Developing a custom training application and creating a model using custom training.
Training code requirements
Describes requirements to consider as you write training code.
Containerizing and running code locally
Describes how to use the
gcloudtool to containerize and run training code on your local computer.
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.
Using managed datasets
Describes how to use Vertex AI managed datasets with custom training.
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.
Configuring compute resources for custom training
Describes the different compute resources that you can use for custom training and how to configure them.
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.
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.
Choosing a custom training method.
Provides an overview and comparison of the different ways you can run custom training.
Creating custom training jobs
How to create custom training jobs to run your custom training applications on Vertex AI.
Creating training pipelines
How to create training pipelines to run custom training applications on Vertex AI