# Pipeline Structure and Execution
<a name="build-and-manage-pipeline"></a>

**Topics**
+ [Pipeline Structure](#build-and-manage-pipeline-structure)
+ [Pipeline Execution using Parallelism Configuration](#build-and-manage-pipeline-execution)

## Pipeline Structure
<a name="build-and-manage-pipeline-structure"></a>

An Amazon SageMaker 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 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.

**Warning**  
When building a pipeline through the visual editor or SageMaker AI Python SDK, do not include sensitive information in pipeline parameters or any step definition field (such as environment variables). These fields will be visible in the future when returned in a `DescribePipeline` request.

```
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
<a name="build-and-manage-pipeline-execution"></a>

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.

**Example Creating a pipeline execution with `ParallelismConfiguration`**  

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

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