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training
Run hyperparameter tuning for a TensorFlow model
Learn how to run a Vertex AI Hyperparameter Tuning job for a TensorFlow model.
The steps performed include:
* Modify training application code for automated hyperparameter tuning.
* Containerize training application code.
* Configure and launch a hyperparameter tuning job with the Vertex AI Python SDK.
Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers.
The steps performed include:
- Setting up your GCP project : Setting up the PROJECT_ID, REGION & SERVICE_ACCOUNT
- Creating a cloud storage bucket
- Building Custom Container using Artifact Registry and Docker
- Create a Vertex AI tensorboard instance to store your Vertex AI experiment
- Run a Vertex AI SDK CustomContainerTrainingJob
Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers.
The steps performed include:
- Setting up your GCP project : Setting up the PROJECT_ID, REGION & SERVICE_ACCOUNT
- Creating a cloud storage bucket
- Building Custom Container using Artifact Registry and Docker
- Create a Vertex AI Tensorboard Instance to store your Vertex AI experiment
- Run a Vertex AI SDK CustomContainerTrainingJob
Create a distributed custom training job
Learn how to create a distributed training job using Vertex AI SDK for Python.
The steps performed include:
- Configure the `PROJECT_ID` and `REGION` variables for your Google Cloud project.
- Create a Cloud Storage bucket to store your model artifacts.
- Build a custom Docker container that hosts your training code and push the container image to Artifact Registry.
- Run a Vertex AI SDK CustomContainerTrainingJob