Introduction to user-managed 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.

User-managed notebooks instances save you the difficulty of creating and configuring a Deep Learning virtual machine by providing verified, optimized, and tested images for your chosen framework.

Pre-installed software

You can configure a user-managed notebooks instance to include the following:

  • JupyterLab (see version details)

  • Python versions 2.7 and 3.*, with key packages:

    • numpy
    • sklearn
    • scipy
    • pandas
    • nltk
    • pillow
    • fairness-indicators for TensorFlow 2.3 and 2.4 user-managed notebooks instances
    • many others
  • R version 4.*, with key packages:

    • xgboost
    • ggplot2
    • caret
    • nnet
    • rpy2 (an R package for accessing R in Python notebooks)
    • randomForest
    • many others
  • Anaconda

  • Nvidia packages with the latest Nvidia driver for GPU-enabled instances:

    • CUDA 9.*, 10.*, and 11.*
    • CuDNN 7.*
    • NCCL 2.*

JupyterLab version details

JupyterLab 3.x is pre-installed on new user-managed notebooks instances by default. For instances created before the M80 Deep Learning VM release, JupyterLab 1.x was pre-installed.

To create an older version of a user-managed notebooks instance, see Create a specific version of a user-managed notebooks instance.

VPC Service Controls

VPC Service Controls provides additional security for your user-managed notebooks instances. To learn more, read the Overview of VPC Service Controls. To use user-managed notebooks within a service perimeter, see Use a user-managed notebooks instance within a service perimeter.

User-managed notebooks and Dataproc Hub

Dataproc Hub is a customized JupyterHub server. Administrators can create Dataproc Hub instances that can spawn single-user Dataproc clusters to host user-managed notebooks environments. See Configure Dataproc Hub.

User-managed notebooks and Dataflow

You can use user-managed notebooks within a pipeline, and then run the pipeline on Dataflow. To create an Apache Beam user-managed notebooks instance that you can use with Dataflow, see Developing interactively with Apache Beam notebooks.


Learn more about Vertex AI Workbench pricing.

What's next

To get started with user-managed notebooks, create a new user-managed notebooks instance, open JupyterLab, and try one of the samples in the tutorials folder.

The tutorials folder in the JupyterLab file browser

Then install dependencies that you'll need to do your work.