Google Professional Machine Learning Engineer Exam - Topic 4 Question 124 Discussion

AI Platform supports hyperparameter tuning for PyTorch models using custom containers. This allows you to use any Python dependencies and libraries that are not included in the pre-built AI Platform Training runtime versions. You can also use a pre-trained model such as ResNet as a base for your custom model. To run a hyperparameter tuning job on AI Platform using custom containers, you need to do the following steps:

Create a Dockerfile that defines the container image for your training application. The Dockerfile should install PyTorch and any other dependencies, copy your training code and configuration files, and set the entrypoint for the container.

Build the container image and push it to Container Registry or another accessible registry.

Create a YAML file that defines the configuration for your hyperparameter tuning job. The YAML file should specify the container image URI, the training input and output paths, the hyperparameters to tune, the metric to optimize, and the tuning algorithm and budget.

Submit the hyperparameter tuning job to AI Platform using the gcloud command-line tool or the AI Platform Training API.

Hyperparameter tuning overview

Using custom containers

PyTorch on AI Platform Training