Caching

Getting started with Kubeflow Pipelines step caching

Starting from Kubeflow Pipelines 0.4, Kubeflow Pipelines supports step caching capabilities in both standalone deployment and AI Platform Pipelines.

Before you start

This guide tells you the basic concepts of Kubeflow Pipelines step caching and how to use it. This guide assumes that you already have Kubeflow Pipelines installed or want to use options in the Kubeflow Pipelines deployment guide to deploy Kubeflow Pipelines.

What is step caching?

Kubeflow Pipelines caching provides step-level output caching. And caching is enabled by default for all pipelines submitted through the KFP backend and UI. The exception is pipelines authored using TFX SDK which has its own caching mechanism. The cache key calculation is based on the component (base image, command-line, code), arguments passed to the component (values or artifacts) and any additional customizations. If the component is exactly the same and the arguments are exactly the same as in some previous execution, then the task can be skipped and the outputs of the old step can be used. The cache reuse behavior can be controlled and the pipeline author can specify the maximum staleness of the cached data considered for reuse. With caching enabled, the system can skip a step that has already been executed which saves time and money.

Disabling/enabling caching

Cache is enabled by default after Kubeflow Pipelines 0.4. These are instructions on disabling and enabling cache service:

Configure access to your Kubeflow cluster

Use the following instructions to configure kubectl with access to your Kubeflow cluster.

  1. To check if you have kubectl installed, run the following command:

    which kubectl
    

    The response should be something like this:

    /usr/bin/kubectl
    

    If you do not have kubectl installed, follow the instructions in the guide to installing and setting up kubectl.

  2. Follow the guide to configuring access to Kubernetes clusters.

Disabling caching in your Kubeflow Pipelines deployment:

  1. Make sure mutatingwebhookconfiguration exists in your cluster:

    export NAMESPACE=<Namespace where KFP is installed>
    kubectl get mutatingwebhookconfiguration cache-webhook-${NAMESPACE}
    
  2. Change mutatingwebhookconfiguration rules:

    kubectl patch mutatingwebhookconfiguration cache-webhook-${NAMESPACE} --type='json' -p='[{"op":"replace", "path": "/webhooks/0/rules/0/operations/0", "value": "DELETE"}]'
    

Enabling caching

  1. Make sure mutatingwebhookconfiguration exists in your cluster:

    export NAMESPACE=<Namespace where KFP is installed>
    kubectl get mutatingwebhookconfiguration cache-webhook-${NAMESPACE}
    
  2. Change back mutatingwebhookconfiguration rules:

    kubectl patch mutatingwebhookconfiguration cache-webhook-${NAMESPACE} --type='json' -p='[{"op":"replace", "path": "/webhooks/0/rules/0/operations/0", "value": "CREATE"}]'
    

Managing caching staleness

The cache is enabled by default and if you ever executed same component with the same arguments, any new execution of the component will be skipped and the outputs will be taken from the cache. For some scenarios, the cached output data of some components might become too stale for use after some time. To control the maximum staleness of the reused cached data, you can set the step’s max_cache_staleness parameter. The max_cache_staleness is in RFC3339 Duration format (so 30 days = “P30D”). By default the max_cache_staleness is set to infinity so any old cached data will be reused.

Set max_cache_staleness to 30 days for a step:

def some_pipeline():
      # task is a target step in a pipeline
      task = some_op()
      task.execution_options.caching_strategy.max_cache_staleness = "P30D"

Ideally, the component code should be pure and deterministic in the sense that it produces same outputs given same inputs. If your component is not deterministic (for example, it returns a different random number on every invocation) you might want to disable caching for the tasks created from this component by setting max_cache_staleness to 0:

def some_pipeline():
      # task is a target step in a pipeline
      task_never_use_cache = some_op()
      task_never_use_cache.execution_options.caching_strategy.max_cache_staleness = "P0D"

A better solution would be to make the component deterministic. If the component uses random number generation, you can expose the RNG seed as a component input. If the component fetches some changing data you can add a timestamp or date input.

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Last modified September 15, 2022: Pipelines v2 content: KFP SDK (#3346) (3f6a118c)