Horovod documentation ===================== Horovod improves the speed, scale, and resource utilization of deep learning training. Get started ----------- Choose your deep learning framework to learn how to get started with Horovod. .. raw:: html

To use Horovod with TensorFlow on your laptop:

  1. Install Open MPI 3.1.2 or 4.0.0, or another MPI implementation.
  2. If you've installed TensorFlow from PyPI, make sure that g++-5 or above is installed.
    If you've installed TensorFlow from Conda, make sure that the gxx_linux-64 Conda package is installed.
  3. Install the Horovod pip package: pip install horovod
  4. Read Horovod with TensorFlow for best practices and examples.
Or, use Horovod on GPUs, in Spark, Docker, Singularity, or Kubernetes (Kubeflow, MPI Operator, Helm Chart, and FfDL).

To use Horovod with Keras on your laptop:

  1. Install Open MPI 3.1.2 or 4.0.0, or another MPI implementation.
  2. If you've installed TensorFlow from PyPI, make sure that g++-5 or above is installed.
    If you've installed TensorFlow from Conda, make sure that the gxx_linux-64 Conda package is installed.
  3. Install the Horovod pip package: pip install horovod
  4. Read Horovod with Keras for best practices and examples.
Or, use Horovod on GPUs, in Spark, Docker, Singularity, or Kubernetes (Kubeflow, MPI Operator, Helm Chart, and FfDL).

To use Horovod with PyTorch on your laptop:

  1. Install Open MPI 3.1.2 or 4.0.0, or another MPI implementation.
  2. If you've installed PyTorch from PyPI, make sure that g++-5 or above is installed.
    If you've installed PyTorch from Conda, make sure that the gxx_linux-64 Conda package is installed.
  3. Install the Horovod pip package: pip install horovod
  4. Read Horovod with PyTorch for best practices and examples.
Or, use Horovod on GPUs, in Spark, Docker, Singularity, or Kubernetes (Kubeflow, MPI Operator, Helm Chart, and FfDL).

To use Horovod with Apache MXNet on your laptop:

  1. Install Open MPI 3.1.2 or 4.0.0, or another MPI implementation.
  2. Install the Horovod pip package: pip install horovod
  3. Read Horovod with MXNet for best practices and examples.
Or, use Horovod on GPUs, in Spark, Docker, Singularity, or Kubernetes (Kubeflow, MPI Operator, Helm Chart, and FfDL).

Guides ------ .. toctree:: :maxdepth: 2 summary_include concepts_include install_include api tensorflow xla keras pytorch mxnet running_include elastic_include benchmarks_include inference_include gpus_include mpi_include oneccl_include conda_include docker_include spark_include ray_include lsf_include tensor-fusion_include adasum_user_guide_include timeline_include hyperparameter_search_include autotune_include process_set_include troubleshooting_include contributors_include Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search`