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 withJupyterLab.
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 withGitHuband 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.
Sign in to your Google Cloud account. If you're new to
Google Cloud,create an accountto evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
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:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[],[],null,["# Use TensorFlow Enterprise with a user-managed notebooks instance\n\n| Vertex AI Workbench user-managed notebooks is deprecated.\n| Support for user-managed notebooks will end soon and the\n| ability to create user-managed notebooks instances will be removed.\n| For the end of availability date, see\n| [Vertex AI deprecations](/vertex-ai/docs/deprecations).\n\nThis page provides a brief overview of\n[Vertex AI Workbench user-managed notebooks\ninstances](/vertex-ai/docs/workbench/user-managed/introduction)\nand describes how to get started using TensorFlow Enterprise in\na user-managed notebooks instance.\n\nIn this example, you create a\nTensorFlow Enterprise user-managed notebooks instance, open\na JupyterLab notebook, and run a classification tutorial on using\nneural networks with Keras.\n\nOverview of Vertex AI Workbench user-managed notebooks instances\n----------------------------------------------------------------\n\nVertex AI Workbench user-managed notebooks instances\nlet you create and manage deep learning virtual machine\n(VM) instances that are prepackaged with\n[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html).\n\nUser-managed notebooks instances have\na preinstalled suite of deep learning packages,\nincluding support for the TensorFlow and PyTorch\nframeworks. You can configure either CPU-only or GPU-enabled instances.\n\nYour user-managed notebooks instances are protected\nby Google Cloud\nauthentication and authorization and are available by using a\nuser-managed notebooks instance URL.\nUser-managed notebooks instances also integrate with\n[GitHub](https://github.com/)\nand can sync with a GitHub repository.\n\nBefore you begin\n----------------\n\nBefore you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **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.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Notebooks API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com&redirect=https://console.cloud.google.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **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.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Notebooks API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com&redirect=https://console.cloud.google.com)\n\n\u003cbr /\u003e\n\n### Required roles\n\nIf you created the project, you have the\nOwner (`roles/owner`) IAM role on the project,\nwhich includes all required permissions. Skip this section and\nstart creating your user-managed notebooks instance. If you didn't\ncreate the project yourself, continue in this section.\n\n\nTo get the permissions that\nyou need to create a Vertex AI Workbench user-managed notebooks instance,\n\nask your administrator to grant you the\nfollowing IAM roles on the project:\n\n- Notebooks Admin ([`roles/notebooks.admin`](/vertex-ai/docs/workbench/user-managed/iam#notebooks.admin))\n- Service Account User ([`roles/iam.serviceAccountUser`](/iam/docs/understanding-roles#iam.serviceAccountUser))\n\n\nFor more information about granting roles, see [Manage access to projects, folders, and organizations](/iam/docs/granting-changing-revoking-access).\n\n\nYou might also be able to get\nthe required permissions through [custom\nroles](/iam/docs/creating-custom-roles) or other [predefined\nroles](/iam/docs/roles-overview#predefined).\n\nCreate a user-managed notebooks instance\n----------------------------------------\n\nTo create a default TensorFlow Enterprise 2.13\nuser-managed notebooks instance, complete the following steps.\n\n1. In the Google Cloud console, go to the **User-managed notebooks** page.\n\n [Go to User-managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/user-managed)\n2. Click add_box **Create new**.\n\n3. In **Environment** , select\n **TensorFlow Enterprise 2.13**.\n\n4. If you want\n to include a GPU, you must select the option to **Attach 1 NVIDIA T4 GPU** .\n You can adjust the number of GPUs later if you need to. For information\n about adjusting the number of GPUs, see\n [Change machine type and configure GPUs of\n a user-managed notebooks instance](/vertex-ai/docs/workbench/user-managed/manage-hardware-accelerators).\n\n5. Click **Create**.\n\n6. Vertex AI Workbench automatically starts the instance. When the\n instance is ready to use, Vertex AI Workbench activates an\n **Open JupyterLab** link.\n\nOpen the notebook\n-----------------\n\nTo open a user-managed notebooks instance, complete the following steps:\n\n1. In the Google Cloud console, next to your user-managed notebooks\n instance's name, click **Open JupyterLab**.\n\n2. Your user-managed notebooks instance opens JupyterLab.\n\nRun a classification tutorial in your notebook instance\n-------------------------------------------------------\n\nComplete these steps to try out your new notebook by running\na classification tutorial:\n\n1. In the JupyterLab\n folder **File Browser** ,\n double-click the **tutorials** folder to open it, and\n navigate to and open **tutorials/keras/basic_classification.ipynb**.\n\n2. To run cells of the tutorial, click the\n play_arrow run button.\n\nWhat's next\n-----------\n\n- Learn more about [Vertex AI Workbench](/vertex-ai/docs/workbench/introduction).\n- Get started [using TensorFlow Enterprise with\n Deep Learning VM](/tensorflow-enterprise/docs/use-with-deep-learning-vm).\n- Get started [using TensorFlow Enterprise with\n Deep Learning Containers](/tensorflow-enterprise/docs/use-with-deep-learning-containers)."]]
