Pipeline Structure and Execution

Pipeline Structure

An Amazon SageMaker Model Building Pipelines instance is composed of a name, parameters, and steps. Pipeline names must be unique within an (account, region) pair. All parameters used in step definitions must be defined in the pipeline. Pipeline steps listed automatically determine their order of execution by their data dependencies on one another. The SageMaker Pipelines service resolves the relationships between steps in the data dependency DAG to create a series of steps that the execution completes. The following is an example of a pipeline structure.

from sagemaker.workflow.pipeline import Pipeline
  
  pipeline_name = f"AbalonePipeline"
  pipeline = Pipeline(
      name=pipeline_name,
      parameters=[
          processing_instance_type, 
          processing_instance_count,
          training_instance_type,
          model_approval_status,
          input_data,
          batch_data,
      ],
      steps=[step_process, step_train, step_eval, step_cond],
  )

Pipeline Execution using Parallelism Configuration

By default, a pipeline performs all steps that are available to run in parallel. You can control this behavior by using the ParallelismConfiguration property when creating or updating a pipeline, as well as when starting or retrying a pipeline execution.

Parallelism configurations are applied per execution. For example, if two executions are started they can each run a maximum of 50 steps concurrently, for a total of 100 concurrently running steps. Also, ParallelismConfiguration(s) specified when starting, retrying or updating an execution take precedence over parallelism configurations defined in the pipeline.

pipeline = Pipeline(
        name="myPipeline",
        steps=[step_process, step_train]
    )

  pipeline.create(role, parallelism_config={"MaxParallelExecutionSteps": 50})