Introduction to the Pipelines SDK

The Kubeflow Pipelines SDK provides a set of Python packages that you can use to specify and run your machine learning (ML) workflows. A pipeline is a description of an ML workflow, including all of the components that make up the steps in the workflow and how the components interact with each other.

Note: The SDK documentation here refers to Kubeflow Pipelines with Argo which is the default. If you are running Kubeflow Pipelines with Tekton instead, please follow the Kubeflow Pipelines SDK for Tekton documentation.

SDK packages

The Kubeflow Pipelines SDK includes the following packages:

• kfp.compiler includes classes and methods for compiling pipeline Python DSL into a workflow yaml spec Methods in this package include, but are not limited to, the following:

  • kfp.compiler.Compiler.compile compiles your Python DSL code into a single static configuration (in YAML format) that the Kubeflow Pipelines service can process. The Kubeflow Pipelines service converts the static configuration into a set of Kubernetes resources for execution.

• kfp.components includes classes and methods for interacting with pipeline components. Methods in this package include, but are not limited to, the following:

  • kfp.components.func_to_container_op converts a Python function to a pipeline component and returns a factory function. You can then call the factory function to construct an instance of a pipeline task (ContainerOp) that runs the original function in a container.

  • kfp.components.load_component_from_file loads a pipeline component from a file and returns a factory function. You can then call the factory function to construct an instance of a pipeline task (ContainerOp) that runs the component container image.

  • kfp.components.load_component_from_url loads a pipeline component from a URL and returns a factory function. You can then call the factory function to construct an instance of a pipeline task (ContainerOp) that runs the component container image.

• kfp.dsl contains the domain-specific language (DSL) that you can use to define and interact with pipelines and components. Methods, classes, and modules in this package include, but are not limited to, the following:

  • kfp.dsl.PipelineParam represents a pipeline parameter that you can pass from one pipeline component to another. See the guide to pipeline parameters.

  • kfp.dsl.component is a decorator for DSL functions that returns a pipeline component. (ContainerOp).

  • kfp.dsl.pipeline is a decorator for Python functions that returns a pipeline.

  • kfp.dsl.python_component is a decorator for Python functions that adds pipeline component metadata to the function object.

  • kfp.dsl.types contains a list of types defined by the Kubeflow Pipelines SDK. Types include basic types like String, Integer, Float, and Bool, as well as domain-specific types like GCPProjectID and GCRPath. See the guide to DSL static type checking.

  • kfp.dsl.ResourceOp represents a pipeline task (op) which lets you directly manipulate Kubernetes resources (create, get, apply, …).

  • kfp.dsl.VolumeOp represents a pipeline task (op) which creates a new PersistentVolumeClaim (PVC). It aims to make the common case of creating a PersistentVolumeClaim fast.

  • kfp.dsl.VolumeSnapshotOp represents a pipeline task (op) which creates a new VolumeSnapshot. It aims to make the common case of creating a VolumeSnapshot fast.

  • kfp.dsl.PipelineVolume represents a volume used to pass data between pipeline steps. ContainerOps can mount a PipelineVolume either via the constructor’s argument pvolumes or add_pvolumes() method.

  • kfp.dsl.ParallelFor represents a parallel for loop over a static or dynamic set of items in a pipeline. Each iteration of the for loop is executed in parallel.

  • kfp.dsl.ExitHandler represents an exit handler that is invoked upon exiting a pipeline. A typical usage of ExitHandler is garbage collection.

  • kfp.dsl.Condition represents a group of ops, that will only be executed when a certain condition is met. The condition specified need to be determined at runtime, by incorporating at least one task output, or PipelineParam in the boolean expression.

• kfp.Client contains the Python client libraries for the Kubeflow Pipelines API. Methods in this package include, but are not limited to, the following:

  • kfp.Client.create_experiment creates a pipeline experiment and returns an experiment object.
  • kfp.Client.run_pipeline runs a pipeline and returns a run object.
  • kfp.Client.create_run_from_pipeline_func compiles a pipeline function and submits it for execution on Kubeflow Pipelines.
  • kfp.Client.create_run_from_pipeline_package runs a local pipeline package on Kubeflow Pipelines.
  • kfp.Client.upload_pipeline uploads a local file to create a new pipeline in Kubeflow Pipelines.
  • kfp.Client.upload_pipeline_version uploads a local file to create a pipeline version. Follow an example to learn more about creating a pipeline version.

• Kubeflow Pipelines extension modules include classes and functions for specific platforms on which you can use Kubeflow Pipelines. Examples include utility functions for on premises, Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure.

• Kubeflow Pipelines diagnose_me modules include classes and functions that help with environment diagnostic tasks.

  • kfp.cli.diagnose_me.dev_env reports on diagnostic metadata from your development environment, such as your python library version.
  • kfp.cli.diagnose_me.kubernetes_cluster reports on diagnostic data from your Kubernetes cluster, such as Kubernetes secrets.
  • kfp.cli.diagnose_me.gcp reports on diagnostic data related to your GCP environment.

Kubeflow Pipelines CLI tool

The Kubeflow Pipelines CLI tool enables you to use a subset of the Kubeflow Pipelines SDK directly from the command line. The Kubeflow Pipelines CLI tool provides the following commands:

• kfp diagnose_me runs environment diagnostic with specified parameters.

  • --json - Indicates that this command must return its results as JSON. Otherwise, results are returned in human readable format.
  • --namespace TEXT - Specifies the Kubernetes namespace to use. all-namespaces is the default value.
  • --project-id TEXT - For GCP deployments, this value specifies the GCP project to use. If this value is not specified, the environment default is used.

• kfp pipeline <COMMAND> provides the following commands to help you manage pipelines.

  • get - Gets detailed information about a Kubeflow pipeline from your Kubeflow Pipelines cluster.
  • list - Lists the pipelines that have been uploaded to your Kubeflow Pipelines cluster.
  • upload - Uploads a pipeline to your Kubeflow Pipelines cluster.

• kfp run <COMMAND> provides the following commands to help you manage pipeline runs.

  • get - Displays the details of a pipeline run.
  • list - Lists recent pipeline runs.
  • submit - Submits a pipeline run.

• kfp --endpoint <ENDPOINT> - Specifies the endpoint that the Kubeflow Pipelines CLI should connect to.

Installing the SDK

Follow the guide to installing the Kubeflow Pipelines SDK.

Building pipelines and components

This section summarizes the ways you can use the SDK to build pipelines and components.

A Kubeflow pipeline is a portable and scalable definition of an ML workflow. Each step in your ML workflow, such as preparing data or training a model, is an instance of a pipeline component.

Learn more about building pipelines.

A pipeline component is a self-contained set of code that performs one step in your ML workflow. Components are defined in a component specification, which defines the following:

• The component’s interface, its inputs and outputs.
• The component’s implementation, the container image and the command to execute.
• The component’s metadata, such as the name and description of the component.

Use the following options to create or reuse pipeline components.

• You can build components by defining a component specification for a containerized application.

  Learn more about building pipeline components.

• Lightweight Python function-based components make it easier to build a component by using the Kubeflow Pipelines SDK to generate the component specification for a Python function.

  Learn how to build a Python function-based component.

• You can reuse prebuilt components in your pipeline.

  Learn more about reusing prebuilt components.

Next steps

• Learn how to write recursive functions in the DSL.
• Build a pipeline component.
• Find out how to use the DSL to manipulate Kubernetes resources dynamically as steps of your pipeline.