Use TensorFlow Enterprise with a user-managed notebooks instance

This page provides a brief overview of Vertex AI Workbench user-managed notebooks instances and describes how to get started using TensorFlow Enterprise in a user-managed notebooks instance.

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

Overview of Vertex AI Workbench user-managed notebooks instances

Vertex AI Workbench user-managed notebooks instances let you create and manage deep learning virtual machine (VM) instances that are prepackaged with JupyterLab.

User-managed notebooks instances have a preinstalled suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can configure either CPU-only or GPU-enabled instances.

Your user-managed notebooks instances are protected by Google Cloud authentication and authorization and are available by using a user-managed notebooks instance URL. User-managed notebooks instances also integrate with GitHub and can sync 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.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud 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.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project.

7. Enable the Notebooks API.

   Enable the API

Create a user-managed notebooks instance

To create a default TensorFlow Enterprise 2.6 user-managed notebooks instance, complete the following steps. To specify properties for your instance, see Create a user-managed 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.

3. Select TensorFlow Enterprise 2.6, and then select Without GPUs .

4. Click Create.

5. Vertex AI Workbench automatically starts the instance. When the instance is ready to use, Vertex AI Workbench activates an Open JupyterLab link.

Open the notebook

To open a user-managed notebooks instance, complete the following steps:

1. In the Google Cloud console, next to your user-managed notebooks instance's name, click Open JupyterLab.

2. Your user-managed notebooks instance opens JupyterLab.

Run a classification tutorial in your notebook instance

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

1. In the JupyterLab  File Browser, double-click the tutorials folder to open it, and navigate to and open tutorials/keras/basic_classification.ipynb.

   

2. To run cells of the tutorial, click the  run button.

What's next

• Learn more about Vertex AI Workbench.
• Get started using TensorFlow Enterprise with Deep Learning VM.
• Get started using TensorFlow Enterprise with Deep Learning Containers.