Using Notebooks, you can create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab.
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 Notebooks instances are protected by Google Cloud authentication and authorization, and are available using a Notebooks instance URL. Notebooks instances also integrate with GitHub so that you can easily sync your notebook with a GitHub repository.
Notebooks saves 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 Notebooks instance to include the following:
Python versions 2.7 and 3.*, with key packages:
- fairness-indicators for TensorFlow 2.3 and 2.4 Notebooks instances
- many others
R version 4.0, 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.*, 10.*, and 11.*
- CuDNN 7.*
- NCCL 2.*
VPC Service Controls
VPC Service Controls provides additional security for your Notebooks instances. To learn more, read the Overview of VPC Service Controls. To use Notebooks within a service perimeter, see Use a Notebooks instance within a service perimeter.
Using Notebooks with Dataproc Hub
Dataproc Hub is a customized JupyterHub server. Administrators can create Dataproc Hub instances that can spawn single-user Dataproc clusters to host Notebooks environments. See Configure Dataproc Hub.
Using Notebooks with Dataflow
You can use Notebooks within a pipeline, and then run the pipeline on Dataflow. To create an Apache Beam Notebooks instance that you can use with Dataflow, see Developing interactively with Apache Beam notebooks.
To get started with Notebooks, create
a new Notebooks instance,
open JupyterLab, and try
one of the samples in the
Then install dependencies that you'll need to do your work.