Use TensorFlow Enterprise with Notebooks

This page provides a brief overview of Notebooks and describes how to get started using TensorFlow Enterprise with Notebooks.

In this example, you create a TensorFlow Enterprise instance of Notebooks, open a JupyterLab notebook, and run a classification tutorial on using neural networks with Keras.

Overview of Notebooks

User-managed notebooks let you create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab.

User-managed notebooks instances have a pre-installed suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can configure either CPU-only or GPU-enabled instances, to best suit your needs.

Your user-managed notebooks instances are protected by Google Cloud authentication and authorization, and are available using a user-managed notebooks instance URL. User-managed notebooks instances also integrate with GitHub so that you can easily sync your notebook with a GitHub repository.

Before you begin

Before you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.
  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

  4. Enable the Notebooks API.

    Enable the API

  5. In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  6. Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

  7. Enable the Notebooks API.

    Enable the API

Create a Notebooks instance

To create a default TensorFlow Enterprise 2.3 Notebooks instance, complete the following steps. To specify properties for your instance, see Create a Notebooks instance with specific properties or go to notebook.new to go directly to the Advanced options instance creation dialog box.

  1. In the Google Cloud Console, go to the User-managed notebooks page.

    Go to User-managed notebooks

  2. Click New Notebook, select TensorFlow Enterprise 2.3, and then select Without GPUs .

  3. Click Create.

  4. Vertex AI Workbench creates a new user-managed notebooks instance based on your selected framework. An Open JupyterLab link becomes active when it's ready to use.

Open the notebook

Complete these steps to open a user-managed notebooks instance:
  1. On the User-managed notebooks page in the Google Cloud Console, click Open JupyterLab to open the notebook.

  2. Vertex AI Workbench opens your notebook.

Run a classification tutorial in your notebook instance

Complete these steps to try out your new notebook by running a classification tutorial:

  1. In your JupyterLab notebook, on the left, double-click the tutorials folder to open it, and navigate to and open tutorials/keras/basic_classification.ipynb.

  2. Click the run button to run cells of the tutorial.

What's next