Introduction to AI Platform Notebooks

AI Platform Notebooks enables you to create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab. AI Platform Notebooks instances support the TensorFlow and PyTorch frameworks and have a pre-installed suite of Python and R deep learning packages. You can configure either CPU-only or GPU-enabled instances to optimize your workflow.

AI Platform 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.

Your notebook instances are protected by Google Cloud Platform (GCP) authentication and authorization, and are available using a notebook instance URL. Notebook instances also integrate with GitHub so that you can easily sync your notebook with a GitHub repository.

Pre-installed software

You can configure an AI Platform Notebooks instance to include the following:

  • Python versions 2.7 and 3.*

  • Python core packages:

    • numpy
    • sklearn
    • scipy
    • pandas
    • nltk
    • pillow
    • many others
  • R version 3.6

  • R core packages:

    • xgboost
    • ggplot2
    • caret
    • nnet
    • rpy2 (an R package for accessing R in Python notebooks)
    • randomForest
    • many others
  • JupyterLab and Notebook environments for quick prototyping

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

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


Learn more about AI Platform Notebooks pricing.

What's next

To get started with AI Platform Notebooks, create a new notebook instance, and then install dependencies that you'll need to do your work.

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