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

Using Notebooks, you can create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab.

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

Before you begin

Before you can use Notebooks, 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

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 to go directly to the Advanced options instance creation dialog box.

  1. Go to the Notebooks page in the Google Cloud Console.

    Go to the Notebooks page

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

  3. Click Create.

  4. Notebooks creates a new 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 Notebooks instance:
  1. On the Notebooks page in the Google Cloud Console, click Open JupyterLab to open the notebook.

  2. Notebooks 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