# Use TensorFlow Enterprise with a user-managed notebooks instance

> [!CAUTION]
>
> Vertex AI Workbench user-managed notebooks is deprecated.
> Support for user-managed notebooks will end soon and the
> ability to create user-managed notebooks instances will be removed.
> For the end of availability date, see
> [Vertex AI deprecations](https://docs.cloud.google.com/vertex-ai/docs/deprecations).

This page provides a brief overview of
[Vertex AI Workbench user-managed notebooks
instances](https://docs.cloud.google.com/vertex-ai/docs/workbench/user-managed/introduction)
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](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html).

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](https://github.com/)
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.

<br />

### Required roles

If you created the project, you have the
Owner (`roles/owner`) IAM role on the project,
which includes all required permissions. Skip this section and
start creating your user-managed notebooks instance. If you didn't
create the project yourself, continue in this section.


To get the permissions that
you need to create a Vertex AI Workbench user-managed notebooks instance,

ask your administrator to grant you the
following IAM roles on the project:

- Notebooks Admin ([`roles/notebooks.admin`](https://docs.cloud.google.com/vertex-ai/docs/workbench/user-managed/iam#notebooks.admin))
- Service Account User ([`roles/iam.serviceAccountUser`](https://docs.cloud.google.com/iam/docs/roles-permissions/iam#iam.serviceAccountUser))


For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access).


You might also be able to get
the required permissions through [custom
roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles) or other [predefined
roles](https://docs.cloud.google.com/iam/docs/roles-overview#predefined).

## Create a user-managed notebooks instance

To create a default TensorFlow Enterprise 2.13
user-managed notebooks instance, complete the following steps.

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

   [Go to User-managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/user-managed)
2. Click **Create new**.

3. In **Environment** , select
   **TensorFlow Enterprise 2.13**.

4. If you want
   to include a GPU, you must select the option to **Attach 1 NVIDIA T4 GPU** .
   You can adjust the number of GPUs later if you need to. For information
   about adjusting the number of GPUs, see
   [Change machine type and configure GPUs of
   a user-managed notebooks instance](https://docs.cloud.google.com/vertex-ai/docs/workbench/user-managed/manage-hardware-accelerators).

5. Click **Create**.

6. 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](https://docs.cloud.google.com/vertex-ai/docs/workbench/introduction).
- Get started [using TensorFlow Enterprise with
  Deep Learning VM](https://docs.cloud.google.com/tensorflow-enterprise/docs/use-with-deep-learning-vm).
- Get started [using TensorFlow Enterprise with
  Deep Learning Containers](https://docs.cloud.google.com/tensorflow-enterprise/docs/use-with-deep-learning-containers).