Introduction to user-managed notebooks

User-managed notebooks let you create and manage 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.

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.

Preinstalled software

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

  • JupyterLab (see version details)

  • Python versions 2.7 and 3.x, 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.x, 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.x, 10.x, and 11.x
    • CuDNN 7.x
    • NCCL 2.x

JupyterLab version details

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

To create an earlier 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. For more information, see 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. For more information, 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. For information about how to create an Apache Beam user-managed notebooks instance that you can use with Dataflow, see Developing interactively with Apache Beam notebooks.


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What's next

To get started with user-managed notebooks, create a 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.