:doc:`SageMaker <../../sagemaker>` / Client / start_pipeline_execution

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start_pipeline_execution
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.. py:method:: SageMaker.Client.start_pipeline_execution(**kwargs)

  

  Starts a pipeline execution.

  

  See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StartPipelineExecution>`_  


  **Request Syntax**
  ::

    response = client.start_pipeline_execution(
        PipelineName='string',
        PipelineExecutionDisplayName='string',
        PipelineParameters=[
            {
                'Name': 'string',
                'Value': 'string'
            },
        ],
        PipelineExecutionDescription='string',
        ClientRequestToken='string',
        ParallelismConfiguration={
            'MaxParallelExecutionSteps': 123
        },
        SelectiveExecutionConfig={
            'SourcePipelineExecutionArn': 'string',
            'SelectedSteps': [
                {
                    'StepName': 'string'
                },
            ]
        },
        PipelineVersionId=123,
        MlflowExperimentName='string'
    )
    
  :type PipelineName: string
  :param PipelineName: **[REQUIRED]** 

    The name or Amazon Resource Name (ARN) of the pipeline.

    

  
  :type PipelineExecutionDisplayName: string
  :param PipelineExecutionDisplayName: 

    The display name of the pipeline execution.

    

  
  :type PipelineParameters: list
  :param PipelineParameters: 

    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.

        

      
    

  :type PipelineExecutionDescription: string
  :param PipelineExecutionDescription: 

    The description of the pipeline execution.

    

  
  :type ClientRequestToken: string
  :param ClientRequestToken: **[REQUIRED]** 

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

    This field is autopopulated if not provided.

  
  :type ParallelismConfiguration: dict
  :param ParallelismConfiguration: 

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.

    

  
    - **MaxParallelExecutionSteps** *(integer) --* **[REQUIRED]** 

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

      

    
  
  :type SelectiveExecutionConfig: dict
  :param SelectiveExecutionConfig: 

    The selective execution configuration applied to the pipeline run.

    

  
    - **SourcePipelineExecutionArn** *(string) --* 

      The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either ``Failed`` or ``Success``.

       

      This field is required if the steps you specify for ``SelectedSteps`` depend on output collaterals from any non-specified pipeline steps. For more information, see `Selective Execution for Pipeline Steps <https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-selective-ex.html>`__.

      

    
    - **SelectedSteps** *(list) --* **[REQUIRED]** 

      A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.

      

    
      - *(dict) --* 

        A step selected to run in selective execution mode.

        

      
        - **StepName** *(string) --* **[REQUIRED]** 

          The name of the pipeline step.

          

        
      
  
  
  :type PipelineVersionId: integer
  :param PipelineVersionId: 

    The ID of the pipeline version to start execution from.

    

  
  :type MlflowExperimentName: string
  :param MlflowExperimentName: 

    The MLflow experiment name of the pipeline execution.

    

  
  
  :rtype: dict
  :returns: 
    
    **Response Syntax**

    
    ::

      {
          'PipelineExecutionArn': 'string'
      }
      
    **Response Structure**

    

    - *(dict) --* 
      

      - **PipelineExecutionArn** *(string) --* 

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

        
  
  **Exceptions**
  
  *   :py:class:`SageMaker.Client.exceptions.ConflictException`

  
  *   :py:class:`SageMaker.Client.exceptions.ResourceNotFound`

  
  *   :py:class:`SageMaker.Client.exceptions.ResourceLimitExceeded`

  