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horovod
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Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

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Horovod

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Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use.

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LF AI

Horovod is hosted by the

LF AI Foundation 
_ (LF AI). If you are a company that is deeply committed to using open source technologies in artificial intelligence, machine, and deep learning, and want to support the communities of open source projects in these domains, consider joining the LF AI Foundation. For details about who's involved and how Horovod plays a role, read the LF AI
announcement 
_.

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Documentation

  • Latest Release 
    _
  • master 
    _

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Why Horovod?

The primary motivation for this project is to make it easy to take a single-GPU training script and successfully scale it to train across many GPUs in parallel. This has two aspects:

  1. How much modification does one have to make to a program to make it distributed, and how easy is it to run it?
  2. How much faster would it run in distributed mode?

Internally at Uber we found the MPI model to be much more straightforward and require far less code changes than previous solutions such as Distributed TensorFlow with parameter servers. Once a training script has been written for scale with Horovod, it can run on a single-GPU, multiple-GPUs, or even multiple hosts without any further code changes. See the

Usage 
__ section for more details.

In addition to being easy to use, Horovod is fast. Below is a chart representing the benchmark that was done on 128 servers with 4 Pascal GPUs each connected by RoCE-capable 25 Gbit/s network:

.. image:: https://user-images.githubusercontent.com/16640218/38965607-bf5c46ca-4332-11e8-895a-b9c137e86013.png :alt: 512-GPU Benchmark

Horovod achieves 90% scaling efficiency for both Inception V3 and ResNet-101, and 68% scaling efficiency for VGG-16. See

Benchmarks 
_ to find out how to reproduce these numbers.

While installing MPI and NCCL itself may seem like an extra hassle, it only needs to be done once by the team dealing with infrastructure, while everyone else in the company who builds the models can enjoy the simplicity of training them at scale.

Install

To install Horovod:

  1. Install
    CMake 
    __

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  1. If you've installed TensorFlow from
    PyPI 
    __, make sure that the
    g++-4.8.5
    or
    g++-4.9
    is installed.

If you've installed PyTorch from

PyPI 
__, make sure that the
g++-4.9
or above is installed.

If you've installed either package from

Conda 
, make sure that the ``gxxlinux-64`` Conda package is installed.

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  1. Install the
    horovod
    pip package.

To run on CPUs:

.. code-block:: bash

  $ pip install horovod

To run on GPUs with NCCL:

.. code-block:: bash

  $ HOROVOD_GPU_OPERATIONS=NCCL pip install horovod

For more details on installing Horovod with GPU support, read

Horovod on GPU 
_.

For the full list of Horovod installation options, read the

Installation Guide 
_.

If you want to use MPI, read

Horovod with MPI 
_.

If you want to use Conda, read

Building a Conda environment with GPU support for Horovod 
_.

If you want to use Docker, read

Horovod in Docker 
_.

To compile Horovod from source, follow the instructions in the

Contributor Guide 
_.

Concepts

Horovod core principles are based on

MPI 
_ concepts such as size, rank, local rank, allreduce, allgather and, broadcast. See
this page 
_ for more details.

Supported frameworks

See these pages for Horovod examples and best practices:

  • Horovod with TensorFlow 
    _
  • Horovod with Keras 
    _
  • Horovod with PyTorch 
    _
  • Horovod with MXNet 
    _

Usage

To use Horovod, make the following additions to your program:

  1. Run
    hvd.init()
    to initialize Horovod.

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  1. Pin each GPU to a single process to avoid resource contention.

With the typical setup of one GPU per process, set this to local rank. The first process on the server will be allocated the first GPU, the second process will be allocated the second GPU, and so forth.

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  1. Scale the learning rate by the number of workers.

Effective batch size in synchronous distributed training is scaled by the number of workers. An increase in learning rate compensates for the increased batch size.

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  1. Wrap the optimizer in
    hvd.DistributedOptimizer
    .

The distributed optimizer delegates gradient computation to the original optimizer, averages gradients using allreduce or allgather, and then applies those averaged gradients.

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  1. Broadcast the initial variable states from rank 0 to all other processes.

This is necessary to ensure consistent initialization of all workers when training is started with random weights or restored from a checkpoint.

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  1. Modify your code to save checkpoints only on worker 0 to prevent other workers from corrupting them.

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Example using TensorFlow v1 (see the

examples 
_ directory for full training examples):

.. code-block:: python

import tensorflow as tf
import horovod.tensorflow as hvd


Initialize Horovod

hvd.init()

Pin GPU to be used to process local rank (one GPU per process)

config = tf.ConfigProto() config.gpu_options.visible_device_list = str(hvd.local_rank())

Build model...

loss = ... opt = tf.train.AdagradOptimizer(0.01 * hvd.size())

Add Horovod Distributed Optimizer

opt = hvd.DistributedOptimizer(opt)

Add hook to broadcast variables from rank 0 to all other processes during

initialization.

hooks = [hvd.BroadcastGlobalVariablesHook(0)]

Make training operation

train_op = opt.minimize(loss)

Save checkpoints only on worker 0 to prevent other workers from corrupting them.

checkpoint_dir = '/tmp/train_logs' if hvd.rank() == 0 else None

The MonitoredTrainingSession takes care of session initialization,

restoring from a checkpoint, saving to a checkpoint, and closing when done

or an error occurs.

with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir, config=config, hooks=hooks) as mon_sess: while not mon_sess.should_stop(): # Perform synchronous training. mon_sess.run(train_op)

Running Horovod

The example commands below show how to run distributed training. See

Run Horovod 
_ for more details, including RoCE/InfiniBand tweaks and tips for dealing with hangs.
  1. To run on a machine with 4 GPUs:

.. code-block:: bash

    $ horovodrun -np 4 -H localhost:4 python train.py
  1. To run on 4 machines with 4 GPUs each:

.. code-block:: bash

   $ horovodrun -np 16 -H server1:4,server2:4,server3:4,server4:4 python train.py
  1. To run using Open MPI without the

    horovodrun
    wrapper, see
    Running Horovod with Open MPI 
    _.
  2. To run in Docker, see

    Horovod in Docker 
    _.
  3. To run in Kubernetes, see

    Kubeflow 
    ,
    MPI Operator 
    ,
    Helm Chart 
    ,
    FfDL 
    , and
    Polyaxon 
    _.
  4. To run on Spark, see

    Horovod on Spark 
    _.
  5. To run on Ray, see

    Horovod on Ray 
    _.
  6. To run in Singularity, see

    Singularity 
    _.
  7. To run in a LSF HPC cluster (e.g. Summit), see

    LSF 
    _.

Gloo

Gloo 
_ is an open source collective communications library developed by Facebook.

Gloo comes included with Horovod, and allows users to run Horovod without requiring MPI to be installed.

For environments that have support both MPI and Gloo, you can choose to use Gloo at runtime by passing the

--gloo
argument to
horovodrun
:

.. code-block:: bash

 $ horovodrun --gloo -np 2 python train.py

mpi4py

Horovod supports mixing and matching Horovod collectives with other MPI libraries, such as

mpi4py 
_, provided that the MPI was built with multi-threading support.

You can check for MPI multi-threading support by querying the

hvd.mpi_threads_supported()
function.

.. code-block:: python

import horovod.tensorflow as hvd

Initialize Horovod

hvd.init()

Verify that MPI multi-threading is supported.

assert hvd.mpi_threads_supported()

from mpi4py import MPI assert hvd.size() == MPI.COMM_WORLD.Get_size()

You can also initialize Horovod with an

mpi4py
sub-communicator, in which case each sub-communicator will run an independent Horovod training.

.. code-block:: python

from mpi4py import MPI
import horovod.tensorflow as hvd

Split COMM_WORLD into subcommunicators

subcomm = MPI.COMM_WORLD.Split(color=MPI.COMM_WORLD.rank % 2, key=MPI.COMM_WORLD.rank)

Initialize Horovod

hvd.init(comm=subcomm)

print('COMM_WORLD rank: %d, Horovod rank: %d' % (MPI.COMM_WORLD.rank, hvd.rank()))

Inference

Learn how to optimize your model for inference and remove Horovod operations from the graph

here 
_.

Tensor Fusion

One of the unique things about Horovod is its ability to interleave communication and computation coupled with the ability to batch small allreduce operations, which results in improved performance. We call this batching feature Tensor Fusion.

See

here 
__ for full details and tweaking instructions.

Horovod Timeline

Horovod has the ability to record the timeline of its activity, called Horovod Timeline.

.. image:: https://user-images.githubusercontent.com/16640218/29735271-9e148da0-89ac-11e7-9ae0-11d7a099ac89.png :alt: Horovod Timeline

Use Horovod timeline to analyze Horovod performance. See

here 
__ for full details and usage instructions.

Automated Performance Tuning

Selecting the right values to efficiently make use of Tensor Fusion and other advanced Horovod features can involve a good amount of trial and error. We provide a system to automate this performance optimization process called autotuning, which you can enable with a single command line argument to

horovodrun
.

See

here 
__ for full details and usage instructions.

Guides

  1. Run distributed training in Microsoft Azure using
    Batch AI and Horovod 
    _.
  2. Distributed model training using Horovod 
    _.

Send us links to any user guides you want to publish on this site

Troubleshooting

See

Troubleshooting 
_ and submit a
ticket 
_ if you can't find an answer.

Citation

Please cite Horovod in your publications if it helps your research:

::

@article{sergeev2018horovod,
  Author = {Alexander Sergeev and Mike Del Balso},
  Journal = {arXiv preprint arXiv:1802.05799},
  Title = {Horovod: fast and easy distributed deep learning in {TensorFlow}},
  Year = {2018}
}

Publications

  1. Sergeev, A., Del Balso, M. (2017) Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow. Retrieved from

    https://eng.uber.com/horovod/ 
    _
  2. Sergeev, A. (2017) Horovod - Distributed TensorFlow Made Easy. Retrieved from

    https://www.slideshare.net/AlexanderSergeev4/horovod-distributed-tensorflow-made-easy 
    _
  3. Sergeev, A., Del Balso, M. (2018) Horovod: fast and easy distributed deep learning in TensorFlow. Retrieved from

    arXiv:1802.05799 
    _

References

The Horovod source code was based off the Baidu

tensorflow-allreduce 
_ repository written by Andrew Gibiansky and Joel Hestness. Their original work is described in the article
Bringing HPC Techniques to Deep Learning 
_.

Mailing lists

Subscribe to

Horovod Announce 
_ and
Horovod Technical-Discuss 
_ to stay up to date.

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