Notebooks as Controllers

Estimated time: 30 minutes

Overview

Questions

• How do you set up and use Vertex AI Workbench notebooks for machine learning tasks?
  
• How can you manage compute resources efficiently using a “controller” notebook approach in GCP?

Objectives

• Describe how to use Vertex AI Workbench notebooks for ML workflows.
  
• Set up a Jupyter-based Workbench instance as a controller to manage compute tasks.
  
• Use the Vertex AI SDK to launch training and tuning jobs on scalable instances.

Setting up our notebook environment

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Google Cloud Workbench provides JupyterLab-based environments that can be used to orchestrate machine learning workflows. In this workshop, we will use a Workbench Instance—the recommended option going forward, as other Workbench environments are being deprecated.

Workbench Instances come with JupyterLab 3 pre-installed and are configured with GPU-enabled ML frameworks (TensorFlow, PyTorch, etc.), making it easy to start experimenting without additional setup. Learn more in the Workbench Instances documentation.

Using the notebook as a controller

The notebook instance functions as a controller to manage more resource-intensive tasks. By selecting a modest machine type (e.g., n1-standard-4), you can perform lightweight operations locally in the notebook while using the Vertex AI Python SDK to launch compute-heavy jobs on larger machines (e.g., GPU-accelerated) when needed.

This approach minimizes costs while giving you access to scalable infrastructure for demanding tasks like model training, batch prediction, and hyperparameter tuning.

We will follow these steps to create our first Workbench Instance:

1. Navigate to Workbench

• In the Google Cloud Console, search for “Workbench.”
  
• Click the “Instances” tab (this is the supported path going forward).
  
• Pin Workbench to your navigation bar for quick access.

2. Create a new Workbench Instance

• Click “Create New” under Instances.
  
• Notebook name: For this workshop, we can use the following naming convention to easily locate our notebooks: lastname-titanic
• Region: Choose the same region as your storage bucket (e.g., us-central1).
  • This avoids cross-region transfer charges and keeps data access latency low.
    
• GPUs: Leave disabled for now (training jobs will request them separately).
  
• Labels: Add labels for cost tracking
  • purpose=workshop
  • owner=lastname_firstname
• Machine type: Select a small machine (e.g., e2-standard-2) to act as the controller.
  • This keeps costs low while you delegate heavy lifting to training jobs.
    
  • For guidance on common machine types for ML, refer to Instances for ML on GCP.
• Click Create to create the intance. Your notebook instance will start in a few minutes. When its status is “Running,” you can open JupyterLab and begin working.

Managing training and tuning with the controller notebook

In the following episodes, we will use the Vertex AI Python SDK (google-cloud-aiplatform) from this notebook to submit compute-heavy tasks on more powerful machines. Examples include:

• Training a model on a GPU-backed instance.
  
• Running hyperparameter tuning jobs managed by Vertex AI.

This pattern keeps costs low by running your notebook on a modest VM while only incurring charges for larger resources when they are actively in use.

Challenge

Challenge: Notebook Roles

Your university provides different compute options: laptops, on-prem HPC, and GCP.

• What role does a Workbench Instance notebook play compared to an HPC login node or a laptop-based JupyterLab?
  
• Which tasks should stay in the notebook (lightweight control, visualization) versus being launched to larger cloud resources?

Show me the solution

The notebook serves as a lightweight control plane.
- Like an HPC login node, it is not meant for heavy computation.
- Suitable for small preprocessing, visualization, and orchestrating jobs.
- Resource-intensive tasks (training, tuning, batch jobs) should be submitted to scalable cloud resources (GPU/large VM instances) via the Vertex AI SDK.

Key Points

• Use a small Workbench Instance notebook as a controller to manage larger, resource-intensive tasks.
  
• Always navigate to the “Instances” tab in Workbench, since older notebook types are deprecated.
  
• Choose the same region for your Workbench Instance and storage bucket to avoid extra transfer costs.
  
• Submit training and tuning jobs to scalable instances using the Vertex AI SDK.
  
• Labels help track costs effectively, especially in shared or multi-project environments.
  
• Workbench Instances come with JupyterLab 3 and GPU frameworks preinstalled, making them an easy entry point for ML workflows.
  
• Enable idle auto-stop to avoid unexpected charges when notebooks are left running.