# Run a pipeline
<a name="pipelines-step-decorator-run-pipeline"></a>

The following page describes how to run a pipeline with Amazon SageMaker Pipelines, either with SageMaker AI resources or locally.

Start a new pipeline run with the `pipeline.start()` function as you would for a traditional SageMaker AI pipeline run. For information about the `start()` function, see [sagemaker.workflow.pipeline.Pipeline.start](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.pipeline.Pipeline.start).

**Note**  
A step defined using the `@step` decorator runs as a training job. Therefore, be aware of the following limits:  
Instance limits and training job limits in your accounts. Update your limits accordingly to avoid any throttling or resource limit issues.
The monetary costs associated with every run of a training step in the pipeline. For more details, refer to [Amazon SageMaker Pricing](https://aws.amazon.com/sagemaker/pricing/).

## Retrieve results from a pipeline run locally
<a name="pipelines-step-decorator-run-pipeline-retrieve"></a>

To view the result of any step of a pipeline run, use [execution.result()](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.pipeline._PipelineExecution.result           ), as shown in the following snippet:

```
execution = pipeline.start()
execution.result(step_name="train")
```

**Note**  
Pipelines does not support `execution.result()` in local mode.

You can only retrieve results for one step at a time. If the step name was generated by SageMaker AI, you can retrieve the step name by calling `list_steps` as follows:

```
execution.list_step()
```

## Run a pipeline locally
<a name="pipelines-step-decorator-run-pipeline-local"></a>

You can run a pipeline with `@step`-decorated steps locally as you would for traditional pipeline steps. For details about local mode pipeline runs, see [Run pipelines using local mode](pipelines-local-mode.md). To use local mode, provide a `LocalPipelineSession` instead of a `SageMakerSession` to your pipeline definition, as shown in the following example:

```
from sagemaker.workflow.function_step import step
from sagemaker.workflow.pipeline import Pipeline
from sagemaker.workflow.pipeline_context import LocalPipelineSession

@step
def train():
    training_data = s3.download(....)
    ...
    return trained_model
    
step_train_result = train()

local_pipeline_session = LocalPipelineSession()

local_pipeline = Pipeline(
    name="<pipeline-name>",
    steps=[step_train_result],
    sagemaker_session=local_pipeline_session # needed for local mode
)

local_pipeline.create(role_arn="role_arn")

# pipeline runs locally
execution = local_pipeline.start()
```