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CustomJob components

Custom training jobs let you run your custom machine learning (ML) training code in Vertex AI.

`CustomTrainingJobOp`

The CustomTrainingJobOp component exposes the full functionalities of the CustomJob resource, to allow both single and distributed training using a ContainerSpec or PythonPackageSpec instance.

`create_custom_training_job_from_component` function

The create_custom_training_job_from_component utility converts a given container or Python component to a component that runs a custom job in Vertex AI. This simplifies the creation of custom training jobs. All inputs and outputs of the supplied component will be copied over to the constructed training job operator.

Note that this utility constructs a ClusterSpec, where the primary and all the workers use the same specification, meaning all disk and machine specification-related parameters will apply to all replicas. This is suitable for use cases where, for example, you are training with MultiWorkerMirroredStrategy or MirroredStrategy.

This component does not support CustomJob Python package training, or distributed training with different worker pool specs.

API reference

For component reference, see the Google Cloud Pipeline Components SDK reference for CustomJob components.
For Vertex AI API reference, see the CustomJob resource page.

Version history and release notes

To learn more about the version history and changes to the Google Cloud Pipeline Components SDK, see the Google Cloud Pipeline Components SDK Release Notes.

Technical support contacts

If you have any questions, reach out to kubeflow-pipelines-components@google.com.