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README.md

Horovod Docker Images

Often installing Horovod on bare metal can be difficult if your environment is not setup correctly with CUDA, MPI, G++, CMake, etc. These Docker images are provided to simplify the onboarding process for new users, and can serve as a starting point for building your own runtime environment.

Repositories

Separate images are provided for different Horovod configurations, and are published to separate repos in DockerHub.

  • horovod/horovod Horovod built with CUDA support and packaged with the latest stable TensorFlow, PyTorch, MXNet, and Spark releases
  • horovod/horovod-cpu Horovod built for CPU training and packaged with the latest stable TensorFlow, PyTorch, MXNet, and Spark releases
  • horovod/horovod-ray Horoovd built with CUDA support from the latest ray-project/ray:nightly-gpu and packaged with the latest stable TensorFlow and PyTorch releases

Image Tags

  • master - built from Horovod's master branch
  • nightly - nightly build of Horovod
  • sha-<commit point> - version of Horovod at designated git sha1 7-character commit point

Building Custom Images

Build arguments are provided to allow the user to build Horovod against custom versions of various frameworks, including:

  • TENSORFLOW_VERSION - version of tensorflow pip package to install
  • PYTORCH_VERSION - version of torch pip package to install
  • PYTORCH_LIGHTNING_VERSION - version of pytorch_lightning pip package to install
  • TORCHVISION_VERSION - version of torchvision pip package to install
  • MXNET_VERSION - version of mxnet pip package to install
  • CUDNN_VERSION - version of libcudnn apt package to install (only for horovod image)
  • NCCL_VERSION - version of libnccl apt package to install (only for horovod image)
  • CUDA_DOCKER_VERSION - tag of the nvidia/cuda image to build from (only for horovod image)
  • RAY_DOCKER_VERSION - tag of the rayproject/ray GPU image to build from (only for horovod-ray image)

Building the Docker images should be run from the root Horovod directory. For example:

docker build \
    --build-arg TENSORFLOW_VERSION=2.3.1 \
    --build-arg PYTORCH_VERSION=1.7.0+cu110 \
    -f docker/horovod/Dockerfile .

Running Containers

See the Horovod in Docker documentation for guidance on running these Docker images, and Horovod on Ray for usage with Ray.