, see Bring your own SageMaker image .

            • ImageName (string) --

              The name of the CustomImage. Must be unique to your account.

            • ImageVersionNumber (integer) --

              The version number of the CustomImage.

            • AppImageConfigName (string) --

              The name of the AppImageConfig.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

DescribeEndpoint (updated) Link ¶
Changes (response)
{'PendingDeploymentSummary': {'ProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                                                'MinInstanceCount': 'integer',
                                                                                'Status': 'ENABLED '
                                                                                          '| '
                                                                                          'DISABLED'},
                                                     'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                                                          '| '
                                                                                          'RANDOM'}},
                              'ShadowProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                                                      'MinInstanceCount': 'integer',
                                                                                      'Status': 'ENABLED '
                                                                                                '| '
                                                                                                'DISABLED'},
                                                           'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                                                                '| '
                                                                                                'RANDOM'}}},
 'ProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                   'MinInstanceCount': 'integer',
                                                   'Status': 'ENABLED | '
                                                             'DISABLED'},
                        'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                             '| RANDOM'}},
 'ShadowProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                         'MinInstanceCount': 'integer',
                                                         'Status': 'ENABLED | '
                                                                   'DISABLED'},
                              'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                                   '| RANDOM'}}}

Returns the description of an endpoint.

See also: AWS API Documentation

Request Syntax

client.describe_endpoint(
    EndpointName='string'
)
type EndpointName

string

param EndpointName

[REQUIRED]

The name of the endpoint.

rtype

dict

returns

Response Syntax

{
    '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
            },
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    '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),
    'LastDeploymentConfig': {
        'BlueGreenUpdatePolicy': {
            'TrafficRoutingConfiguration': {
                'Type': 'ALL_AT_ONCE'|'CANARY'|'LINEAR',
                'WaitIntervalInSeconds': 123,
                'CanarySize': {
                    'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                    'Value': 123
                },
                'LinearStepSize': {
                    'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                    'Value': 123
                }
            },
            'TerminationWaitInSeconds': 123,
            'MaximumExecutionTimeoutInSeconds': 123
        },
        'AutoRollbackConfiguration': {
            'Alarms': [
                {
                    'AlarmName': 'string'
                },
            ]
        },
        'RollingUpdatePolicy': {
            'MaximumBatchSize': {
                'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                'Value': 123
            },
            'WaitIntervalInSeconds': 123,
            'MaximumExecutionTimeoutInSeconds': 123,
            'RollbackMaximumBatchSize': {
                'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                'Value': 123
            }
        }
    },
    'AsyncInferenceConfig': {
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string',
                'IncludeInferenceResponseIn': [
                    'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
                ]
            },
            'S3FailurePath': 'string'
        }
    },
    'PendingDeploymentSummary': {
        'EndpointConfigName': 'string',
        'ProductionVariants': [
            {
                'VariantName': 'string',
                'DeployedImages': [
                    {
                        'SpecifiedImage': 'string',
                        'ResolvedImage': 'string',
                        'ResolutionTime': datetime(2015, 1, 1)
                    },
                ],
                'CurrentWeight': ...,
                'DesiredWeight': ...,
                'CurrentInstanceCount': 123,
                'DesiredInstanceCount': 123,
                'InstanceType': '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'|'ml.p5.48xlarge',
                'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
                '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
                },
                'ManagedInstanceScaling': {
                    'Status': 'ENABLED'|'DISABLED',
                    'MinInstanceCount': 123,
                    'MaxInstanceCount': 123
                },
                'RoutingConfig': {
                    'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
                }
            },
        ],
        'StartTime': datetime(2015, 1, 1),
        'ShadowProductionVariants': [
            {
                'VariantName': 'string',
                'DeployedImages': [
                    {
                        'SpecifiedImage': 'string',
                        'ResolvedImage': 'string',
                        'ResolutionTime': datetime(2015, 1, 1)
                    },
                ],
                'CurrentWeight': ...,
                'DesiredWeight': ...,
                'CurrentInstanceCount': 123,
                'DesiredInstanceCount': 123,
                'InstanceType': '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'|'ml.p5.48xlarge',
                'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
                '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
                },
                'ManagedInstanceScaling': {
                    'Status': 'ENABLED'|'DISABLED',
                    'MinInstanceCount': 123,
                    'MaxInstanceCount': 123
                },
                'RoutingConfig': {
                    'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
                }
            },
        ]
    },
    'ExplainerConfig': {
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    '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
            },
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ]
}

Response Structure

  • (dict) --

    • EndpointName (string) --

      Name of the endpoint.

    • EndpointArn (string) --

      The Amazon Resource Name (ARN) of the endpoint.

    • EndpointConfigName (string) --

      The name of the endpoint configuration associated with this endpoint.

    • ProductionVariants (list) --

      An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

      • (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 .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • 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.

      • OutOfService : Endpoint is not available to take incoming requests.

      • Creating : CreateEndpoint is executing.

      • Updating : UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

      • SystemUpdating : Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

      • RollingBack : Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

      • InService : Endpoint is available to process incoming requests.

      • Deleting : DeleteEndpoint is executing.

      • Failed : Endpoint could not be created, updated, or re-scaled. Use the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

      • UpdateRollbackFailed : Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status to InService , see Rolling Deployments .

    • FailureReason (string) --

      If the status of the endpoint is Failed , the reason why it failed.

    • CreationTime (datetime) --

      A timestamp that shows when the endpoint was created.

    • LastModifiedTime (datetime) --

      A timestamp that shows when the endpoint was last modified.

    • LastDeploymentConfig (dict) --

      The most recent deployment configuration for the endpoint.

      • BlueGreenUpdatePolicy (dict) --

        Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

        • TrafficRoutingConfiguration (dict) --

          Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.

          • Type (string) --

            Traffic routing strategy type.

            • ALL_AT_ONCE : Endpoint traffic shifts to the new fleet in a single step.

            • CANARY : Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic.

            • LINEAR : Endpoint traffic shifts to the new fleet in n steps of a configurable size.

          • WaitIntervalInSeconds (integer) --

            The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.

          • CanarySize (dict) --

            Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than or equal to 50% of the variant's total instance count.

            • Type (string) --

              Specifies the endpoint capacity type.

              • INSTANCE_COUNT : The endpoint activates based on the number of instances.

              • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

            • Value (integer) --

              Defines the capacity size, either as a number of instances or a capacity percentage.

          • LinearStepSize (dict) --

            Batch size for each step to turn on traffic on the new endpoint fleet. Value must be 10-50% of the variant's total instance count.

            • Type (string) --

              Specifies the endpoint capacity type.

              • INSTANCE_COUNT : The endpoint activates based on the number of instances.

              • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

            • Value (integer) --

              Defines the capacity size, either as a number of instances or a capacity percentage.

        • TerminationWaitInSeconds (integer) --

          Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.

        • MaximumExecutionTimeoutInSeconds (integer) --

          Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds .

      • AutoRollbackConfiguration (dict) --

        Automatic rollback configuration for handling endpoint deployment failures and recovery.

        • Alarms (list) --

          List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.

          • (dict) --

            An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

            • AlarmName (string) --

              The name of a CloudWatch alarm in your account.

      • RollingUpdatePolicy (dict) --

        Specifies a rolling deployment strategy for updating a SageMaker endpoint.

        • MaximumBatchSize (dict) --

          Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.

          • Type (string) --

            Specifies the endpoint capacity type.

            • INSTANCE_COUNT : The endpoint activates based on the number of instances.

            • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

          • Value (integer) --

            Defines the capacity size, either as a number of instances or a capacity percentage.

        • WaitIntervalInSeconds (integer) --

          The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.

        • MaximumExecutionTimeoutInSeconds (integer) --

          The time limit for the total deployment. Exceeding this limit causes a timeout.

        • RollbackMaximumBatchSize (dict) --

          Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.

          • Type (string) --

            Specifies the endpoint capacity type.

            • INSTANCE_COUNT : The endpoint activates based on the number of instances.

            • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

          • Value (integer) --

            Defines the capacity size, either as a number of instances or a capacity percentage.

    • AsyncInferenceConfig (dict) --

      Returns the description of an endpoint configuration created using the ` CreateEndpointConfig https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html`__ API.

      • ClientConfig (dict) --

        Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

        • MaxConcurrentInvocationsPerInstance (integer) --

          The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

      • OutputConfig (dict) --

        Specifies the configuration for asynchronous inference invocation outputs.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

        • S3OutputPath (string) --

          The Amazon S3 location to upload inference responses to.

        • NotificationConfig (dict) --

          Specifies the configuration for notifications of inference results for asynchronous inference.

          • SuccessTopic (string) --

            Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

          • ErrorTopic (string) --

            Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

          • IncludeInferenceResponseIn (list) --

            The Amazon SNS topics where you want the inference response to be included.

            Note

            The inference response is included only if the response size is less than or equal to 128 KB.

            • (string) --

        • S3FailurePath (string) --

          The Amazon S3 location to upload failure inference responses to.

    • PendingDeploymentSummary (dict) --

      Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.

      • EndpointConfigName (string) --

        The name of the endpoint configuration used in the deployment.

      • ProductionVariants (list) --

        An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.

        • (dict) --

          The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.

          • 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 for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • CurrentInstanceCount (integer) --

            The number of instances associated with the variant.

          • DesiredInstanceCount (integer) --

            The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • InstanceType (string) --

            The type of instances associated with the variant.

          • AcceleratorType (string) --

            The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

          • 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 this deployment, as specified in the endpoint 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 .

          • ManagedInstanceScaling (dict) --

            Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

            • Status (string) --

              Indicates whether managed instance scaling is enabled.

            • MinInstanceCount (integer) --

              The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

            • MaxInstanceCount (integer) --

              The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

          • RoutingConfig (dict) --

            Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

            • RoutingStrategy (string) --

              Sets how the endpoint routes incoming traffic:

              • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

              • RANDOM : The endpoint routes each request to a randomly chosen instance.

      • StartTime (datetime) --

        The start time of the deployment.

      • ShadowProductionVariants (list) --

        An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants for the in-progress deployment.

        • (dict) --

          The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.

          • 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 for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • CurrentInstanceCount (integer) --

            The number of instances associated with the variant.

          • DesiredInstanceCount (integer) --

            The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • InstanceType (string) --

            The type of instances associated with the variant.

          • AcceleratorType (string) --

            The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

          • 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 this deployment, as specified in the endpoint 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 .

          • ManagedInstanceScaling (dict) --

            Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

            • Status (string) --

              Indicates whether managed instance scaling is enabled.

            • MinInstanceCount (integer) --

              The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

            • MaxInstanceCount (integer) --

              The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

          • RoutingConfig (dict) --

            Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

            • RoutingStrategy (string) --

              Sets how the endpoint routes incoming traffic:

              • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

              • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • ExplainerConfig (dict) --

      The configuration parameters for an explainer.

      • ClarifyExplainerConfig (dict) --

        A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

        • EnableExplanations (string) --

          A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

        • InferenceConfig (dict) --

          The inference configuration parameter for the model container.

          • FeaturesAttribute (string) --

            Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

          • ContentTemplate (string) --

            A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

          • MaxRecordCount (integer) --

            The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

          • MaxPayloadInMB (integer) --

            The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

          • ProbabilityIndex (integer) --

            A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

            Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

            Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

          • LabelIndex (integer) --

            A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

            Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .

          • ProbabilityAttribute (string) --

            A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

            Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .

          • LabelAttribute (string) --

            A JMESPath expression used to locate the list of label headers in the model container output.

            Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

          • LabelHeaders (list) --

            For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

            • (string) --

          • FeatureHeaders (list) --

            The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

          • FeatureTypes (list) --

            A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

        • ShapConfig (dict) --

          The configuration for SHAP analysis.

          • ShapBaselineConfig (dict) --

            The configuration for the SHAP baseline of the Kernal SHAP algorithm.

            • MimeType (string) --

              The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

            • ShapBaseline (string) --

              The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

            • ShapBaselineUri (string) --

              The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .

          • NumberOfSamples (integer) --

            The number of samples to be used for analysis by the Kernal SHAP algorithm.

            Note

            The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .

          • UseLogit (boolean) --

            A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

          • Seed (integer) --

            The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

          • TextConfig (dict) --

            A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

            • Language (string) --

              Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

              Note

              For a mix of multiple languages, use code 'xx' .

            • Granularity (string) --

              The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

    • ShadowProductionVariants (list) --

      An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .

      • (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 .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

DescribeEndpointConfig (updated) Link ¶
Changes (response)
{'EnableNetworkIsolation': 'boolean',
 'ExecutionRoleArn': 'string',
 'ProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                   'MinInstanceCount': 'integer',
                                                   'Status': 'ENABLED | '
                                                             'DISABLED'},
                        'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                             '| RANDOM'}},
 'ShadowProductionVariants': {'ManagedInstanceScaling': {'MaxInstanceCount': 'integer',
                                                         'MinInstanceCount': 'integer',
                                                         'Status': 'ENABLED | '
                                                                   'DISABLED'},
                              'RoutingConfig': {'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS '
                                                                   '| RANDOM'}},
 'VpcConfig': {'SecurityGroupIds': ['string'], 'Subnets': ['string']}}

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

See also: AWS API Documentation

Request Syntax

client.describe_endpoint_config(
    EndpointConfigName='string'
)
type EndpointConfigName

string

param EndpointConfigName

[REQUIRED]

The name of the endpoint configuration.

rtype

dict

returns

Response Syntax

{
    'EndpointConfigName': 'string',
    'EndpointConfigArn': 'string',
    'ProductionVariants': [
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': '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'|'ml.p5.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    'DataCaptureConfig': {
        'EnableCapture': True|False,
        'InitialSamplingPercentage': 123,
        'DestinationS3Uri': 'string',
        'KmsKeyId': 'string',
        'CaptureOptions': [
            {
                'CaptureMode': 'Input'|'Output'
            },
        ],
        'CaptureContentTypeHeader': {
            'CsvContentTypes': [
                'string',
            ],
            'JsonContentTypes': [
                'string',
            ]
        }
    },
    'KmsKeyId': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'AsyncInferenceConfig': {
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string',
                'IncludeInferenceResponseIn': [
                    'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
                ]
            },
            'S3FailurePath': 'string'
        }
    },
    'ExplainerConfig': {
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    'ShadowProductionVariants': [
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': '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'|'ml.p5.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    'ExecutionRoleArn': 'string',
    'VpcConfig': {
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    'EnableNetworkIsolation': True|False
}

Response Structure

  • (dict) --

    • EndpointConfigName (string) --

      Name of the SageMaker endpoint configuration.

    • EndpointConfigArn (string) --

      The Amazon Resource Name (ARN) of the endpoint configuration.

    • ProductionVariants (list) --

      An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

      • (dict) --

        Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

        • VariantName (string) --

          The name of the production variant.

        • ModelName (string) --

          The name of the model that you want to host. This is the name that you specified when creating the model.

        • InitialInstanceCount (integer) --

          Number of instances to launch initially.

        • InstanceType (string) --

          The ML compute instance type.

        • InitialVariantWeight (float) --

          Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

        • AcceleratorType (string) --

          The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

        • CoreDumpConfig (dict) --

          Specifies configuration for a core dump from the model container when the process crashes.

          • DestinationS3Uri (string) --

            The Amazon S3 bucket to send the core dump to.

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data 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 CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • ServerlessConfig (dict) --

          The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

          • 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 .

        • VolumeSizeInGB (integer) --

          The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

        • ModelDataDownloadTimeoutInSeconds (integer) --

          The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

        • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

          The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

        • EnableSSMAccess (boolean) --

          You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • DataCaptureConfig (dict) --

      Configuration to control how SageMaker captures inference data.

      • EnableCapture (boolean) --

        Whether data capture should be enabled or disabled (defaults to enabled).

      • InitialSamplingPercentage (integer) --

        The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.

      • DestinationS3Uri (string) --

        The Amazon S3 location used to capture the data.

      • KmsKeyId (string) --

        The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data 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

      • CaptureOptions (list) --

        Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

        • (dict) --

          Specifies data Model Monitor will capture.

          • CaptureMode (string) --

            Specify the boundary of data to capture.

      • CaptureContentTypeHeader (dict) --

        Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.

        • CsvContentTypes (list) --

          The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.

          • (string) --

        • JsonContentTypes (list) --

          The list of all content type headers that SageMaker will treat as JSON and capture accordingly.

          • (string) --

    • KmsKeyId (string) --

      Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

    • CreationTime (datetime) --

      A timestamp that shows when the endpoint configuration was created.

    • AsyncInferenceConfig (dict) --

      Returns the description of an endpoint configuration created using the ` CreateEndpointConfig https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html`__ API.

      • ClientConfig (dict) --

        Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

        • MaxConcurrentInvocationsPerInstance (integer) --

          The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

      • OutputConfig (dict) --

        Specifies the configuration for asynchronous inference invocation outputs.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

        • S3OutputPath (string) --

          The Amazon S3 location to upload inference responses to.

        • NotificationConfig (dict) --

          Specifies the configuration for notifications of inference results for asynchronous inference.

          • SuccessTopic (string) --

            Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

          • ErrorTopic (string) --

            Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

          • IncludeInferenceResponseIn (list) --

            The Amazon SNS topics where you want the inference response to be included.

            Note

            The inference response is included only if the response size is less than or equal to 128 KB.

            • (string) --

        • S3FailurePath (string) --

          The Amazon S3 location to upload failure inference responses to.

    • ExplainerConfig (dict) --

      The configuration parameters for an explainer.

      • ClarifyExplainerConfig (dict) --

        A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

        • EnableExplanations (string) --

          A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

        • InferenceConfig (dict) --

          The inference configuration parameter for the model container.

          • FeaturesAttribute (string) --

            Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

          • ContentTemplate (string) --

            A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

          • MaxRecordCount (integer) --

            The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

          • MaxPayloadInMB (integer) --

            The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

          • ProbabilityIndex (integer) --

            A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

            Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

            Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

          • LabelIndex (integer) --

            A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

            Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .

          • ProbabilityAttribute (string) --

            A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

            Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .

          • LabelAttribute (string) --

            A JMESPath expression used to locate the list of label headers in the model container output.

            Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

          • LabelHeaders (list) --

            For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

            • (string) --

          • FeatureHeaders (list) --

            The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

          • FeatureTypes (list) --

            A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

        • ShapConfig (dict) --

          The configuration for SHAP analysis.

          • ShapBaselineConfig (dict) --

            The configuration for the SHAP baseline of the Kernal SHAP algorithm.

            • MimeType (string) --

              The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

            • ShapBaseline (string) --

              The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

            • ShapBaselineUri (string) --

              The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .

          • NumberOfSamples (integer) --

            The number of samples to be used for analysis by the Kernal SHAP algorithm.

            Note

            The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .

          • UseLogit (boolean) --

            A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

          • Seed (integer) --

            The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

          • TextConfig (dict) --

            A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

            • Language (string) --

              Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

              Note

              For a mix of multiple languages, use code 'xx' .

            • Granularity (string) --

              The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

    • ShadowProductionVariants (list) --

      An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .

      • (dict) --

        Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

        • VariantName (string) --

          The name of the production variant.

        • ModelName (string) --

          The name of the model that you want to host. This is the name that you specified when creating the model.

        • InitialInstanceCount (integer) --

          Number of instances to launch initially.

        • InstanceType (string) --

          The ML compute instance type.

        • InitialVariantWeight (float) --

          Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

        • AcceleratorType (string) --

          The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

        • CoreDumpConfig (dict) --

          Specifies configuration for a core dump from the model container when the process crashes.

          • DestinationS3Uri (string) --

            The Amazon S3 bucket to send the core dump to.

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data 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 CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • ServerlessConfig (dict) --

          The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

          • 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 .

        • VolumeSizeInGB (integer) --

          The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

        • ModelDataDownloadTimeoutInSeconds (integer) --

          The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

        • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

          The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

        • EnableSSMAccess (boolean) --

          You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • ExecutionRoleArn (string) --

      The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.

    • VpcConfig (dict) --

      Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

      • 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) --

    • EnableNetworkIsolation (boolean) --

      Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

DescribeSpace (updated) Link ¶
Changes (response)
{'SpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                   'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}},
 'Url': 'string'}

Describes the space.

See also: AWS API Documentation

Request Syntax

client.describe_space(
    DomainId='string',
    SpaceName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The ID of the associated Domain.

type SpaceName

string

param SpaceName

[REQUIRED]

The name of the space.

rtype

dict

returns

Response Syntax

{
    'DomainId': 'string',
    'SpaceArn': 'string',
    'SpaceName': 'string',
    'HomeEfsFileSystemUid': 'string',
    'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
    'LastModifiedTime': datetime(2015, 1, 1),
    'CreationTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'SpaceSettings': {
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        }
    },
    'Url': 'string'
}

Response Structure

  • (dict) --

    • DomainId (string) --

      The ID of the associated Domain.

    • SpaceArn (string) --

      The space's Amazon Resource Name (ARN).

    • SpaceName (string) --

      The name of the space.

    • HomeEfsFileSystemUid (string) --

      The ID of the space's profile in the Amazon Elastic File System volume.

    • Status (string) --

      The status.

    • LastModifiedTime (datetime) --

      The last modified time.

    • CreationTime (datetime) --

      The creation time.

    • FailureReason (string) --

      The failure reason.

    • SpaceSettings (dict) --

      A collection of space settings.

      • JupyterServerAppSettings (dict) --

        The JupyterServer app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

        • CodeRepositories (list) --

          A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

          • (dict) --

            A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

            • RepositoryUrl (string) --

              The URL of the Git repository.

      • KernelGatewayAppSettings (dict) --

        The KernelGateway app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

          Note

          The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • CustomImages (list) --

          A list of custom SageMaker images that are configured to run as a KernelGateway app.

          • (dict) --

            A custom SageMaker image. For more information, see Bring your own SageMaker image .

            • ImageName (string) --

              The name of the CustomImage. Must be unique to your account.

            • ImageVersionNumber (integer) --

              The version number of the CustomImage.

            • AppImageConfigName (string) --

              The name of the AppImageConfig.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

    • Url (string) --

      Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center.

      The following application types are supported:

      • Studio Classic: &redirect=JupyterServer

      • JupyterLab: &redirect=JupyterLab

DescribeTrainingJob (updated) Link ¶
Changes (response)
{'InfraCheckConfig': {'EnableInfraCheck': 'boolean'}}

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'
)
type TrainingJobName

string

param TrainingJobName

[REQUIRED]

The name of the training job.

rtype

dict

returns

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,
        'MaxPendingTimeInSeconds': 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'
    },
    'InfraCheckConfig': {
        'EnableInfraCheck': True|False
    }
}

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:

      • 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.

      • MaxPendingTimeInSeconds (integer) --

        The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

    • 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.

    • InfraCheckConfig (dict) --

      Contains information about the infrastructure health check configuration for the training job.

      • EnableInfraCheck (boolean) --

        Enables an infrastructure health check.

DescribeUserProfile (updated) Link ¶
Changes (response)
{'UserSettings': {'DefaultLandingUri': 'string',
                  'JupyterServerAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                  'KernelGatewayAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                  'RSessionAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}},
                  'StudioWebPortal': 'ENABLED | DISABLED',
                  'TensorBoardAppSettings': {'DefaultResourceSpec': {'SageMakerImageVersionAlias': 'string'}}}}

Describes a user profile. For more information, see CreateUserProfile .

See also: AWS API Documentation

Request Syntax

client.describe_user_profile(
    DomainId='string',
    UserProfileName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

[REQUIRED]

The user profile name. This value is not case sensitive.

rtype

dict

returns

Response Syntax

{
    'DomainId': 'string',
    'UserProfileArn': 'string',
    'UserProfileName': 'string',
    'HomeEfsFileSystemUid': 'string',
    'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
    'LastModifiedTime': datetime(2015, 1, 1),
    'CreationTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'SingleSignOnUserIdentifier': 'string',
    'SingleSignOnUserValue': 'string',
    'UserSettings': {
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'SharingSettings': {
            'NotebookOutputOption': 'Allowed'|'Disabled',
            'S3OutputPath': 'string',
            'S3KmsKeyId': 'string'
        },
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        },
        'TensorBoardAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'RStudioServerProAppSettings': {
            'AccessStatus': 'ENABLED'|'DISABLED',
            'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
        },
        'RSessionAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'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.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ]
        },
        'CanvasAppSettings': {
            'TimeSeriesForecastingSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'AmazonForecastRoleArn': 'string'
            },
            'ModelRegisterSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'CrossAccountModelRegisterRoleArn': 'string'
            },
            'WorkspaceSettings': {
                'S3ArtifactPath': 'string',
                'S3KmsKeyId': 'string'
            },
            'IdentityProviderOAuthSettings': [
                {
                    'DataSourceName': 'SalesforceGenie'|'Snowflake',
                    'Status': 'ENABLED'|'DISABLED',
                    'SecretArn': 'string'
                },
            ],
            'KendraSettings': {
                'Status': 'ENABLED'|'DISABLED'
            },
            'DirectDeploySettings': {
                'Status': 'ENABLED'|'DISABLED'
            }
        },
        'DefaultLandingUri': 'string',
        'StudioWebPortal': 'ENABLED'|'DISABLED'
    }
}

Response Structure

  • (dict) --

    • DomainId (string) --

      The ID of the domain that contains the profile.

    • UserProfileArn (string) --

      The user profile Amazon Resource Name (ARN).

    • UserProfileName (string) --

      The user profile name.

    • HomeEfsFileSystemUid (string) --

      The ID of the user's profile in the Amazon Elastic File System (EFS) volume.

    • Status (string) --

      The status.

    • LastModifiedTime (datetime) --

      The last modified time.

    • CreationTime (datetime) --

      The creation time.

    • FailureReason (string) --

      The failure reason.

    • SingleSignOnUserIdentifier (string) --

      The IAM Identity Center user identifier.

    • SingleSignOnUserValue (string) --

      The IAM Identity Center user value.

    • UserSettings (dict) --

      A collection of settings.

      • ExecutionRole (string) --

        The execution role for the user.

      • SecurityGroups (list) --

        The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

        Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .

        Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.

        Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.

        • (string) --

      • SharingSettings (dict) --

        Specifies options for sharing Amazon SageMaker Studio notebooks.

        • NotebookOutputOption (string) --

          Whether to include the notebook cell output when sharing the notebook. The default is Disabled .

        • S3OutputPath (string) --

          When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.

        • S3KmsKeyId (string) --

          When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.

      • JupyterServerAppSettings (dict) --

        The Jupyter server's app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

        • CodeRepositories (list) --

          A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

          • (dict) --

            A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

            • RepositoryUrl (string) --

              The URL of the Git repository.

      • KernelGatewayAppSettings (dict) --

        The kernel gateway app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

          Note

          The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • CustomImages (list) --

          A list of custom SageMaker images that are configured to run as a KernelGateway app.

          • (dict) --

            A custom SageMaker image. For more information, see Bring your own SageMaker image .

            • ImageName (string) --

              The name of the CustomImage. Must be unique to your account.

            • ImageVersionNumber (integer) --

              The version number of the CustomImage.

            • AppImageConfigName (string) --

              The name of the AppImageConfig.

        • LifecycleConfigArns (list) --

          The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

          Note

          To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

          • (string) --

      • TensorBoardAppSettings (dict) --

        The TensorBoard app settings.

        • DefaultResourceSpec (dict) --

          The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • RStudioServerProAppSettings (dict) --

        A collection of settings that configure user interaction with the RStudioServerPro app.

        • AccessStatus (string) --

          Indicates whether the current user has access to the RStudioServerPro app.

        • UserGroup (string) --

          The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.

      • RSessionAppSettings (dict) --

        A collection of settings that configure the RSessionGateway app.

        • DefaultResourceSpec (dict) --

          Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

          • SageMakerImageArn (string) --

            The ARN of the SageMaker image that the image version belongs to.

          • SageMakerImageVersionArn (string) --

            The ARN of the image version created on the instance.

          • SageMakerImageVersionAlias (string) --

            The SageMakerImageVersionAlias.

          • InstanceType (string) --

            The instance type that the image version runs on.

            Note

            JupyterServer apps only support the system value.

            For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

          • LifecycleConfigArn (string) --

            The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

        • CustomImages (list) --

          A list of custom SageMaker images that are configured to run as a RSession app.

          • (dict) --

            A custom SageMaker image. For more information, see Bring your own SageMaker image .

            • ImageName (string) --

              The name of the CustomImage. Must be unique to your account.

            • ImageVersionNumber (integer) --

              The version number of the CustomImage.

            • AppImageConfigName (string) --

              The name of the AppImageConfig.

      • CanvasAppSettings (dict) --

        The Canvas app settings.

        • TimeSeriesForecastingSettings (dict) --

          Time series forecast settings for the SageMaker Canvas application.

          • Status (string) --

            Describes whether time series forecasting is enabled or disabled in the Canvas application.

          • AmazonForecastRoleArn (string) --

            The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.

        • ModelRegisterSettings (dict) --

          The model registry settings for the SageMaker Canvas application.

          • Status (string) --

            Describes whether the integration to the model registry is enabled or disabled in the Canvas application.

          • CrossAccountModelRegisterRoleArn (string) --

            The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.

        • WorkspaceSettings (dict) --

          The workspace settings for the SageMaker Canvas application.

          • S3ArtifactPath (string) --

            The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.

          • S3KmsKeyId (string) --

            The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.

        • IdentityProviderOAuthSettings (list) --

          The settings for connecting to an external data source with OAuth.

          • (dict) --

            The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

            • DataSourceName (string) --

              The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.

            • Status (string) --

              Describes whether OAuth for a data source is enabled or disabled in the Canvas application.

            • SecretArn (string) --

              The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.

        • KendraSettings (dict) --

          The settings for document querying.

          • Status (string) --

            Describes whether the document querying feature is enabled or disabled in the Canvas application.

        • DirectDeploySettings (dict) --

          The model deployment settings for the SageMaker Canvas application.

          • Status (string) --

            Describes whether model deployment permissions are enabled or disabled in the Canvas application.

      • DefaultLandingUri (string) --

        The default experience that the user is directed to when accessing the domain. The supported values are:

        • studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .

        • app:JupyterServer: : Indicates that Studio Classic is the default experience.

      • StudioWebPortal (string) --

        Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.