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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:

export DOCKER_BUILDKIT=1
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.

Running in Kubernetes

See the Horovod Helm Chart, Kubeflow MPI Operator, FfDL, and Polyaxon for guidance on running these Docker images in Kubernetes.