# Turn on step caching
<a name="pipelines-caching-enabling"></a>

To turn on step caching, you must add a `CacheConfig` property to the step definition. `CacheConfig` properties use the following format in the pipeline definition file:

```
{
    "CacheConfig": {
        "Enabled": false,
        "ExpireAfter": "<time>"
    }
}
```

The `Enabled` field indicates whether caching is turned on for the particular step. You can set the field to `true`, which tells SageMaker AI to try to find a previous run of the step with the same attributes. Or, you can set the field to `false`, which tells SageMaker AI to run the step every time the pipeline runs. `ExpireAfter` is a string in [ISO 8601 duration](https://en.wikipedia.org/wiki/ISO_8601#Durations) format that defines the timeout period. The `ExpireAfter` duration can be a year, month, week, day, hour, or minute value. Each value consists of a number followed by a letter indicating the unit of duration. For example:
+ "30d" = 30 days
+ "5y" = 5 years
+ "T16m" = 16 minutes
+ "30dT5h" = 30 days and 5 hours.

The following discussion describes the procedure to turn on caching for new or pre-existing pipelines using the Amazon SageMaker Python SDK.

**Turn on caching for new pipelines**

For new pipelines, initialize a `CacheConfig` instance with `enable_caching=True` and provide it as an input to your pipeline step. The following example turns on caching with a 1-hour timeout period for a training step: 

```
from sagemaker.workflow.pipeline_context import PipelineSession
from sagemaker.workflow.steps import CacheConfig
      
cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")
estimator = Estimator(..., sagemaker_session=PipelineSession())

step_train = TrainingStep(
    name="TrainAbaloneModel",
    step_args=estimator.fit(inputs=inputs),
    cache_config=cache_config
)
```

**Turn on caching for pre-existing pipelines**

To turn on caching for pre-existing, already-defined pipelines, turn on the `enable_caching` property for the step, and set `expire_after` to a timeout value. Lastly, update the pipeline with `pipeline.upsert()` or `pipeline.update()`. Once you run it again, the following code example turns on caching with a 1-hour timeout period for a training step:

```
from sagemaker.workflow.pipeline_context import PipelineSession
from sagemaker.workflow.steps import CacheConfig
from sagemaker.workflow.pipeline import Pipeline

cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")
estimator = Estimator(..., sagemaker_session=PipelineSession())

step_train = TrainingStep(
    name="TrainAbaloneModel",
    step_args=estimator.fit(inputs=inputs),
    cache_config=cache_config
)

# define pipeline
pipeline = Pipeline(
    steps=[step_train]
)

# additional step for existing pipelines
pipeline.update()
# or, call upsert() to update the pipeline
# pipeline.upsert()
```

Alternatively, update the cache config after you have already defined the (pre-existing) pipeline, allowing one continuous code run. The following code sample demonstrates this method:

```
# turn on caching with timeout period of one hour
pipeline.steps[0].cache_config.enable_caching = True 
pipeline.steps[0].cache_config.expire_after = "PT1H" 

# additional step for existing pipelines
pipeline.update()
# or, call upsert() to update the pipeline
# pipeline.upsert()
```

For more detailed code examples and a discussion about how Python SDK parameters affect caching, see [ Caching Configuration](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_building_pipeline.html#caching-configuration) in the Amazon SageMaker Python SDK documentation.