create_pipeline

create_pipeline(**kwargs)

Creates a pipeline using a JSON pipeline definition.

See also: AWS API Documentation

Request Syntax

response = client.create_pipeline(
    PipelineName='string',
    PipelineDisplayName='string',
    PipelineDefinition='string',
    PipelineDefinitionS3Location={
        'Bucket': 'string',
        'ObjectKey': 'string',
        'VersionId': 'string'
    },
    PipelineDescription='string',
    ClientRequestToken='string',
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ParallelismConfiguration={
        'MaxParallelExecutionSteps': 123
    }
)

Parameters

• PipelineName (string) --

  [REQUIRED]

  The name of the pipeline.

• PipelineDisplayName (string) -- The display name of the pipeline.
• PipelineDefinition (string) -- The JSON pipeline definition of the pipeline.
• PipelineDefinitionS3Location (dict) --

  The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

  • Bucket (string) -- [REQUIRED]

    Name of the S3 bucket.

  • ObjectKey (string) -- [REQUIRED]

    The object key (or key name) uniquely identifies the object in an S3 bucket.

  • VersionId (string) --

    Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.

• PipelineDescription (string) -- A description of the pipeline.
• ClientRequestToken (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.

• RoleArn (string) --

  [REQUIRED]

  The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.

• Tags (list) --

  A list of tags to apply to the created pipeline.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

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

    • Value (string) -- [REQUIRED]

      The tag value.

• ParallelismConfiguration (dict) --

  This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.

  • MaxParallelExecutionSteps (integer) -- [REQUIRED]

    The max number of steps that can be executed in parallel.

Return type

dict

Returns

Response Syntax

{
    'PipelineArn': 'string'
}

Response Structure

• (dict) --

  • PipelineArn (string) --

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

Exceptions

• SageMaker.Client.exceptions.ResourceNotFound
• SageMaker.Client.exceptions.ResourceLimitExceeded