# Best Practices
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The following sections suggest best practices to follow when you use the `@step` decorator for your pipeline steps.

## Use warm pools
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For faster pipeline step runs, use the warm pooling functionality provided for training jobs. You can turn on the warm pool functionality by providing the `keep_alive_period_in_seconds` argument to the `@step` decorator as demonstrated in the following snippet:

```
@step(
   keep_alive_period_in_seconds=900
)
```

For more information about warm pools, see [SageMaker AI Managed Warm Pools](train-warm-pools.md). 

## Structure your directory
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You are advised to use code modules while using the `@step` decorator. Put the `pipeline.py` module, in which you invoke the step functions and define the pipeline, at the root of the workspace. The recommended structure is shown as follows:

```
.
├── config.yaml # the configuration file that define the infra settings
├── requirements.txt # dependencies
├── pipeline.py  # invoke @step-decorated functions and define the pipeline here
├── steps/
| ├── processing.py
| ├── train.py
├── data/
├── test/
```