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.
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:
- fairness-indicators for TensorFlow 2.3 and 2.4 user-managed notebooks instances
- many others
R version 4.x, with key packages:
- rpy2 (an R package for accessing R in Python notebooks)
- many others
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.
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.
Then install dependencies that you'll need to do your work.