Deep Learning Containers overview

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Deep Learning Containers are a set of Docker containers with key data science frameworks, libraries, and tools pre-installed. These containers provide you with performance-optimized, consistent environments that can help you prototype and implement workflows quickly.

To learn more about containers, see Containers at Google.

Pre-installed software

Deep Learning Containers images can be configured to include the following:

  • Frameworks:

    • TensorFlow
    • PyTorch
    • R
    • scikit-learn
    • XGBoost
  • Python, including these packages:

    • numpy
    • sklearn
    • scipy
    • pandas
    • nltk
    • pillow
    • fairness-indicators for TensorFlow 2.3 and 2.4 Deep Learning Containers instances
    • many others
  • Nvidia packages with the latest Nvidia driver for GPU-enabled instances:

    • CUDA 10.* and 11.* (the version depends on the framework)
    • CuDNN 7.* and NCCL 2.* (the version depends on the CUDA version)
  • JupyterLab

Community support

Ask a question about Deep Learning Containers on Stack Overflow or join the google-dl-platform Google group to discuss Deep Learning Containers.

Learn more about getting support from the community.

What's next

You can get started with Deep Learning Containers by walking through the How-to guides, which provide instructions on create and work with deep learning containers.