2020/12/01 - Amazon SageMaker Service - 50 new 19 updated api methods
Changes Amazon SageMaker Pipelines for ML workflows. Amazon SageMaker Feature Store, a fully managed repository for ML features.
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
See also: AWS API Documentation
Request Syntax
client.enable_sagemaker_servicecatalog_portfolio()
dict
Response Syntax
{}
Response Structure
(dict) --
Use this operation to describe a FeatureGroup . The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup , and more.
See also: AWS API Documentation
Request Syntax
client.describe_feature_group(
FeatureGroupName='string',
NextToken='string'
)
string
[REQUIRED]
The name of the FeatureGroup you want described.
string
A token to resume pagination of the list of Features (FeatureDefinitions ). 2,500 Features are returned by default.
dict
Response Syntax
{
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
'CreationTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'NextToken': 'string'
}
Response Structure
(dict) --
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of the FeatureGroup .
FeatureGroupName (string) --
he name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature used for RecordIdentifier , whose value uniquely identifies a record stored in the feature store.
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup have a corresponding EventTime .
FeatureDefinitions (list) --
A list of the Features in the FeatureGroup . Each feature is defined by a FeatureName and FeatureType .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CreationTime (datetime) --
A timestamp indicating when SageMaker created the FeatureGroup .
OnlineStoreConfig (dict) --
The configuration for the OnlineStore .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The ID of the AWS Key Management Service (AWS KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
OfflineStoreConfig (dict) --
The configuration of the OfflineStore , inducing the S3 location of the OfflineStore , AWS Glue or AWS Hive data catalogue configurations, and the security configuration.
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The AWS Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an AWS Glue table when configuring an OfflineStore .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
FeatureGroupStatus (string) --
The status of the feature group.
OfflineStoreStatus (dict) --
The status of the OfflineStore . Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure can occur because:
The FeatureGroup could not be created in the OfflineStore .
The FeatureGroup could not be deleted from the OfflineStore .
Description (string) --
A free form description of the feature group.
NextToken (string) --
A token to resume pagination of the list of Features (FeatureDefinitions ).
Lists the associations in your account and their properties.
See also: AWS API Documentation
Request Syntax
client.list_associations(
SourceArn='string',
DestinationArn='string',
SourceType='string',
DestinationType='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='SourceArn'|'DestinationArn'|'SourceType'|'DestinationType'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
A filter that returns only associations with the specified source ARN.
string
A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
string
A filter that returns only associations with the specified source type.
string
A filter that returns only associations with the specified destination type.
string
A filter that returns only associations of the specified type.
datetime
A filter that returns only associations created on or after the specified time.
datetime
A filter that returns only associations created on or before the specified time.
string
The property used to sort results. The default value is CreationTime .
string
The sort order. The default value is Descending .
string
If the previous call to ListAssociations didn't return the full set of associations, the call returns a token for getting the next set of associations.
integer
The maximum number of associations to return in the response. The default value is 10.
dict
Response Syntax
{
'AssociationSummaries': [
{
'SourceArn': 'string',
'DestinationArn': 'string',
'SourceType': 'string',
'DestinationType': 'string',
'AssociationType': 'ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced',
'SourceName': 'string',
'DestinationName': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
AssociationSummaries (list) --
A list of associations and their properties.
(dict) --
Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
SourceType (string) --
The source type.
DestinationType (string) --
The destination type.
AssociationType (string) --
The type of the association.
SourceName (string) --
The name of the source.
DestinationName (string) --
The name of the destination.
CreationTime (datetime) --
When the association was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
NextToken (string) --
A token for getting the next set of associations, if there are any.
Gets a list of pipelines.
See also: AWS API Documentation
Request Syntax
client.list_pipelines(
PipelineNamePrefix='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
The prefix of the pipeline name.
datetime
A filter that returns the pipelines that were created after a specified time.
datetime
A filter that returns the pipelines that were created before a specified time.
string
The field by which to sort results. The default is CreatedTime .
string
The sort order for results.
string
If the result of the previous ListPipelines request was truncated, the response includes a NextToken . To retrieve the next set of pipelines, use the token in the next request.
integer
The maximum number of pipelines to return in the response.
dict
Response Syntax
{
'PipelineSummaries': [
{
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastExecutionTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineSummaries (list) --
Contains a sorted list of PipelineSummary objects matching the specified filters. Each PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.
(dict) --
A summary of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) that the pipeline used to execute.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastExecutionTime (datetime) --
The last time that a pipeline execution began.
NextToken (string) --
If the result of the previous ListPipelines request was truncated, the response includes a NextToken . To retrieve the next set of pipelines, use the token in the next request.
Gets a description for the specified model group.
See also: AWS API Documentation
Request Syntax
client.describe_model_package_group(
ModelPackageGroupName='string'
)
string
[REQUIRED]
The name of the model group to describe.
dict
Response Syntax
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed'
}
Response Structure
(dict) --
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
A description of the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ModelPackageGroupStatus (string) --
The status of the model group.
Deletes an association.
See also: AWS API Documentation
Request Syntax
client.delete_association(
SourceArn='string',
DestinationArn='string'
)
string
[REQUIRED]
The ARN of the source.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the destination.
dict
Response Syntax
{
'SourceArn': 'string',
'DestinationArn': 'string'
}
Response Structure
(dict) --
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
Deletes an context.
See also: AWS API Documentation
Request Syntax
client.delete_context(
ContextName='string'
)
string
[REQUIRED]
The name of the context to delete.
dict
Response Syntax
{
'ContextArn': 'string'
}
Response Structure
(dict) --
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
See also: AWS API Documentation
Request Syntax
client.disable_sagemaker_servicecatalog_portfolio()
dict
Response Syntax
{}
Response Structure
(dict) --
Describes an action.
See also: AWS API Documentation
Request Syntax
client.describe_action(
ActionName='string'
)
string
[REQUIRED]
The name of the action to describe.
dict
Response Syntax
{
'ActionName': 'string',
'ActionArn': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ActionType': 'string',
'Description': 'string',
'Status': 'Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Properties': {
'string': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
}
}
Response Structure
(dict) --
ActionName (string) --
The name of the action.
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
Source (dict) --
The source of the action.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ActionType (string) --
The type of the action.
Description (string) --
The description of the action.
Status (string) --
The status of the action.
Properties (dict) --
A list of the action's properties.
(string) --
(string) --
CreationTime (datetime) --
When the action was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the action was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Describes the details of an execution's pipeline definition.
See also: AWS API Documentation
Request Syntax
client.describe_pipeline_definition_for_execution(
PipelineExecutionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
dict
Response Syntax
{
'PipelineDefinition': 'string',
'CreationTime': datetime(2015, 1, 1)
}
Response Structure
(dict) --
PipelineDefinition (string) --
The JSON pipeline definition.
CreationTime (datetime) --
The time when the pipeline was created.
Describes the details of a pipeline execution.
See also: AWS API Documentation
Request Syntax
client.describe_pipeline_execution(
PipelineExecutionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
dict
Response Syntax
{
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline execution.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
CreationTime (datetime) --
The time when the pipeline execution was created.
LastModifiedTime (datetime) --
The time when the pipeline execution was modified last.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Gets a list of the pipeline executions.
See also: AWS API Documentation
Request Syntax
client.list_pipeline_executions(
PipelineName='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='CreationTime'|'PipelineExecutionArn',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
[REQUIRED]
The name of the pipeline.
datetime
A filter that returns the pipeline executions that were created after a specified time.
datetime
A filter that returns the pipeline executions that were created before a specified time.
string
The field by which to sort results. The default is CreatedTime .
string
The sort order for results.
string
If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken . To retrieve the next set of pipeline executions, use the token in the next request.
integer
The maximum number of pipeline executions to return in the response.
dict
Response Syntax
{
'PipelineExecutionSummaries': [
{
'PipelineExecutionArn': 'string',
'StartTime': datetime(2015, 1, 1),
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExecutionDisplayName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineExecutionSummaries (list) --
Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.
(dict) --
A pipeline execution summary.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
StartTime (datetime) --
The start time of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline execution.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
NextToken (string) --
If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken . To retrieve the next set of pipeline executions, use the token in the next request.
Starts a pipeline execution.
See also: AWS API Documentation
Request Syntax
client.start_pipeline_execution(
PipelineName='string',
PipelineExecutionDisplayName='string',
PipelineParameters=[
{
'Name': 'string',
'Value': 'string'
},
],
PipelineExecutionDescription='string',
ClientRequestToken='string'
)
string
[REQUIRED]
The name of the pipeline.
string
The display name of the pipeline execution.
list
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) -- [REQUIRED]
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) -- [REQUIRED]
The literal value for the parameter.
string
The description of the pipeline execution.
string
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
Describes the details of a project.
See also: AWS API Documentation
Request Syntax
client.describe_project(
ProjectName='string'
)
string
[REQUIRED]
The name of the project to describe.
dict
Response Syntax
{
'ProjectArn': 'string',
'ProjectName': 'string',
'ProjectId': 'string',
'ProjectDescription': 'string',
'ServiceCatalogProvisioningDetails': {
'ProductId': 'string',
'ProvisioningArtifactId': 'string',
'PathId': 'string',
'ProvisioningParameters': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ServiceCatalogProvisionedProductDetails': {
'ProvisionedProductId': 'string',
'ProvisionedProductStatusMessage': 'string'
},
'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'CreationTime': datetime(2015, 1, 1)
}
Response Structure
(dict) --
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectName (string) --
The name of the project.
ProjectId (string) --
The ID of the project.
ProjectDescription (string) --
The description of the project.
ServiceCatalogProvisioningDetails (dict) --
Information used to provision a service catalog product. For information, see What is AWS Service Catalog .
ProductId (string) --
The ID of the product to provision.
ProvisioningArtifactId (string) --
The ID of the provisioning artifact.
PathId (string) --
The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters (list) --
A list of key value pairs that you specify when you provision a product.
(dict) --
A key value pair used when you provision a project as a service catalog product. For information, see What is AWS Service Catalog .
Key (string) --
The key that identifies a provisioning parameter.
Value (string) --
The value of the provisioning parameter.
ServiceCatalogProvisionedProductDetails (dict) --
Information about a provisioned service catalog product.
ProvisionedProductId (string) --
The ID of the provisioned product.
ProvisionedProductStatusMessage (string) --
The current status of the product.
AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.
UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.
TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.
ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.
PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.
ProjectStatus (string) --
The status of the project.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
CreationTime (datetime) --
The time when the project was created.
Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup . Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.
Data written into the OfflineStore will not be deleted. The AWS Glue database and tables that are automatically created for your OfflineStore are not deleted.
See also: AWS API Documentation
Request Syntax
client.delete_feature_group(
FeatureGroupName='string'
)
string
[REQUIRED]
The name of the FeatureGroup you want to delete. The name must be unique within an AWS Region in an AWS account.
None
Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the AWS Identity and Access Management User Guide. .
See also: AWS API Documentation
Request Syntax
client.put_model_package_group_policy(
ModelPackageGroupName='string',
ResourcePolicy='string'
)
string
[REQUIRED]
The name of the model group to add a resource policy to.
string
[REQUIRED]
The resource policy for the model group.
dict
Response Syntax
{
'ModelPackageGroupArn': 'string'
}
Response Structure
(dict) --
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model package group.
Stops a pipeline execution.
See also: AWS API Documentation
Request Syntax
client.stop_pipeline_execution(
PipelineExecutionArn='string',
ClientRequestToken='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
string
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
Gets a list of the model groups in your AWS account.
See also: AWS API Documentation
Request Syntax
client.list_model_package_groups(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
datetime
A filter that returns only model groups created after the specified time.
datetime
A filter that returns only model groups created before the specified time.
integer
The maximum number of results to return in the response.
string
A string in the model group name. This filter returns only model groups whose name contains the specified string.
string
If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken . To retrieve the next set of model groups, use the token in the next request.
string
The field to sort results by. The default is CreationTime .
string
The sort order for results. The default is Ascending .
dict
Response Syntax
{
'ModelPackageGroupSummaryList': [
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ModelPackageGroupSummaryList (list) --
A list of summaries of the model groups in your AWS account.
(dict) --
Summary information about a model group.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
A description of the model group.
CreationTime (datetime) --
The time that the model group was created.
ModelPackageGroupStatus (string) --
The status of the model group.
NextToken (string) --
If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
Describes an artifact.
See also: AWS API Documentation
Request Syntax
client.describe_artifact(
ArtifactArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the artifact to describe.
dict
Response Syntax
{
'ArtifactName': 'string',
'ArtifactArn': 'string',
'Source': {
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
'ArtifactType': 'string',
'Properties': {
'string': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
}
}
Response Structure
(dict) --
ArtifactName (string) --
The name of the artifact.
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
Source (dict) --
The source of the artifact.
SourceUri (string) --
The URI of the source.
SourceTypes (list) --
A list of source types.
(dict) --
The ID and ID type of an artifact source.
SourceIdType (string) --
The type of ID.
Value (string) --
The ID.
ArtifactType (string) --
The type of the artifact.
Properties (dict) --
A list of the artifact's properties.
(string) --
(string) --
CreationTime (datetime) --
When the artifact was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the artifact was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Creates a model group. A model group contains a group of model versions.
See also: AWS API Documentation
Request Syntax
client.create_model_package_group(
ModelPackageGroupName='string',
ModelPackageGroupDescription='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the model group.
string
A description for the model group.
list
A list of key value pairs associated with the model group. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'ModelPackageGroupArn': 'string'
}
Response Structure
(dict) --
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
See also: AWS API Documentation
Request Syntax
client.get_sagemaker_servicecatalog_portfolio_status()
dict
Response Syntax
{
'Status': 'Enabled'|'Disabled'
}
Response Structure
(dict) --
Status (string) --
Whether Service Catalog is enabled or disabled in SageMaker.
Updates a context.
See also: AWS API Documentation
Request Syntax
client.update_context(
ContextName='string',
Description='string',
Properties={
'string': 'string'
},
PropertiesToRemove=[
'string',
]
)
string
[REQUIRED]
The name of the context to update.
string
The new description for the context.
dict
The new list of properties. Overwrites the current property list.
(string) --
(string) --
list
A list of properties to remove.
(string) --
dict
Response Syntax
{
'ContextArn': 'string'
}
Response Structure
(dict) --
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
See also: AWS API Documentation
Request Syntax
client.create_project(
ProjectName='string',
ProjectDescription='string',
ServiceCatalogProvisioningDetails={
'ProductId': 'string',
'ProvisioningArtifactId': 'string',
'PathId': 'string',
'ProvisioningParameters': [
{
'Key': 'string',
'Value': 'string'
},
]
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the project.
string
A description for the project.
dict
[REQUIRED]
The product ID and provisioning artifact ID to provision a service catalog. For information, see What is AWS Service Catalog .
ProductId (string) -- [REQUIRED]
The ID of the product to provision.
ProvisioningArtifactId (string) -- [REQUIRED]
The ID of the provisioning artifact.
PathId (string) --
The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters (list) --
A list of key value pairs that you specify when you provision a product.
(dict) --
A key value pair used when you provision a project as a service catalog product. For information, see What is AWS Service Catalog .
Key (string) --
The key that identifies a provisioning parameter.
Value (string) --
The value of the provisioning parameter.
list
An array of key-value pairs that you want to use to organize and track your AWS resource costs. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'ProjectArn': 'string',
'ProjectId': 'string'
}
Response Structure
(dict) --
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectId (string) --
The ID of the new project.
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking .
See also: AWS API Documentation
Request Syntax
client.add_association(
SourceArn='string',
DestinationArn='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'
)
string
[REQUIRED]
The ARN of the source.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the destination.
string
The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.
ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.
AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.
DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.
Produced - The source generated the destination. For example, a training job produced a model artifact.
dict
Response Syntax
{
'SourceArn': 'string',
'DestinationArn': 'string'
}
Response Structure
(dict) --
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
Lists the actions in your account and their properties.
See also: AWS API Documentation
Request Syntax
client.list_actions(
SourceUri='string',
ActionType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
A filter that returns only actions with the specified source URI.
string
A filter that returns only actions of the specified type.
datetime
A filter that returns only actions created on or after the specified time.
datetime
A filter that returns only actions created on or before the specified time.
string
The property used to sort results. The default value is CreationTime .
string
The sort order. The default value is Descending .
string
If the previous call to ListActions didn't return the full set of actions, the call returns a token for getting the next set of actions.
integer
The maximum number of actions to return in the response. The default value is 10.
dict
Response Syntax
{
'ActionSummaries': [
{
'ActionArn': 'string',
'ActionName': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ActionType': 'string',
'Status': 'Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ActionSummaries (list) --
A list of actions and their properties.
(dict) --
Lists the properties of an action . An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
ActionName (string) --
The name of the action.
Source (dict) --
The source of the action.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ActionType (string) --
The type of the action.
Status (string) --
The status of the action.
CreationTime (datetime) --
When the action was created.
LastModifiedTime (datetime) --
When the action was last modified.
NextToken (string) --
A token for getting the next set of actions, if there are any.
Gets a list of PipeLineExecutionStep objects.
See also: AWS API Documentation
Request Syntax
client.list_pipeline_execution_steps(
PipelineExecutionArn='string',
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending'
)
string
The Amazon Resource Name (ARN) of the pipeline execution.
string
If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken . To retrieve the next set of pipeline execution steps, use the token in the next request.
integer
The maximum number of pipeline execution steps to return in the response.
string
The field by which to sort results. The default is CreatedTime .
dict
Response Syntax
{
'PipelineExecutionSteps': [
{
'StepName': 'string',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StepStatus': 'Starting'|'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'CacheHitResult': {
'SourcePipelineExecutionArn': 'string'
},
'FailureReason': 'string',
'Metadata': {
'TrainingJob': {
'Arn': 'string'
},
'ProcessingJob': {
'Arn': 'string'
},
'TransformJob': {
'Arn': 'string'
},
'Model': {
'Arn': 'string'
},
'RegisterModel': {
'Arn': 'string'
},
'Condition': {
'Outcome': 'True'|'False'
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineExecutionSteps (list) --
A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.
(dict) --
An execution of a step in a pipeline.
StepName (string) --
The name of the step that is executed.
StartTime (datetime) --
The time that the step started executing.
EndTime (datetime) --
The time that the step stopped executing.
StepStatus (string) --
The status of the step execution.
CacheHitResult (dict) --
If this pipeline execution step was cached, details on the cache hit.
SourcePipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
FailureReason (string) --
The reason why the step failed execution. This is only returned if the step failed its execution.
Metadata (dict) --
The metadata for the step execution.
TrainingJob (dict) --
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
ProcessingJob (dict) --
The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the processing job.
TransformJob (dict) --
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
Model (dict) --
Metadata for the Model step.
Arn (string) --
The Amazon Resource Name (ARN) of the created model.
RegisterModel (dict) --
Metadata for the RegisterModel step.
Arn (string) --
The Amazon Resource Name (ARN) of the model package.
Condition (dict) --
If this is a Condition step metadata object, details on the condition.
Outcome (string) --
The outcome of the Condition step evaluation.
NextToken (string) --
If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken . To retrieve the next set of pipeline execution steps, use the token in the next request.
Creates an action . An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking .
See also: AWS API Documentation
Request Syntax
client.create_action(
ActionName='string',
Source={
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
ActionType='string',
Description='string',
Status='Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
Properties={
'string': 'string'
},
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the action. Must be unique to your account in an AWS Region.
dict
[REQUIRED]
The source type, ID, and URI.
SourceUri (string) -- [REQUIRED]
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
string
[REQUIRED]
The action type.
string
The description of the action.
string
The status of the action.
dict
A list of properties to add to the action.
(string) --
(string) --
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
list
A list of tags to apply to the action.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'ActionArn': 'string'
}
Response Structure
(dict) --
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
Creates a pipeline using a JSON pipeline definition.
See also: AWS API Documentation
Request Syntax
client.create_pipeline(
PipelineName='string',
PipelineDisplayName='string',
PipelineDefinition='string',
PipelineDescription='string',
ClientRequestToken='string',
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the pipeline.
string
The display name of the pipeline.
string
[REQUIRED]
The JSON pipeline definition of the pipeline.
string
A description of the pipeline.
string
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
list
A list of tags to apply to the created pipeline.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'PipelineArn': 'string'
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the created pipeline.
Deletes a pipeline.
See also: AWS API Documentation
Request Syntax
client.delete_pipeline(
PipelineName='string',
ClientRequestToken='string'
)
string
[REQUIRED]
The name of the pipeline to delete.
string
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineArn': 'string'
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline to delete.
Lists the contexts in your account and their properties.
See also: AWS API Documentation
Request Syntax
client.list_contexts(
SourceUri='string',
ContextType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
A filter that returns only contexts with the specified source URI.
string
A filter that returns only contexts of the specified type.
datetime
A filter that returns only contexts created on or after the specified time.
datetime
A filter that returns only contexts created on or before the specified time.
string
The property used to sort results. The default value is CreationTime .
string
The sort order. The default value is Descending .
string
If the previous call to ListContexts didn't return the full set of contexts, the call returns a token for getting the next set of contexts.
integer
The maximum number of contexts to return in the response. The default value is 10.
dict
Response Syntax
{
'ContextSummaries': [
{
'ContextArn': 'string',
'ContextName': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ContextType': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ContextSummaries (list) --
A list of contexts and their properties.
(dict) --
Lists a summary of the properties of a context. A context provides a logical grouping of other entities.
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
ContextName (string) --
The name of the context.
Source (dict) --
The source of the context.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ContextType (string) --
The type of the context.
CreationTime (datetime) --
When the context was created.
LastModifiedTime (datetime) --
When the context was last modified.
NextToken (string) --
A token for getting the next set of contexts, if there are any.
Updates a pipeline.
See also: AWS API Documentation
Request Syntax
client.update_pipeline(
PipelineName='string',
PipelineDisplayName='string',
PipelineDefinition='string',
PipelineDescription='string',
RoleArn='string'
)
string
[REQUIRED]
The name of the pipeline to update.
string
The display name of the pipeline.
string
The JSON pipeline definition.
string
The description of the pipeline.
string
The Amazon Resource Name (ARN) that the pipeline uses to execute.
dict
Response Syntax
{
'PipelineArn': 'string'
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the updated pipeline.
Deletes the specified model group.
See also: AWS API Documentation
Request Syntax
client.delete_model_package_group(
ModelPackageGroupName='string'
)
string
[REQUIRED]
The name of the model group to delete.
None
List FeatureGroup s based on given filter and order.
See also: AWS API Documentation
Request Syntax
client.list_feature_groups(
NameContains='string',
FeatureGroupStatusEquals='Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
OfflineStoreStatusEquals='Active'|'Blocked'|'Disabled',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
SortBy='Name'|'FeatureGroupStatus'|'OfflineStoreStatus'|'CreationTime',
MaxResults=123,
NextToken='string'
)
string
A string that partially matches one or more FeatureGroup s names. Filters FeatureGroup s by name.
string
A FeatureGroup status. Filters by FeatureGroup status.
string
An OfflineStore status. Filters by OfflineStore status.
datetime
Use this parameter to search for FeatureGroups s created after a specific date and time.
datetime
Use this parameter to search for FeatureGroups s created before a specific date and time.
string
The order in which feature groups are listed.
string
The value on which the feature group list is sorted.
integer
The maximum number of results returned by ListFeatureGroups .
string
A token to resume pagination of ListFeatureGroups results.
dict
Response Syntax
{
'FeatureGroupSummaries': [
{
'FeatureGroupName': 'string',
'FeatureGroupArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
FeatureGroupSummaries (list) --
A summary of feature groups.
(dict) --
The name, Arn, CreationTime , FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup .
FeatureGroupName (string) --
The name of FeatureGroup .
FeatureGroupArn (string) --
Unique identifier for the FeatureGroup .
CreationTime (datetime) --
A timestamp indicating the time of creation time of the FeatureGroup .
FeatureGroupStatus (string) --
The status of a FeatureGroup. The status can be any of the following: Creating , Created , CreateFail , Deleting or DetailFail .
OfflineStoreStatus (dict) --
Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
NextToken (string) --
A token to resume pagination of ListFeatureGroups results.
Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the AWS Identity and Access Management User Guide. .
See also: AWS API Documentation
Request Syntax
client.get_model_package_group_policy(
ModelPackageGroupName='string'
)
string
[REQUIRED]
The name of the model group for which to get the resource policy.
dict
Response Syntax
{
'ResourcePolicy': 'string'
}
Response Structure
(dict) --
ResourcePolicy (string) --
The resource policy for the model group.
Gets a list of the projects in an AWS account.
See also: AWS API Documentation
Request Syntax
client.list_projects(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
datetime
A filter that returns the projects that were created after a specified time.
datetime
A filter that returns the projects that were created before a specified time.
integer
The maximum number of projects to return in the response.
string
A filter that returns the projects whose name contains a specified string.
string
If the result of the previous ListProjects request was truncated, the response includes a NextToken . To retrieve the next set of projects, use the token in the next request.
string
The field by which to sort results. The default is CreationTime .
string
The sort order for results. The default is Ascending .
dict
Response Syntax
{
'ProjectSummaryList': [
{
'ProjectName': 'string',
'ProjectDescription': 'string',
'ProjectArn': 'string',
'ProjectId': 'string',
'CreationTime': datetime(2015, 1, 1),
'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ProjectSummaryList (list) --
A list of summaries of projects.
(dict) --
Information about a project.
ProjectName (string) --
The name of the project.
ProjectDescription (string) --
The description of the project.
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectId (string) --
The ID of the project.
CreationTime (datetime) --
The time that the project was created.
ProjectStatus (string) --
The status of the project.
NextToken (string) --
If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken . To retrieve the next set of model compilation jobs, use the token in the next request.
Updates an action.
See also: AWS API Documentation
Request Syntax
client.update_action(
ActionName='string',
Description='string',
Status='Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
Properties={
'string': 'string'
},
PropertiesToRemove=[
'string',
]
)
string
[REQUIRED]
The name of the action to update.
string
The new description for the action.
string
The new status for the action.
dict
The new list of properties. Overwrites the current property list.
(string) --
(string) --
list
A list of properties to remove.
(string) --
dict
Response Syntax
{
'ActionArn': 'string'
}
Response Structure
(dict) --
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
Creates a context . A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking .
See also: AWS API Documentation
Request Syntax
client.create_context(
ContextName='string',
Source={
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
ContextType='string',
Description='string',
Properties={
'string': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the context. Must be unique to your account in an AWS Region.
dict
[REQUIRED]
The source type, ID, and URI.
SourceUri (string) -- [REQUIRED]
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
string
[REQUIRED]
The context type.
string
The description of the context.
dict
A list of properties to add to the context.
(string) --
(string) --
list
A list of tags to apply to the context.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'ContextArn': 'string'
}
Response Structure
(dict) --
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
Deletes an action.
See also: AWS API Documentation
Request Syntax
client.delete_action(
ActionName='string'
)
string
[REQUIRED]
The name of the action to delete.
dict
Response Syntax
{
'ActionArn': 'string'
}
Response Structure
(dict) --
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
Updates a versioned model.
See also: AWS API Documentation
Request Syntax
client.update_model_package(
ModelPackageArn='string',
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
ApprovalDescription='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the model.
string
[REQUIRED]
The approval status of the model.
string
A description for the approval status of the model.
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model.
Deletes an artifact. Either ArtifactArn or Source must be specified.
See also: AWS API Documentation
Request Syntax
client.delete_artifact(
ArtifactArn='string',
Source={
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
}
)
string
The Amazon Resource Name (ARN) of the artifact to delete.
dict
The URI of the source.
SourceUri (string) -- [REQUIRED]
The URI of the source.
SourceTypes (list) --
A list of source types.
(dict) --
The ID and ID type of an artifact source.
SourceIdType (string) -- [REQUIRED]
The type of ID.
Value (string) -- [REQUIRED]
The ID.
dict
Response Syntax
{
'ArtifactArn': 'string'
}
Response Structure
(dict) --
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
Gets a list of parameters for a pipeline execution.
See also: AWS API Documentation
Request Syntax
client.list_pipeline_parameters_for_execution(
PipelineExecutionArn='string',
NextToken='string',
MaxResults=123
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
string
If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken . To retrieve the next set of parameters, use the token in the next request.
integer
The maximum number of parameters to return in the response.
dict
Response Syntax
{
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
NextToken (string) --
If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken . To retrieve the next set of parameters, use the token in the next request.
Create a new FeatureGroup . A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record .
The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features , a RecordIdentifierFeatureName , an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore . Check AWS service quotas to see the FeatureGroup s quota for your AWS account.
Warning
You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup .
See also: AWS API Documentation
Request Syntax
client.create_feature_group(
FeatureGroupName='string',
RecordIdentifierFeatureName='string',
EventTimeFeatureName='string',
FeatureDefinitions=[
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
OnlineStoreConfig={
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
OfflineStoreConfig={
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
RoleArn='string',
Description='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the FeatureGroup . The name must be unique within an AWS Region in an AWS account. The name:
Must start and end with an alphanumeric character.
Can only contain alphanumeric character and hyphens. Spaces are not allowed.
string
[REQUIRED]
The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore . Only the latest record per identifier value will be stored in the OnlineStore . RecordIdentifierFeatureName must be one of feature definitions' names.
You use the RecordIdentifierFeatureName to access data in a FeatureStore .
This name:
Must start and end with an alphanumeric character.
Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
string
[REQUIRED]
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
An EventTime can be a String or Fractional .
Fractional : EventTime feature values must be a Unix timestamp in seconds.
String : EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy , MM , and dd represent the year, month, and day respectively and HH , mm , ss , and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.
list
[REQUIRED]
A list of Feature names and types. Name and Type is compulsory per Feature .
Valid feature FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
dict
You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig ; the default value is False .
You can also include an AWS KMS key ID (KMSKeyId ) for at-rest encryption of the OnlineStore .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The ID of the AWS Key Management Service (AWS KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
dict
Use this to configure an OfflineFeatureStore . This parameter allows you to specify:
The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore .
A configuration for an AWS Glue or AWS Hive data cataolgue.
An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore .
To learn more about this parameter, see OfflineStoreConfig .
S3StorageConfig (dict) -- [REQUIRED]
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) -- [REQUIRED]
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The AWS Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an AWS Glue table when configuring an OfflineStore .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) -- [REQUIRED]
The name of the Glue table.
Catalog (string) -- [REQUIRED]
The name of the Glue table catalog.
Database (string) -- [REQUIRED]
The name of the Glue table database.
string
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
string
A free-form description of a FeatureGroup .
list
Tags used to identify Features in each FeatureGroup .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'FeatureGroupArn': 'string'
}
Response Structure
(dict) --
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of the FeatureGroup . This is a unique identifier for the feature group.
Lists the artifacts in your account and their properties.
See also: AWS API Documentation
Request Syntax
client.list_artifacts(
SourceUri='string',
ArtifactType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
A filter that returns only artifacts with the specified source URI.
string
A filter that returns only artifacts of the specified type.
datetime
A filter that returns only artifacts created on or after the specified time.
datetime
A filter that returns only artifacts created on or before the specified time.
string
The property used to sort results. The default value is CreationTime .
string
The sort order. The default value is Descending .
string
If the previous call to ListArtifacts didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts.
integer
The maximum number of artifacts to return in the response. The default value is 10.
dict
Response Syntax
{
'ArtifactSummaries': [
{
'ArtifactArn': 'string',
'ArtifactName': 'string',
'Source': {
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
'ArtifactType': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ArtifactSummaries (list) --
A list of artifacts and their properties.
(dict) --
Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
ArtifactName (string) --
The name of the artifact.
Source (dict) --
The source of the artifact.
SourceUri (string) --
The URI of the source.
SourceTypes (list) --
A list of source types.
(dict) --
The ID and ID type of an artifact source.
SourceIdType (string) --
The type of ID.
Value (string) --
The ID.
ArtifactType (string) --
The type of the artifact.
CreationTime (datetime) --
When the artifact was created.
LastModifiedTime (datetime) --
When the artifact was last modified.
NextToken (string) --
A token for getting the next set of artifacts, if there are any.
Deletes a model group resource policy.
See also: AWS API Documentation
Request Syntax
client.delete_model_package_group_policy(
ModelPackageGroupName='string'
)
string
[REQUIRED]
The name of the model group for which to delete the policy.
None
Describes a context.
See also: AWS API Documentation
Request Syntax
client.describe_context(
ContextName='string'
)
string
[REQUIRED]
The name of the context to describe.
dict
Response Syntax
{
'ContextName': 'string',
'ContextArn': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ContextType': 'string',
'Description': 'string',
'Properties': {
'string': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
(dict) --
ContextName (string) --
The name of the context.
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
Source (dict) --
The source of the context.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ContextType (string) --
The type of the context.
Description (string) --
The description of the context.
Properties (dict) --
A list of the context's properties.
(string) --
(string) --
CreationTime (datetime) --
When the context was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the context was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Updates an artifact.
See also: AWS API Documentation
Request Syntax
client.update_artifact(
ArtifactArn='string',
ArtifactName='string',
Properties={
'string': 'string'
},
PropertiesToRemove=[
'string',
]
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the artifact to update.
string
The new name for the artifact.
dict
The new list of properties. Overwrites the current property list.
(string) --
(string) --
list
A list of properties to remove.
(string) --
dict
Response Syntax
{
'ArtifactArn': 'string'
}
Response Structure
(dict) --
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
Describes the details of a pipeline.
See also: AWS API Documentation
Request Syntax
client.describe_pipeline(
PipelineName='string'
)
string
[REQUIRED]
The name of the pipeline to describe.
dict
Response Syntax
{
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDefinition': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDefinition (string) --
The JSON pipeline definition.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) that the pipeline uses to execute.
PipelineStatus (string) --
The status of the pipeline execution.
CreationTime (datetime) --
The time when the pipeline was created.
LastModifiedTime (datetime) --
The time when the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Delete the specified project.
See also: AWS API Documentation
Request Syntax
client.delete_project(
ProjectName='string'
)
string
[REQUIRED]
The name of the project to delete.
None
Updates a pipeline execution.
See also: AWS API Documentation
Request Syntax
client.update_pipeline_execution(
PipelineExecutionArn='string',
PipelineExecutionDescription='string',
PipelineExecutionDisplayName='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
string
The description of the pipeline execution.
string
The display name of the pipeline execution.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the updated pipeline execution.
Creates an artifact . An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking .
See also: AWS API Documentation
Request Syntax
client.create_artifact(
ArtifactName='string',
Source={
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
ArtifactType='string',
Properties={
'string': 'string'
},
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
The name of the artifact. Must be unique to your account in an AWS Region.
dict
[REQUIRED]
The ID, ID type, and URI of the source.
SourceUri (string) -- [REQUIRED]
The URI of the source.
SourceTypes (list) --
A list of source types.
(dict) --
The ID and ID type of an artifact source.
SourceIdType (string) -- [REQUIRED]
The type of ID.
Value (string) -- [REQUIRED]
The ID.
string
[REQUIRED]
The artifact type.
dict
A list of properties to add to the artifact.
(string) --
(string) --
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
list
A list of tags to apply to the artifact.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'ArtifactArn': 'string'
}
Response Structure
(dict) --
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
{'Tags': [{'Key': 'string', 'Value': 'string'}]}
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
See also: AWS API Documentation
Request Syntax
client.create_algorithm(
AlgorithmName='string',
AlgorithmDescription='string',
TrainingSpecification={
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
]
},
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'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',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the algorithm.
string
A description of the algorithm.
dict
[REQUIRED]
Specifies details about training jobs run by this algorithm, including the following:
The Amazon ECR path of the container and the version digest of the algorithm.
The hyperparameters that the algorithm supports.
The instance types that the algorithm supports for training.
Whether the algorithm supports distributed training.
The metrics that the algorithm emits to Amazon CloudWatch.
Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
The input channels that the algorithm supports for training data. For example, an algorithm might support train , validation , and test channels.
TrainingImage (string) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters (list) --
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
(dict) --
Defines a hyperparameter to be used by an algorithm.
Name (string) -- [REQUIRED]
The name of this hyperparameter. The name must be unique.
Description (string) --
A brief description of the hyperparameter.
Type (string) -- [REQUIRED]
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
Range (dict) --
The allowed range for this hyperparameter.
IntegerParameterRangeSpecification (dict) --
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
MinValue (string) -- [REQUIRED]
The minimum integer value allowed.
MaxValue (string) -- [REQUIRED]
The maximum integer value allowed.
ContinuousParameterRangeSpecification (dict) --
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
MinValue (string) -- [REQUIRED]
The minimum floating-point value allowed.
MaxValue (string) -- [REQUIRED]
The maximum floating-point value allowed.
CategoricalParameterRangeSpecification (dict) --
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
Values (list) -- [REQUIRED]
The allowed categories for the hyperparameter.
(string) --
IsTunable (boolean) --
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired (boolean) --
Indicates whether this hyperparameter is required.
DefaultValue (string) --
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes (list) -- [REQUIRED]
A list of the instance types that this algorithm can use for training.
(string) --
SupportsDistributedTraining (boolean) --
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions (list) --
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
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 Objective Metrics .
TrainingChannels (list) -- [REQUIRED]
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
(dict) --
Defines a named input source, called a channel, to be used by an algorithm.
Name (string) -- [REQUIRED]
The name of the channel.
Description (string) --
A brief description of the channel.
IsRequired (boolean) --
Indicates whether the channel is required by the algorithm.
SupportedContentTypes (list) -- [REQUIRED]
The supported MIME types for the data.
(string) --
SupportedCompressionTypes (list) --
The allowed compression types, if data compression is used.
(string) --
SupportedInputModes (list) -- [REQUIRED]
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
(string) --
SupportedTuningJobObjectiveMetrics (list) --
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
(dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
dict
Specifies details about inference jobs that the algorithm runs, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the algorithm supports for inference.
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The AWS Marketplace product ID of the model package.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) -- [REQUIRED]
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) -- [REQUIRED]
The supported MIME types for the output data.
(string) --
dict
Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
ValidationRole (string) -- [REQUIRED]
The IAM roles that Amazon SageMaker uses to run the training jobs.
ValidationProfiles (list) -- [REQUIRED]
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.
(dict) --
Defines a training job and a batch transform job that Amazon SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on AWS Marketplace.
ProfileName (string) -- [REQUIRED]
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition (dict) -- [REQUIRED]
The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.
TrainingInputMode (string) -- [REQUIRED]
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms .
If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
HyperParameters (dict) --
The hyperparameters used for the training job.
(string) --
(string) --
InputDataConfig (list) -- [REQUIRED]
An array of Channel objects, each of which specifies an input source.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon 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 Amazon 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 Amazon SageMaker uses to perform tasks on your behalf.
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon 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) --
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, Amazon 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 , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) -- [REQUIRED]
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// 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 master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon 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 AWS KMS in the AWS Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) -- [REQUIRED]
The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType (string) -- [REQUIRED]
The ML compute instance type.
InstanceCount (integer) -- [REQUIRED]
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.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The AWS KMS key that Amazon 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"
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) -- [REQUIRED]
A description of the input source and the way the transform job consumes it.
DataSource (dict) -- [REQUIRED]
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) -- [REQUIRED]
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) -- [REQUIRED]
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide .
TransformResources (dict) -- [REQUIRED]
Identifies the ML compute instances for the transform job.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
boolean
Whether to certify the algorithm so that it can be listed in AWS Marketplace.
list
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'AlgorithmArn': 'string'
}
Response Structure
(dict) --
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the new algorithm.
{'Tags': [{'Key': 'string', 'Value': 'string'}]}
Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in AWS CodeCommit or in any other Git repository.
See also: AWS API Documentation
Request Syntax
client.create_code_repository(
CodeRepositoryName='string',
GitConfig={
'RepositoryUrl': 'string',
'Branch': 'string',
'SecretArn': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
dict
[REQUIRED]
Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
RepositoryUrl (string) -- [REQUIRED]
The URL where the Git repository is located.
Branch (string) --
The default branch for the Git repository.
SecretArn (string) --
The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
list
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'CodeRepositoryArn': 'string'
}
Response Structure
(dict) --
CodeRepositoryArn (string) --
The Amazon Resource Name (ARN) of the new repository.
{'InputConfig': {'Framework': {'SKLEARN'}},
'OutputConfig': {'KmsKeyId': 'string', 'TargetDevice': {'jacinto_tda4vm'}}}
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob . To get information about a particular model compilation job, use DescribeCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .
See also: AWS API Documentation
Request Syntax
client.create_compilation_job(
CompilationJobName='string',
RoleArn='string',
InputConfig={
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN'
},
OutputConfig={
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'
},
'CompilerOptions': 'string',
'KmsKeyId': 'string'
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, Amazon SageMaker needs your permission to:
Read input data from an S3 bucket
Write model artifacts to an S3 bucket
Write logs to Amazon CloudWatch Logs
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
dict
[REQUIRED]
Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri (string) -- [REQUIRED]
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig (string) -- [REQUIRED]
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST : input data name and shape are not needed.
DataInputConfig supports the following parameters for CoreML OutputConfig$TargetDevice (ML Model format):
shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .
bias : If the input type is an Image, you need to provide the bias vector.
scale : If the input type is an Image, you need to provide a scale factor.
CoreML ClassifierConfig parameters can be specified using OutputConfig$CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Tensor type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
Tensor type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
Image type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Image type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Framework (string) -- [REQUIRED]
Identifies the framework in which the model was trained. For example: TENSORFLOW.
dict
[REQUIRED]
Provides information about the output location for the compiled model and the target device the model runs on.
S3OutputLocation (string) -- [REQUIRED]
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
TargetDevice (string) --
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .
TargetPlatform (dict) --
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
Os (string) -- [REQUIRED]
Specifies a target platform OS.
LINUX : Linux-based operating systems.
ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
Arch (string) -- [REQUIRED]
Specifies a target platform architecture.
X86_64 : 64-bit version of the x86 instruction set.
X86 : 32-bit version of the x86 instruction set.
ARM64 : ARMv8 64-bit CPU.
ARM_EABIHF : ARMv7 32-bit, Hard Float.
ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
Accelerator (string) --
Specifies a target platform accelerator (optional).
NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options
MALI : ARM Mali graphics processor
INTEL_GRAPHICS : Integrated Intel graphics
CompilerOptions (string) --
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
CPU : Compilation for CPU supports the following compiler options.
mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM : Details of ARM CPU compilations.
NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.
gpu_code : Specifies the targeted architecture.
trt-ver : Specifies the TensorRT versions in x.y.z. format.
cuda-ver : Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID : Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .
mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI .
CoreML : Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
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
dict
[REQUIRED]
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .
list
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'CompilationJobArn': 'string'
}
Response Structure
(dict) --
CompilationJobArn (string) --
If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
CompilationJobArn : The Amazon Resource Name (ARN) of the compiled job.
{'ClientToken': 'string',
'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'},
'ModelApprovalStatus': 'Approved | Rejected | PendingManualApproval',
'ModelMetrics': {'Bias': {'Report': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'Explainability': {'Report': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'ModelDataQuality': {'Constraints': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'},
'Statistics': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'ModelQuality': {'Constraints': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'},
'Statistics': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}}},
'ModelPackageGroupName': 'string',
'Tags': [{'Key': 'string', 'Value': 'string'}]}
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification . To create a model from an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for SourceAlgorithmSpecification .
Note
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
See also: AWS API Documentation
Request Syntax
client.create_model_package(
ModelPackageName='string',
ModelPackageGroupName='string',
ModelPackageDescription='string',
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
SourceAlgorithmSpecification={
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
ModelMetrics={
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
ClientToken='string'
)
string
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
string
The name of the model group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
string
A description of the model package.
dict
Specifies details about inference jobs that can be run with models based on this model package, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the model package supports for inference.
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The AWS Marketplace product ID of the model package.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) -- [REQUIRED]
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) -- [REQUIRED]
The supported MIME types for the output data.
(string) --
dict
Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
ValidationRole (string) -- [REQUIRED]
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) -- [REQUIRED]
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on AWS Marketplace.
ProfileName (string) -- [REQUIRED]
The name of the profile for the model package.
TransformJobDefinition (dict) -- [REQUIRED]
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) -- [REQUIRED]
A description of the input source and the way the transform job consumes it.
DataSource (dict) -- [REQUIRED]
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) -- [REQUIRED]
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) -- [REQUIRED]
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide .
TransformResources (dict) -- [REQUIRED]
Identifies the ML compute instances for the transform job.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
dict
Details about the algorithm that was used to create the model package.
SourceAlgorithms (list) -- [REQUIRED]
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
AlgorithmName (string) -- [REQUIRED]
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
boolean
Whether to certify the model package for listing on AWS Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
list
A list of key value pairs associated with the model. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
string
Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to Approved to deploy the model.
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
dict
A structure that contains model metrics reports.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) -- [REQUIRED]
ContentDigest (string) --
S3Uri (string) -- [REQUIRED]
Constraints (dict) --
Model quality constraints.
ContentType (string) -- [REQUIRED]
ContentDigest (string) --
S3Uri (string) -- [REQUIRED]
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) -- [REQUIRED]
ContentDigest (string) --
S3Uri (string) -- [REQUIRED]
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) -- [REQUIRED]
ContentDigest (string) --
S3Uri (string) -- [REQUIRED]
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) -- [REQUIRED]
ContentDigest (string) --
S3Uri (string) -- [REQUIRED]
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) -- [REQUIRED]
ContentDigest (string) --
S3Uri (string) -- [REQUIRED]
string
A unique token that guarantees that the call to this API is idempotent.
This field is autopopulated if not provided.
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the new model package.
{'ProcessingInputs': {'AppManaged': 'boolean',
'DatasetDefinition': {'AthenaDatasetDefinition': {'Catalog': 'string',
'Database': 'string',
'KmsKeyId': 'string',
'OutputCompression': 'GZIP '
'| '
'SNAPPY '
'| '
'ZLIB',
'OutputFormat': 'PARQUET '
'| '
'ORC '
'| '
'AVRO '
'| '
'JSON '
'| '
'TEXTFILE',
'OutputS3Uri': 'string',
'QueryString': 'string',
'WorkGroup': 'string'},
'DataDistributionType': 'FullyReplicated '
'| '
'ShardedByS3Key',
'InputMode': 'Pipe | File',
'LocalPath': 'string',
'RedshiftDatasetDefinition': {'ClusterId': 'string',
'ClusterRoleArn': 'string',
'Database': 'string',
'DbUser': 'string',
'KmsKeyId': 'string',
'OutputCompression': 'None '
'| '
'GZIP '
'| '
'BZIP2 '
'| '
'ZSTD '
'| '
'SNAPPY',
'OutputFormat': 'PARQUET '
'| '
'CSV',
'OutputS3Uri': 'string',
'QueryString': 'string'}}},
'ProcessingOutputConfig': {'Outputs': {'AppManaged': 'boolean',
'FeatureStoreOutput': {'FeatureGroupName': 'string'}}}}
Creates a processing job.
See also: AWS API Documentation
Request Syntax
client.create_processing_job(
ProcessingInputs=[
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
ProcessingOutputConfig={
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
ProcessingJobName='string',
ProcessingResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
AppSpecification={
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
Environment={
'string': 'string'
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
}
)
list
List of input configurations for the processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) -- [REQUIRED]
The name of the inputs for the processing job.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for processing job inputs in Amazon S3.
S3Uri (string) -- [REQUIRED]
The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
S3DataType (string) -- [REQUIRED]
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.This is a required parameter when AppManaged is False (default).
S3DataDistributionType (string) --
Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key .
S3CompressionType (string) --
Whether to use Gzip compression for Amazon S3 storage.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) -- [REQUIRED]
The name of the data catalog used in Athena query execution.
Database (string) -- [REQUIRED]
The name of the database used in the Athena query execution.
QueryString (string) -- [REQUIRED]
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) -- [REQUIRED]
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) -- [REQUIRED]
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) -- [REQUIRED]
The Redshift cluster Identifier.
Database (string) -- [REQUIRED]
The name of the Redshift database used in Redshift query execution.
DbUser (string) -- [REQUIRED]
The database user name used in Redshift query execution.
QueryString (string) -- [REQUIRED]
The SQL query statements to be executed.
ClusterRoleArn (string) -- [REQUIRED]
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) -- [REQUIRED]
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) -- [REQUIRED]
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
dict
Output configuration for the processing job.
Outputs (list) -- [REQUIRED]
List of output configurations for the processing job.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) -- [REQUIRED]
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) -- [REQUIRED]
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) -- [REQUIRED]
The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job. LocalPath is an absolute path to the input data.
S3UploadMode (string) -- [REQUIRED]
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) -- [REQUIRED]
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
string
[REQUIRED]
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
dict
[REQUIRED]
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) -- [REQUIRED]
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
dict
The time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds (integer) -- [REQUIRED]
Specifies the maximum runtime in seconds.
dict
[REQUIRED]
Configures the processing job to run a specified Docker container image.
ImageUri (string) -- [REQUIRED]
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a processing job.
(string) --
dict
Sets the environment variables in the Docker container.
(string) --
(string) --
dict
Networking options for a processing job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [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) --
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
list
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
dict
Response Syntax
{
'ProcessingJobArn': 'string'
}
Response Structure
(dict) --
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the processing job.
{'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'}}
Creates an Amazon SageMaker trial . A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment .
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
See also: AWS API Documentation
Request Syntax
client.create_trial(
TrialName='string',
DisplayName='string',
ExperimentName='string',
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the trial. The name must be unique in your AWS account and is not case-sensitive.
string
The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
string
[REQUIRED]
The name of the experiment to associate the trial with.
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
list
A list of tags to associate with the trial. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'TrialArn': 'string'
}
Response Structure
(dict) --
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
{'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'}}
Creates a trial component , which is a stage of a machine learning trial . A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
Note
CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call to CreateTrialComponent from outside one of these environments results in an error.
See also: AWS API Documentation
Request Syntax
client.create_trial_component(
TrialComponentName='string',
DisplayName='string',
Status={
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
StartTime=datetime(2015, 1, 1),
EndTime=datetime(2015, 1, 1),
Parameters={
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
InputArtifacts={
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
OutputArtifacts={
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the component. The name must be unique in your AWS account and is not case-sensitive.
string
The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
dict
The status of the component. States include:
InProgress
Completed
Failed
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
datetime
When the component started.
datetime
When the component ended.
dict
The hyperparameters for the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
dict
The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) -- [REQUIRED]
The location of the artifact.
dict
The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) -- [REQUIRED]
The location of the artifact.
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
list
A list of tags to associate with the component. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'TrialComponentArn': 'string'
}
Response Structure
(dict) --
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
{'InputConfig': {'Framework': {'SKLEARN'}},
'ModelDigests': {'ArtifactDigest': 'string'},
'OutputConfig': {'KmsKeyId': 'string', 'TargetDevice': {'jacinto_tda4vm'}}}
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .
See also: AWS API Documentation
Request Syntax
client.describe_compilation_job(
CompilationJobName='string'
)
string
[REQUIRED]
The name of the model compilation job that you want information about.
dict
Response Syntax
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'ModelDigests': {
'ArtifactDigest': 'string'
},
'RoleArn': 'string',
'InputConfig': {
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN'
},
'OutputConfig': {
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'
},
'CompilerOptions': 'string',
'KmsKeyId': 'string'
}
}
Response Structure
(dict) --
CompilationJobName (string) --
The name of the model compilation job.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
CompilationJobStatus (string) --
The status of the model compilation job.
CompilationStartTime (datetime) --
The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
CompilationEndTime (datetime) --
The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
StoppingCondition (dict) --
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .
CreationTime (datetime) --
The time that the model compilation job was created.
LastModifiedTime (datetime) --
The time that the status of the model compilation job was last modified.
FailureReason (string) --
If a model compilation job failed, the reason it failed.
ModelArtifacts (dict) --
Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
ModelDigests (dict) --
Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
ArtifactDigest (string) --
Provides a hash value that uniquely identifies the stored model artifacts.
RoleArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
InputConfig (dict) --
Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig (string) --
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST : input data name and shape are not needed.
DataInputConfig supports the following parameters for CoreML OutputConfig$TargetDevice (ML Model format):
shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .
bias : If the input type is an Image, you need to provide the bias vector.
scale : If the input type is an Image, you need to provide a scale factor.
CoreML ClassifierConfig parameters can be specified using OutputConfig$CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Tensor type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
Tensor type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
Image type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Image type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Framework (string) --
Identifies the framework in which the model was trained. For example: TENSORFLOW.
OutputConfig (dict) --
Information about the output location for the compiled model and the target device that the model runs on.
S3OutputLocation (string) --
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
TargetDevice (string) --
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .
TargetPlatform (dict) --
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
Os (string) --
Specifies a target platform OS.
LINUX : Linux-based operating systems.
ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
Arch (string) --
Specifies a target platform architecture.
X86_64 : 64-bit version of the x86 instruction set.
X86 : 32-bit version of the x86 instruction set.
ARM64 : ARMv8 64-bit CPU.
ARM_EABIHF : ARMv7 32-bit, Hard Float.
ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
Accelerator (string) --
Specifies a target platform accelerator (optional).
NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options
MALI : ARM Mali graphics processor
INTEL_GRAPHICS : Integrated Intel graphics
CompilerOptions (string) --
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
CPU : Compilation for CPU supports the following compiler options.
mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM : Details of ARM CPU compilations.
NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.
gpu_code : Specifies the targeted architecture.
trt-ver : Specifies the TensorRT versions in x.y.z. format.
cuda-ver : Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID : Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .
mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI .
CoreML : Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
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
{'LastDeploymentConfig': {'AutoRollbackConfiguration': {'Alarms': [{'AlarmName': 'string'}]},
'BlueGreenUpdatePolicy': {'MaximumExecutionTimeoutInSeconds': 'integer',
'TerminationWaitInSeconds': 'integer',
'TrafficRoutingConfiguration': {'CanarySize': {'Type': 'INSTANCE_COUNT '
'| '
'CAPACITY_PERCENT',
'Value': 'integer'},
'Type': 'ALL_AT_ONCE '
'| '
'CANARY',
'WaitIntervalInSeconds': 'integer'}}}}
Returns the description of an endpoint.
See also: AWS API Documentation
Request Syntax
client.describe_endpoint(
EndpointName='string'
)
string
[REQUIRED]
The name of the endpoint.
dict
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
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastDeploymentConfig': {
'BlueGreenUpdatePolicy': {
'TrafficRoutingConfiguration': {
'Type': 'ALL_AT_ONCE'|'CANARY',
'WaitIntervalInSeconds': 123,
'CanarySize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
}
},
'TerminationWaitInSeconds': 123,
'MaximumExecutionTimeoutInSeconds': 123
},
'AutoRollbackConfiguration': {
'Alarms': [
{
'AlarmName': 'string'
},
]
}
}
}
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.
DataCaptureConfig (dict) --
EnableCapture (boolean) --
CaptureStatus (string) --
CurrentSamplingPercentage (integer) --
DestinationS3Uri (string) --
KmsKeyId (string) --
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 DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.
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) --
TrafficRoutingConfiguration (dict) --
Type (string) --
WaitIntervalInSeconds (integer) --
CanarySize (dict) --
Type (string) --
This API is not supported.
Value (integer) --
TerminationWaitInSeconds (integer) --
MaximumExecutionTimeoutInSeconds (integer) --
AutoRollbackConfiguration (dict) --
Alarms (list) --
(dict) --
This API is not supported.
AlarmName (string) --
{'ApprovalDescription': 'string',
'CreatedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'LastModifiedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'LastModifiedTime': 'timestamp',
'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'},
'ModelApprovalStatus': 'Approved | Rejected | PendingManualApproval',
'ModelMetrics': {'Bias': {'Report': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'Explainability': {'Report': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'ModelDataQuality': {'Constraints': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'},
'Statistics': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'ModelQuality': {'Constraints': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'},
'Statistics': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}}},
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 'integer'}
Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
See also: AWS API Documentation
Request Syntax
client.describe_model_package(
ModelPackageName='string'
)
string
[REQUIRED]
The name of the model package to describe.
dict
Response Syntax
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ApprovalDescription': 'string'
}
Response Structure
(dict) --
ModelPackageName (string) --
The name of the model package being described.
ModelPackageGroupName (string) --
If the model is a versioned model, the name of the model group that the versioned model belongs to.
ModelPackageVersion (integer) --
The version of the model package.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
A brief summary of the model package.
CreationTime (datetime) --
A timestamp specifying when the model package was created.
InferenceSpecification (dict) --
Details about inference jobs that can be run with models based on this model package.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The AWS Marketplace product ID of the model package.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SourceAlgorithmSpecification (dict) --
Details about the algorithm that was used to create the model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on AWS Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
ModelPackageStatus (string) --
The current status of the model package.
ModelPackageStatusDetails (dict) --
Details about the current status of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is certified for listing on AWS Marketplace.
ModelApprovalStatus (string) --
The approval status of the model package.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Constraints (dict) --
Model quality constraints.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
LastModifiedTime (datetime) --
The last time the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ApprovalDescription (string) --
A description provided for the model approval.
{'ProcessingInputs': {'AppManaged': 'boolean',
'DatasetDefinition': {'AthenaDatasetDefinition': {'Catalog': 'string',
'Database': 'string',
'KmsKeyId': 'string',
'OutputCompression': 'GZIP '
'| '
'SNAPPY '
'| '
'ZLIB',
'OutputFormat': 'PARQUET '
'| '
'ORC '
'| '
'AVRO '
'| '
'JSON '
'| '
'TEXTFILE',
'OutputS3Uri': 'string',
'QueryString': 'string',
'WorkGroup': 'string'},
'DataDistributionType': 'FullyReplicated '
'| '
'ShardedByS3Key',
'InputMode': 'Pipe | File',
'LocalPath': 'string',
'RedshiftDatasetDefinition': {'ClusterId': 'string',
'ClusterRoleArn': 'string',
'Database': 'string',
'DbUser': 'string',
'KmsKeyId': 'string',
'OutputCompression': 'None '
'| '
'GZIP '
'| '
'BZIP2 '
'| '
'ZSTD '
'| '
'SNAPPY',
'OutputFormat': 'PARQUET '
'| '
'CSV',
'OutputS3Uri': 'string',
'QueryString': 'string'}}},
'ProcessingOutputConfig': {'Outputs': {'AppManaged': 'boolean',
'FeatureStoreOutput': {'FeatureGroupName': 'string'}}}}
Returns a description of a processing job.
See also: AWS API Documentation
Request Syntax
client.describe_processing_job(
ProcessingJobName='string'
)
string
[REQUIRED]
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
dict
Response Syntax
{
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string'
}
Response Structure
(dict) --
ProcessingInputs (list) --
The inputs for a processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) --
The name of the inputs for the processing job.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for processing job inputs in Amazon S3.
S3Uri (string) --
The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.This is a required parameter when AppManaged is False (default).
S3DataDistributionType (string) --
Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key .
S3CompressionType (string) --
Whether to use Gzip compression for Amazon S3 storage.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) --
The name of the data catalog used in Athena query execution.
Database (string) --
The name of the database used in the Athena query execution.
QueryString (string) --
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) --
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) --
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) --
The Redshift cluster Identifier.
Database (string) --
The name of the Redshift database used in Redshift query execution.
DbUser (string) --
The database user name used in Redshift query execution.
QueryString (string) --
The SQL query statements to be executed.
ClusterRoleArn (string) --
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) --
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) --
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
ProcessingOutputConfig (dict) --
Output configuration for the processing job.
Outputs (list) --
List of output configurations for the processing job.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job. LocalPath is an absolute path to the input data.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) --
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
StoppingCondition (dict) --
The time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configures the processing job to run a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a processing job.
(string) --
Environment (dict) --
The environment variables set in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Networking options for a processing job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
ExperimentConfig (dict) --
The configuration information used to create an experiment.
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the processing job.
ProcessingJobStatus (string) --
Provides the status of a processing job.
ExitMessage (string) --
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time at which the processing job completed.
ProcessingStartTime (datetime) --
The time at which the processing job started.
LastModifiedTime (datetime) --
The time at which the processing job was last modified.
CreationTime (datetime) --
The time at which the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The ARN of an AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of a training job associated with this processing job.
{'SecondaryStatus': {'Updating'},
'SecondaryStatusTransitions': {'Status': {'Updating'}}}
Returns information about a training job.
See also: AWS API Documentation
Request Syntax
client.describe_training_job(
TrainingJobName='string'
)
string
[REQUIRED]
The name of the training job.
dict
Response Syntax
{
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'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',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating',
'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'
},
'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',
'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)
},
]
}
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 Amazon 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.
Amazon 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 .
Amazon 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
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
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 AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
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 Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
RoleArn (string) --
The AWS 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. Amazon 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 Amazon 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 Amazon SageMaker uses to perform tasks on your behalf.
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon 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) --
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, Amazon 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 , Amazon 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. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// 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 master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon 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 AWS KMS in the AWS Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
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.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The AWS KMS key that Amazon 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"
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 the maximum time to wait for a spot instance. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon 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 the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .
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 Amazon 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 DescribeTrainingJobResponse$SecondaryStatusTransitions . 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, Amazon 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.
Amazon 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, Amazon 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 Amazon 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.
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 debug hook parameters, collection configuration, and storage paths.
LocalPath (string) --
Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for tensors.
HookParameters (dict) --
Configuration information for the debug hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for tensor collections.
(dict) --
Configuration information for 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:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
DebugRuleConfigurations (list) --
Configuration information for debugging rules.
(dict) --
Configuration information for debugging 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 (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy for 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 TensorBoard output.
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) --
Status about the debug rule evaluation.
(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.
{'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'}}
Provides a list of a trial's properties.
See also: AWS API Documentation
Request Syntax
client.describe_trial(
TrialName='string'
)
string
[REQUIRED]
The name of the trial to describe.
dict
Response Syntax
{
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
}
}
Response Structure
(dict) --
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Who created the trial.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the trial was last modified.
LastModifiedBy (dict) --
Who last modified the trial.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
{'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'}}
Provides a list of a trials component's properties.
See also: AWS API Documentation
Request Syntax
client.describe_trial_component(
TrialComponentName='string'
)
string
[REQUIRED]
The name of the trial component to describe.
dict
Response Syntax
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
]
}
Response Structure
(dict) --
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
Source (dict) --
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the component. States include:
InProgress
Completed
Failed
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Who created the component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Who last modified the component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
{'Resource': {'Endpoint',
'FeatureGroup',
'ModelPackage',
'ModelPackageGroup',
'Pipeline',
'PipelineExecution'}}
An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters , Tags , and Metrics .
See also: AWS API Documentation
Request Syntax
client.get_search_suggestions(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup',
SuggestionQuery={
'PropertyNameQuery': {
'PropertyNameHint': 'string'
}
}
)
string
[REQUIRED]
The name of the Amazon SageMaker resource to search for.
dict
Limits the property names that are included in the response.
PropertyNameQuery (dict) --
Defines a property name hint. Only property names that begin with the specified hint are included in the response.
PropertyNameHint (string) -- [REQUIRED]
Text that begins a property's name.
dict
Response Syntax
{
'PropertyNameSuggestions': [
{
'PropertyName': 'string'
},
]
}
Response Structure
(dict) --
PropertyNameSuggestions (list) --
A list of property names for a Resource that match a SuggestionQuery .
(dict) --
A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.
PropertyName (string) --
A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.
{'CompilationJobSummaries': {'CompilationTargetDevice': {'jacinto_tda4vm'}}}
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob . To get information about a particular model compilation job you have created, use DescribeCompilationJob .
See also: AWS API Documentation
Request Syntax
client.list_compilation_jobs(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending'
)
string
If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken . To retrieve the next set of model compilation jobs, use the token in the next request.
integer
The maximum number of model compilation jobs to return in the response.
datetime
A filter that returns the model compilation jobs that were created after a specified time.
datetime
A filter that returns the model compilation jobs that were created before a specified time.
datetime
A filter that returns the model compilation jobs that were modified after a specified time.
datetime
A filter that returns the model compilation jobs that were modified before a specified time.
string
A filter that returns the model compilation jobs whose name contains a specified string.
string
A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.
string
The field by which to sort results. The default is CreationTime .
string
The sort order for results. The default is Ascending .
dict
Response Syntax
{
'CompilationJobSummaries': [
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'CompilationTargetPlatformOs': 'ANDROID'|'LINUX',
'CompilationTargetPlatformArch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA',
'LastModifiedTime': datetime(2015, 1, 1),
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
CompilationJobSummaries (list) --
An array of CompilationJobSummary objects, each describing a model compilation job.
(dict) --
A summary of a model compilation job.
CompilationJobName (string) --
The name of the model compilation job that you want a summary for.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
CreationTime (datetime) --
The time when the model compilation job was created.
CompilationStartTime (datetime) --
The time when the model compilation job started.
CompilationEndTime (datetime) --
The time when the model compilation job completed.
CompilationTargetDevice (string) --
The type of device that the model will run on after the compilation job has completed.
CompilationTargetPlatformOs (string) --
The type of OS that the model will run on after the compilation job has completed.
CompilationTargetPlatformArch (string) --
The type of architecture that the model will run on after the compilation job has completed.
CompilationTargetPlatformAccelerator (string) --
The type of accelerator that the model will run on after the compilation job has completed.
LastModifiedTime (datetime) --
The time when the model compilation job was last modified.
CompilationJobStatus (string) --
The status of the model compilation job.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this NextToken . To retrieve the next set of model compilation jobs, use this token in the next request.
{'ModelApprovalStatus': 'Approved | Rejected | PendingManualApproval',
'ModelPackageGroupName': 'string',
'ModelPackageType': 'Versioned | Unversioned | Both'}
Response {'ModelPackageSummaryList': {'ModelApprovalStatus': 'Approved | Rejected | '
'PendingManualApproval',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 'integer'}}
Lists the model packages that have been created.
See also: AWS API Documentation
Request Syntax
client.list_model_packages(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
ModelPackageGroupName='string',
ModelPackageType='Versioned'|'Unversioned'|'Both',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
datetime
A filter that returns only model packages created after the specified time (timestamp).
datetime
A filter that returns only model packages created before the specified time (timestamp).
integer
The maximum number of model packages to return in the response.
string
A string in the model package name. This filter returns only model packages whose name contains the specified string.
string
A filter that returns only the model packages with the specified approval status.
string
A filter that returns only model versions that belong to the specified model group.
string
A filter that returns onlyl the model packages of the specified type. This can be one of the following values.
VERSIONED - List only versioned models.
UNVERSIONED - List only unversioined models.
BOTH - List both versioned and unversioned models.
string
If the response to a previous ListModelPackages request was truncated, the response includes a NextToken . To retrieve the next set of model packages, use the token in the next request.
string
The parameter by which to sort the results. The default is CreationTime .
string
The sort order for the results. The default is Ascending .
dict
Response Syntax
{
'ModelPackageSummaryList': [
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ModelPackageSummaryList (list) --
An array of ModelPackageSummary objects, each of which lists a model package.
(dict) --
Provides summary information about a model package.
ModelPackageName (string) --
The name of the model package.
ModelPackageGroupName (string) --
If the model package is a versioned model, the model group that the versioned model belongs to.
ModelPackageVersion (integer) --
If the model package is a versioned model, the version of the model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
A brief description of the model package.
CreationTime (datetime) --
A timestamp that shows when the model package was created.
ModelPackageStatus (string) --
The overall status of the model package.
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
APPROVED - The model is approved
REJECTED - The model is rejected.
PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
{'Resource': {'Endpoint',
'FeatureGroup',
'ModelPackage',
'ModelPackageGroup',
'Pipeline',
'PipelineExecution'}}
Response {'Results': {'Endpoint': {'CreationTime': 'timestamp',
'DataCaptureConfig': {'CaptureStatus': 'Started | '
'Stopped',
'CurrentSamplingPercentage': 'integer',
'DestinationS3Uri': 'string',
'EnableCapture': 'boolean',
'KmsKeyId': 'string'},
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'EndpointName': 'string',
'EndpointStatus': 'OutOfService | Creating | '
'Updating | SystemUpdating | '
'RollingBack | InService | '
'Deleting | Failed',
'FailureReason': 'string',
'LastModifiedTime': 'timestamp',
'MonitoringSchedules': [{'CreationTime': 'timestamp',
'EndpointName': 'string',
'FailureReason': 'string',
'LastModifiedTime': 'timestamp',
'LastMonitoringExecutionSummary': {'CreationTime': 'timestamp',
'EndpointName': 'string',
'FailureReason': 'string',
'LastModifiedTime': 'timestamp',
'MonitoringExecutionStatus': 'Pending '
'| '
'Completed '
'| '
'CompletedWithViolations '
'| '
'InProgress '
'| '
'Failed '
'| '
'Stopping '
'| '
'Stopped',
'MonitoringScheduleName': 'string',
'ProcessingJobArn': 'string',
'ScheduledTime': 'timestamp'},
'MonitoringScheduleArn': 'string',
'MonitoringScheduleConfig': {'MonitoringJobDefinition': {'BaselineConfig': {'ConstraintsResource': {'S3Uri': 'string'},
'StatisticsResource': {'S3Uri': 'string'}},
'Environment': {'string': 'string'},
'MonitoringAppSpecification': {'ContainerArguments': ['string'],
'ContainerEntrypoint': ['string'],
'ImageUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string',
'RecordPreprocessorSourceUri': 'string'},
'MonitoringInputs': [{'EndpointInput': {'EndpointName': 'string',
'LocalPath': 'string',
'S3DataDistributionType': 'FullyReplicated '
'| '
'ShardedByS3Key',
'S3InputMode': 'Pipe '
'| '
'File'}}],
'MonitoringOutputConfig': {'KmsKeyId': 'string',
'MonitoringOutputs': [{'S3Output': {'LocalPath': 'string',
'S3UploadMode': 'Continuous '
'| '
'EndOfJob',
'S3Uri': 'string'}}]},
'MonitoringResources': {'ClusterConfig': {'InstanceCount': 'integer',
'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',
'VolumeKmsKeyId': 'string',
'VolumeSizeInGB': 'integer'}},
'NetworkConfig': {'EnableInterContainerTrafficEncryption': 'boolean',
'EnableNetworkIsolation': 'boolean',
'VpcConfig': {'SecurityGroupIds': ['string'],
'Subnets': ['string']}},
'RoleArn': 'string',
'StoppingCondition': {'MaxRuntimeInSeconds': 'integer'}},
'ScheduleConfig': {'ScheduleExpression': 'string'}},
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending '
'| '
'Failed '
'| '
'Scheduled '
'| '
'Stopped',
'Tags': [{'Key': 'string',
'Value': 'string'}]}],
'ProductionVariants': [{'CurrentInstanceCount': 'integer',
'CurrentWeight': 'float',
'DeployedImages': [{'ResolutionTime': 'timestamp',
'ResolvedImage': 'string',
'SpecifiedImage': 'string'}],
'DesiredInstanceCount': 'integer',
'DesiredWeight': 'float',
'VariantName': 'string'}],
'Tags': [{'Key': 'string', 'Value': 'string'}]},
'FeatureGroup': {'CreationTime': 'timestamp',
'Description': 'string',
'EventTimeFeatureName': 'string',
'FailureReason': 'string',
'FeatureDefinitions': [{'FeatureName': 'string',
'FeatureType': 'Integral '
'| '
'Fractional '
'| '
'String'}],
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'FeatureGroupStatus': 'Creating | Created | '
'CreateFailed | Deleting | '
'DeleteFailed',
'OfflineStoreConfig': {'DataCatalogConfig': {'Catalog': 'string',
'Database': 'string',
'TableName': 'string'},
'DisableGlueTableCreation': 'boolean',
'S3StorageConfig': {'KmsKeyId': 'string',
'S3Uri': 'string'}},
'OfflineStoreStatus': {'BlockedReason': 'string',
'Status': 'Active | '
'Blocked | '
'Disabled'},
'OnlineStoreConfig': {'EnableOnlineStore': 'boolean',
'SecurityConfig': {'KmsKeyId': 'string'}},
'RecordIdentifierFeatureName': 'string',
'RoleArn': 'string',
'Tags': [{'Key': 'string', 'Value': 'string'}]},
'ModelPackage': {'ApprovalDescription': 'string',
'CertifyForMarketplace': 'boolean',
'CreatedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'CreationTime': 'timestamp',
'InferenceSpecification': {'Containers': [{'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string'}],
'SupportedContentTypes': ['string'],
'SupportedRealtimeInferenceInstanceTypes': ['ml.t2.medium '
'| '
'ml.t2.large '
'| '
'ml.t2.xlarge '
'| '
'ml.t2.2xlarge '
'| '
'ml.m4.xlarge '
'| '
'ml.m4.2xlarge '
'| '
'ml.m4.4xlarge '
'| '
'ml.m4.10xlarge '
'| '
'ml.m4.16xlarge '
'| '
'ml.m5.large '
'| '
'ml.m5.xlarge '
'| '
'ml.m5.2xlarge '
'| '
'ml.m5.4xlarge '
'| '
'ml.m5.12xlarge '
'| '
'ml.m5.24xlarge '
'| '
'ml.m5d.large '
'| '
'ml.m5d.xlarge '
'| '
'ml.m5d.2xlarge '
'| '
'ml.m5d.4xlarge '
'| '
'ml.m5d.12xlarge '
'| '
'ml.m5d.24xlarge '
'| '
'ml.c4.large '
'| '
'ml.c4.xlarge '
'| '
'ml.c4.2xlarge '
'| '
'ml.c4.4xlarge '
'| '
'ml.c4.8xlarge '
'| '
'ml.p2.xlarge '
'| '
'ml.p2.8xlarge '
'| '
'ml.p2.16xlarge '
'| '
'ml.p3.2xlarge '
'| '
'ml.p3.8xlarge '
'| '
'ml.p3.16xlarge '
'| '
'ml.c5.large '
'| '
'ml.c5.xlarge '
'| '
'ml.c5.2xlarge '
'| '
'ml.c5.4xlarge '
'| '
'ml.c5.9xlarge '
'| '
'ml.c5.18xlarge '
'| '
'ml.c5d.large '
'| '
'ml.c5d.xlarge '
'| '
'ml.c5d.2xlarge '
'| '
'ml.c5d.4xlarge '
'| '
'ml.c5d.9xlarge '
'| '
'ml.c5d.18xlarge '
'| '
'ml.g4dn.xlarge '
'| '
'ml.g4dn.2xlarge '
'| '
'ml.g4dn.4xlarge '
'| '
'ml.g4dn.8xlarge '
'| '
'ml.g4dn.12xlarge '
'| '
'ml.g4dn.16xlarge '
'| '
'ml.r5.large '
'| '
'ml.r5.xlarge '
'| '
'ml.r5.2xlarge '
'| '
'ml.r5.4xlarge '
'| '
'ml.r5.12xlarge '
'| '
'ml.r5.24xlarge '
'| '
'ml.r5d.large '
'| '
'ml.r5d.xlarge '
'| '
'ml.r5d.2xlarge '
'| '
'ml.r5d.4xlarge '
'| '
'ml.r5d.12xlarge '
'| '
'ml.r5d.24xlarge '
'| '
'ml.inf1.xlarge '
'| '
'ml.inf1.2xlarge '
'| '
'ml.inf1.6xlarge '
'| '
'ml.inf1.24xlarge'],
'SupportedResponseMIMETypes': ['string'],
'SupportedTransformInstanceTypes': ['ml.m4.xlarge '
'| '
'ml.m4.2xlarge '
'| '
'ml.m4.4xlarge '
'| '
'ml.m4.10xlarge '
'| '
'ml.m4.16xlarge '
'| '
'ml.c4.xlarge '
'| '
'ml.c4.2xlarge '
'| '
'ml.c4.4xlarge '
'| '
'ml.c4.8xlarge '
'| '
'ml.p2.xlarge '
'| '
'ml.p2.8xlarge '
'| '
'ml.p2.16xlarge '
'| '
'ml.p3.2xlarge '
'| '
'ml.p3.8xlarge '
'| '
'ml.p3.16xlarge '
'| '
'ml.c5.xlarge '
'| '
'ml.c5.2xlarge '
'| '
'ml.c5.4xlarge '
'| '
'ml.c5.9xlarge '
'| '
'ml.c5.18xlarge '
'| '
'ml.m5.large '
'| '
'ml.m5.xlarge '
'| '
'ml.m5.2xlarge '
'| '
'ml.m5.4xlarge '
'| '
'ml.m5.12xlarge '
'| '
'ml.m5.24xlarge']},
'LastModifiedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'LastModifiedTime': 'timestamp',
'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'},
'ModelApprovalStatus': 'Approved | Rejected | '
'PendingManualApproval',
'ModelMetrics': {'Bias': {'Report': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'Explainability': {'Report': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'ModelDataQuality': {'Constraints': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'},
'Statistics': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}},
'ModelQuality': {'Constraints': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'},
'Statistics': {'ContentDigest': 'string',
'ContentType': 'string',
'S3Uri': 'string'}}},
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageName': 'string',
'ModelPackageStatus': 'Pending | InProgress | '
'Completed | Failed | '
'Deleting',
'ModelPackageStatusDetails': {'ImageScanStatuses': [{'FailureReason': 'string',
'Name': 'string',
'Status': 'NotStarted '
'| '
'InProgress '
'| '
'Completed '
'| '
'Failed'}],
'ValidationStatuses': [{'FailureReason': 'string',
'Name': 'string',
'Status': 'NotStarted '
'| '
'InProgress '
'| '
'Completed '
'| '
'Failed'}]},
'ModelPackageVersion': 'integer',
'SourceAlgorithmSpecification': {'SourceAlgorithms': [{'AlgorithmName': 'string',
'ModelDataUrl': 'string'}]},
'Tags': [{'Key': 'string', 'Value': 'string'}],
'ValidationSpecification': {'ValidationProfiles': [{'ProfileName': 'string',
'TransformJobDefinition': {'BatchStrategy': 'MultiRecord '
'| '
'SingleRecord',
'Environment': {'string': 'string'},
'MaxConcurrentTransforms': 'integer',
'MaxPayloadInMB': 'integer',
'TransformInput': {'CompressionType': 'None '
'| '
'Gzip',
'ContentType': 'string',
'DataSource': {'S3DataSource': {'S3DataType': 'ManifestFile '
'| '
'S3Prefix '
'| '
'AugmentedManifestFile',
'S3Uri': 'string'}},
'SplitType': 'None '
'| '
'Line '
'| '
'RecordIO '
'| '
'TFRecord'},
'TransformOutput': {'Accept': 'string',
'AssembleWith': 'None '
'| '
'Line',
'KmsKeyId': 'string',
'S3OutputPath': 'string'},
'TransformResources': {'InstanceCount': 'integer',
'InstanceType': 'ml.m4.xlarge '
'| '
'ml.m4.2xlarge '
'| '
'ml.m4.4xlarge '
'| '
'ml.m4.10xlarge '
'| '
'ml.m4.16xlarge '
'| '
'ml.c4.xlarge '
'| '
'ml.c4.2xlarge '
'| '
'ml.c4.4xlarge '
'| '
'ml.c4.8xlarge '
'| '
'ml.p2.xlarge '
'| '
'ml.p2.8xlarge '
'| '
'ml.p2.16xlarge '
'| '
'ml.p3.2xlarge '
'| '
'ml.p3.8xlarge '
'| '
'ml.p3.16xlarge '
'| '
'ml.c5.xlarge '
'| '
'ml.c5.2xlarge '
'| '
'ml.c5.4xlarge '
'| '
'ml.c5.9xlarge '
'| '
'ml.c5.18xlarge '
'| '
'ml.m5.large '
'| '
'ml.m5.xlarge '
'| '
'ml.m5.2xlarge '
'| '
'ml.m5.4xlarge '
'| '
'ml.m5.12xlarge '
'| '
'ml.m5.24xlarge',
'VolumeKmsKeyId': 'string'}}}],
'ValidationRole': 'string'}},
'ModelPackageGroup': {'CreatedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'CreationTime': 'timestamp',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageGroupStatus': 'Pending | '
'InProgress | '
'Completed | '
'Failed | '
'Deleting | '
'DeleteFailed',
'Tags': [{'Key': 'string',
'Value': 'string'}]},
'Pipeline': {'CreatedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'CreationTime': 'timestamp',
'LastModifiedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'LastModifiedTime': 'timestamp',
'LastRunTime': 'timestamp',
'PipelineArn': 'string',
'PipelineDescription': 'string',
'PipelineDisplayName': 'string',
'PipelineName': 'string',
'PipelineStatus': 'Active',
'RoleArn': 'string',
'Tags': [{'Key': 'string', 'Value': 'string'}]},
'PipelineExecution': {'CreatedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'CreationTime': 'timestamp',
'LastModifiedBy': {'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string'},
'LastModifiedTime': 'timestamp',
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDescription': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing | '
'Stopping | '
'Stopped | '
'Failed | '
'Succeeded',
'PipelineParameters': [{'Name': 'string',
'Value': 'string'}]},
'TrainingJob': {'SecondaryStatus': {'Updating'},
'SecondaryStatusTransitions': {'Status': {'Updating'}}},
'Trial': {'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'}},
'TrialComponent': {'MetadataProperties': {'CommitId': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string',
'Repository': 'string'},
'SourceDetail': {'ProcessingJob': {'ProcessingInputs': {'AppManaged': 'boolean',
'DatasetDefinition': {'AthenaDatasetDefinition': {'Catalog': 'string',
'Database': 'string',
'KmsKeyId': 'string',
'OutputCompression': 'GZIP '
'| '
'SNAPPY '
'| '
'ZLIB',
'OutputFormat': 'PARQUET '
'| '
'ORC '
'| '
'AVRO '
'| '
'JSON '
'| '
'TEXTFILE',
'OutputS3Uri': 'string',
'QueryString': 'string',
'WorkGroup': 'string'},
'DataDistributionType': 'FullyReplicated '
'| '
'ShardedByS3Key',
'InputMode': 'Pipe '
'| '
'File',
'LocalPath': 'string',
'RedshiftDatasetDefinition': {'ClusterId': 'string',
'ClusterRoleArn': 'string',
'Database': 'string',
'DbUser': 'string',
'KmsKeyId': 'string',
'OutputCompression': 'None '
'| '
'GZIP '
'| '
'BZIP2 '
'| '
'ZSTD '
'| '
'SNAPPY',
'OutputFormat': 'PARQUET '
'| '
'CSV',
'OutputS3Uri': 'string',
'QueryString': 'string'}}},
'ProcessingOutputConfig': {'Outputs': {'AppManaged': 'boolean',
'FeatureStoreOutput': {'FeatureGroupName': 'string'}}}},
'TrainingJob': {'SecondaryStatus': {'Updating'},
'SecondaryStatusTransitions': {'Status': {'Updating'}}}}}}}
Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
See also: AWS API Documentation
Request Syntax
client.search(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup',
SearchExpression={
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
],
'NestedFilters': [
{
'NestedPropertyName': 'string',
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
]
},
],
'SubExpressions': [
{'... recursive ...'},
],
'Operator': 'And'|'Or'
},
SortBy='string',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
[REQUIRED]
The name of the Amazon SageMaker resource to search for.
dict
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions , NestedFilters , and Filters that can be included in a SearchExpression object is 50.
Filters (list) --
A list of filter objects.
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
NestedFilters (list) --
A list of nested filter objects.
(dict) --
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',
'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'
NestedPropertyName (string) -- [REQUIRED]
The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .
Filters (list) -- [REQUIRED]
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
SubExpressions (list) --
A list of search expression objects.
(dict) --
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.
A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.
A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.
A Boolean operator: And or Or .
Operator (string) --
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .
string
The name of the resource property used to sort the SearchResults . The default is LastModifiedTime .
string
How SearchResults are ordered. Valid values are Ascending or Descending . The default is Descending .
string
If more than MaxResults resources match the specified SearchExpression , the response includes a NextToken . The NextToken can be passed to the next SearchRequest to continue retrieving results.
integer
The maximum number of results to return.
dict
Response Syntax
{
'Results': [
{
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'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',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating',
'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'
},
'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',
'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)
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Experiment': {
'ExperimentName': 'string',
'ExperimentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Trial': {
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
]
},
'TrialComponent': {
'TrialComponentName': 'string',
'DisplayName': 'string',
'TrialComponentArn': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
],
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'SourceDetail': {
'SourceArn': 'string',
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'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',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating',
'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'
},
'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',
'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)
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ProcessingJob': {
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'TransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'Parents': [
{
'TrialName': 'string',
'ExperimentName': 'string'
},
]
},
'Endpoint': {
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringSchedules': [
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
}
},
'EndpointName': 'string',
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelPackage': {
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string'
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ApprovalDescription': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelPackageGroup': {
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Pipeline': {
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'PipelineExecution': {
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
]
},
'FeatureGroup': {
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
'CreationTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Results (list) --
A list of SearchRecord objects.
(dict) --
A single resource returned as part of the Search API response.
TrainingJob (dict) --
The properties of a training job.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
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 AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
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 Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
RoleArn (string) --
The AWS 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. Amazon 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 Amazon 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 Amazon SageMaker uses to perform tasks on your behalf.
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon 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) --
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, Amazon 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 , Amazon 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. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// 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 master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon 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 AWS KMS in the AWS Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
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.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The AWS KMS key that Amazon 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"
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. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon 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 the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .
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 Amazon 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 DescribeTrainingJobResponse$SecondaryStatusTransitions . 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, Amazon 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.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the debug hook parameters, collection configuration, and storage paths.
LocalPath (string) --
Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for tensors.
HookParameters (dict) --
Configuration information for the debug hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for tensor collections.
(dict) --
Configuration information for 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:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for debugging 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 (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy for 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 TensorBoard output.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Tags (list) --
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Experiment (dict) --
The properties of an experiment.
ExperimentName (string) --
The name of the experiment.
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
Source (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
Description (string) --
The description of the experiment.
CreationTime (datetime) --
When the experiment was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the experiment was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Trial (dict) --
The properties of a trial.
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
Who last modified the trial.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Tags (list) --
The list of tags that are associated with the trial. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
TrialComponentSummaries (list) --
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
(dict) --
A short summary of a trial component.
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
TrialComponent (dict) --
The properties of a trial component.
TrialComponentName (string) --
The name of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Source (dict) --
The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the trial component.
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
SourceDetail (dict) --
Details of the source of the component.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TrainingJob (dict) --
Information about a training job that's the source of a trial component.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
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 AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
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 Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
RoleArn (string) --
The AWS 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. Amazon 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 Amazon 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 Amazon SageMaker uses to perform tasks on your behalf.
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon 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) --
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, Amazon 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 , Amazon 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. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// 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 master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon 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 AWS KMS in the AWS Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
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.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The AWS KMS key that Amazon 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"
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. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon 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 the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .
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 Amazon 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 DescribeTrainingJobResponse$SecondaryStatusTransitions . 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, Amazon 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.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the debug hook parameters, collection configuration, and storage paths.
LocalPath (string) --
Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for tensors.
HookParameters (dict) --
Configuration information for the debug hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for tensor collections.
(dict) --
Configuration information for 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:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for debugging 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 (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy for 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 TensorBoard output.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Tags (list) --
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
ProcessingJob (dict) --
Information about a processing job that's the source of a trial component.
ProcessingInputs (list) --
List of input configurations for the processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) --
The name of the inputs for the processing job.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for processing job inputs in Amazon S3.
S3Uri (string) --
The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.This is a required parameter when AppManaged is False (default).
S3DataDistributionType (string) --
Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key .
S3CompressionType (string) --
Whether to use Gzip compression for Amazon S3 storage.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) --
The name of the data catalog used in Athena query execution.
Database (string) --
The name of the database used in the Athena query execution.
QueryString (string) --
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) --
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) --
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) --
The Redshift cluster Identifier.
Database (string) --
The name of the Redshift database used in Redshift query execution.
DbUser (string) --
The database user name used in Redshift query execution.
QueryString (string) --
The SQL query statements to be executed.
ClusterRoleArn (string) --
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) --
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) --
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
ProcessingOutputConfig (dict) --
The output configuration for the processing job.
Outputs (list) --
List of output configurations for the processing job.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job. LocalPath is an absolute path to the input data.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) --
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
StoppingCondition (dict) --
Specifies a time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configuration to run a processing job in a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a processing job.
(string) --
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The ARN of the role used to create the processing job.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
ProcessingJobArn (string) --
The ARN of the processing job.
ProcessingJobStatus (string) --
The status of the processing job.
ExitMessage (string) --
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time that the processing job ended.
ProcessingStartTime (datetime) --
The time that the processing job started.
LastModifiedTime (datetime) --
The time the processing job was last modified.
CreationTime (datetime) --
The time the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of the training job associated with this processing job.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
TransformJob (dict) --
Information about a transform job that's the source of a trial component.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.
Completed - The job has completed.
Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.
Stopping - The transform job is stopping.
Stopped - The transform job has stopped.
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide .
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input .
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV files, Amazon SageMaker combines the transformed data with the input data at the end of the input data and stores it in the output file. The joined data has the joined input data followed by the transformed data and the output is a CSV file.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
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.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Tags (list) --
The list of tags that are associated with the component. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Parents (list) --
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
(dict) --
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
TrialName (string) --
The name of the trial.
ExperimentName (string) --
The name of the experiment.
Endpoint (dict) --
A hosted endpoint for real-time inference.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName (string) --
The endpoint configuration associated with the endpoint.
ProductionVariants (list) --
A list of the production variants hosted on the endpoint. Each production variant is a model.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
DataCaptureConfig (dict) --
EnableCapture (boolean) --
CaptureStatus (string) --
CurrentSamplingPercentage (integer) --
DestinationS3Uri (string) --
KmsKeyId (string) --
EndpointStatus (string) --
The status of the endpoint.
FailureReason (string) --
If the endpoint failed, the reason it failed.
CreationTime (datetime) --
The time that the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
MonitoringSchedules (list) --
A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor .
(dict) --
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor .
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule. This can be one of the following values.
PENDING - The schedule is pending being created.
FAILED - The schedule failed.
SCHEDULED - The schedule was successfully created.
STOPPED - The schedule was stopped.
FailureReason (string) --
If the monitoring schedule failed, the reason it failed.
CreationTime (datetime) --
The time that the monitoring schedule was created.
LastModifiedTime (datetime) --
The last time the monitoring schedule was changed.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
If you want to set the job to start every hour, please use the following: Hourly: cron(0 * ? * * *)
If you want to start the job daily: cron(0 [00-23] ? * * *)
For example, the following are valid cron expressions:
Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)
Note
Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defauts to FullyReplicated
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
(string) --
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
(string) --
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
EndpointName (string) --
The endpoint that hosts the model being monitored.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of teh endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
Tags (list) --
A list of the tags associated with the monitoring schedlue. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Tags (list) --
A list of the tags associated with the endpoint. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
ModelPackage (dict) --
A versioned model that can be deployed for SageMaker inference.
ModelPackageName (string) --
The name of the model.
ModelPackageGroupName (string) --
The model group to which the model belongs.
ModelPackageVersion (integer) --
The version number of a versioned model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
The description of the model package.
CreationTime (datetime) --
The time that the model package was created.
InferenceSpecification (dict) --
Defines how to perform inference generation after a training job is run.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The AWS Marketplace product ID of the model package.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SourceAlgorithmSpecification (dict) --
A list of algorithms that were used to create a model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on AWS Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
ModelPackageStatus (string) --
The status of the model package. This can be one of the following values.
PENDING - The model package is pending being created.
IN_PROGRESS - The model package is in the process of being created.
COMPLETED - The model package was successfully created.
FAILED - The model package failed.
DELETING - The model package is in the process of being deleted.
ModelPackageStatusDetails (dict) --
Specifies the validation and image scan statuses of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is to be certified to be listed on AWS Marketplace. For information about listing model packages on AWS Marketplace, see List Your Algorithm or Model Package on AWS Marketplace .
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
APPROVED - The model is approved
REJECTED - The model is rejected.
PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Constraints (dict) --
Model quality constraints.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) --
ContentDigest (string) --
S3Uri (string) --
LastModifiedTime (datetime) --
The last time the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ApprovalDescription (string) --
A description provided when the model approval is set.
Tags (list) --
A list of the tags associated with the model package. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
ModelPackageGroup (dict) --
A group of versioned models in the model registry.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
The description for the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ModelPackageGroupStatus (string) --
The status of the model group. This can be one of the following values.
PENDING - The model group is pending being created.
IN_PROGRESS - The model group is in the process of being created.
COMPLETED - The model group was successfully created.
FAILED - The model group failed.
DELETING - The model group is in the process of being deleted.
DELETE_FAILED - SageMaker failed to delete the model group.
Tags (list) --
A list of the tags associated with the model group. For more information, see Tagging AWS resources in the AWS General Reference Guide .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Pipeline (dict) --
A SageMaker Model Building Pipeline instance.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that created the pipeline.
PipelineStatus (string) --
The status of the pipeline.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
A list of tags that apply to the pipeline.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
PipelineExecution (dict) --
An execution of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline status.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
CreationTime (datetime) --
The creation time of the pipeline execution.
LastModifiedTime (datetime) --
The time that the pipeline execution was last modified.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
FeatureGroup (dict) --
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of a FeatureGroup .
FeatureGroupName (string) --
The name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions .
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
FeatureDefinitions (list) --
A list of Feature s. Each Feature must include a FeatureName and a FeatureType .
Valid FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time .
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CreationTime (datetime) --
The time a FeatureGroup was created.
OnlineStoreConfig (dict) --
Use this to specify the AWS Key Management Service (KMS) Key ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly; the default value is False .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The ID of the AWS Key Management Service (AWS KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
OfflineStoreConfig (dict) --
The configuration of an OfflineStore .
Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore .
To encrypt an OfflineStore using at rest data encryption, specify AWS Key Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig .
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The AWS Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an AWS Glue table when configuring an OfflineStore .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
FeatureGroupStatus (string) --
A FeatureGroup status.
OfflineStoreStatus (dict) --
The status of OfflineStore .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore .
Description (string) --
A free form description of a FeatureGroup .
Tags (list) --
Tags used to define a FeatureGroup .
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
NextToken (string) --
If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
{'DeploymentConfig': {'AutoRollbackConfiguration': {'Alarms': [{'AlarmName': 'string'}]},
'BlueGreenUpdatePolicy': {'MaximumExecutionTimeoutInSeconds': 'integer',
'TerminationWaitInSeconds': 'integer',
'TrafficRoutingConfiguration': {'CanarySize': {'Type': 'INSTANCE_COUNT '
'| '
'CAPACITY_PERCENT',
'Value': 'integer'},
'Type': 'ALL_AT_ONCE '
'| '
'CANARY',
'WaitIntervalInSeconds': 'integer'}}}}
Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
When Amazon SageMaker receives the request, it sets the endpoint status to Updating . After updating the endpoint, it sets the status to InService . To check the status of an endpoint, use the DescribeEndpoint API.
Note
You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig .
If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
See also: AWS API Documentation
Request Syntax
client.update_endpoint(
EndpointName='string',
EndpointConfigName='string',
RetainAllVariantProperties=True|False,
ExcludeRetainedVariantProperties=[
{
'VariantPropertyType': 'DesiredInstanceCount'|'DesiredWeight'|'DataCaptureConfig'
},
],
DeploymentConfig={
'BlueGreenUpdatePolicy': {
'TrafficRoutingConfiguration': {
'Type': 'ALL_AT_ONCE'|'CANARY',
'WaitIntervalInSeconds': 123,
'CanarySize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
}
},
'TerminationWaitInSeconds': 123,
'MaximumExecutionTimeoutInSeconds': 123
},
'AutoRollbackConfiguration': {
'Alarms': [
{
'AlarmName': 'string'
},
]
}
}
)
string
[REQUIRED]
The name of the endpoint whose configuration you want to update.
string
[REQUIRED]
The name of the new endpoint configuration.
boolean
When updating endpoint resources, enables or disables the retention of variant properties , such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set RetainAllVariantProperties to true . To use the variant properties specified in a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to false . The default is false .
list
When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties , whose value is set to true , ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig . If you don't specify a value for ExcludeAllVariantProperties , no variant properties are overridden.
(dict) --
Specifies a production variant property type for an Endpoint.
If you are updating an endpoint with the UpdateEndpointInput$RetainAllVariantProperties option set to true , the VariantProperty objects listed in UpdateEndpointInput$ExcludeRetainedVariantProperties override the existing variant properties of the endpoint.
VariantPropertyType (string) -- [REQUIRED]
The type of variant property. The supported values are:
DesiredInstanceCount : Overrides the existing variant instance counts using the ProductionVariant$InitialInstanceCount values in the CreateEndpointConfigInput$ProductionVariants .
DesiredWeight : Overrides the existing variant weights using the ProductionVariant$InitialVariantWeight values in the CreateEndpointConfigInput$ProductionVariants .
DataCaptureConfig : (Not currently supported.)
dict
The deployment configuration for the endpoint to be updated.
BlueGreenUpdatePolicy (dict) -- [REQUIRED]
TrafficRoutingConfiguration (dict) -- [REQUIRED]
Type (string) -- [REQUIRED]
WaitIntervalInSeconds (integer) -- [REQUIRED]
CanarySize (dict) --
Type (string) -- [REQUIRED]
This API is not supported.
Value (integer) -- [REQUIRED]
TerminationWaitInSeconds (integer) --
MaximumExecutionTimeoutInSeconds (integer) --
AutoRollbackConfiguration (dict) --
Alarms (list) --
(dict) --
This API is not supported.
AlarmName (string) --
dict
Response Syntax
{
'EndpointArn': 'string'
}
Response Structure
(dict) --
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.