Developing a custom training application and creating a model using custom training.
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Training code requirements
Describes requirements to consider as you write training code.
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Understanding the custom training service on Vertex AI
Describes the lifecycle of a training cluster during a distributed training job, and explains how the Vertex AI custom training service handles errors.
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Using managed datasets
Describes how to use Vertex AI managed datasets with custom training.
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Configuring container settings for custom training
Describes the fields of the Vertex AI API that you must specify to configure training container settings for either a custom container or a Python training application that runs on a pre-built container.
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Configuring compute resources for custom training
Describes the different compute resources that you can use for custom training and how to configure them.
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Creating a Python training application for a pre-built container
How to create a Python source distribution that contains your training application and upload it to a Cloud Storage bucket.
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Pre-built containers for custom training
Provides a list of the pre-built containers for training, and describes how to use them with a Python training application.
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Choosing a custom training method.
Provides an overview and comparison of the different ways you can run custom training.
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Creating custom training jobs
How to create custom training jobs to run your custom training applications on Vertex AI.
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Creating training pipelines
How to create training pipelines to run custom training applications on Vertex AI