What is Horovod?

Horovod is an open-source distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. Horovod improves the speed, scale, and resource utilization of deep learning training.

In this post I describe how I build Conda environments for my deep learning projects where I plan to use Horovod to enable distributed training across multiple GPUs (either on the same node or spread across multuple nodes). If you like my approach then you can make use of the template repository on GitHub to get started with you rnext Horovod data science project!

Installing the NVIDIA CUDA Toolkit

First thing you need to do is to install the appropriate version of the NVIDIA CUDA Toolkit on your workstation. For this blog post I am using NVIDIA CUDA Toolkit 10.1 (documentation) which works with all three deep learning frameworks that are currently supported by Horovod.

Why not just use the cudatoolkit package?

Typically when installing PyTorch, TensorFlow, or Apache MXNet with GPU support using Conda you simply add the appropriate version cudatoolkit package to your environment.yml file.

Unfortunately, the cudatoolkit package available from conda-forge does not include NVCC and in order to use Horovod with either PyTorch, TensorFlow, or MXNet you need to compile extensions.

What about the cudatoolkit-dev package?

While there are cudatoolkit-dev packages available from conda-forge that do include NVCC, I have had difficult getting these packages to consistently install properly.

Use the nvcc_linux-64 meta-pacakge!

The most robust approach to obtain NVCC and still use Conda to manage all the other dependencies is to install the NVIDIA CUDA Toolkit on your system and then install a meta-package nvcc_linux-64 from conda-forge which configures your Conda environment to use the NVCC installed on the system together with the other CUDA Toolkit components installed inside the Conda environment.

The environment.yml file

I prefer to specify as many dependencies as possible in the Conda environment.yml file and only specify dependencies in requirements.txt that are not available via Conda channels. Check the official Horovod installation guide for details of required dependencies.

Channel Priority

I use the recommended channel priorities. Note that conda-forge has priority over defaults.

name: null

channels:
  - pytorch
  - conda-forge
  - defaults

Dependencies

There are a few things worth noting about the dependencies.

  1. Even though I have installed the NVIDIA CUDA Toolkit manually I still use Conda to manage the other required CUDA components such as cudnn and nccl (and the optional cupti).
  2. I use two meta-pacakges, cxx-compiler and nvcc_linux-64, to make sure that suitable C, and C++ compilers are installed and that the resulting Conda environment is aware of the manually installed CUDA Toolkit.
  3. Horovod requires some controller library to coordinate work between the various Horovod processes. Typically this will be some MPI implementation such as OpenMPI. However, rather than specifying the openmpi package directly I instead opt for mpi4py Conda package which provides a cuda-aware build of OpenMPI (where possible).
  4. Horovod also support that Gloo collective communications library that can be used in place of MPI. I include cmake in order to insure that the Horovod extensions for Gloo are built.

Below are the core required dependencies. The complete environment.yml file is available on GitHub.

dependencies:
  - bokeh=1.4
  - cmake=3.16 # insures that the Gloo library extensions will be built
  - cudnn=7.6
  - cupti=10.1
  - cxx-compiler=1.0 # meta-pacakge that insures suitable C and C++ compilers are available
  - jupyterlab=1.2
  - mpi4py=3.0 # installs cuda-aware openmpi
  - nccl=2.5
  - nodejs=13
  - nvcc_linux-64=10.1 # meta-package that configures environment to be "cuda-aware"
  - pip=20.0
  - pip:
    - mxnet-cu101mkl==1.6.* # makes sure MXNET is installed prior to horovod
    - -r file:requirements.txt
  - python=3.7
  - pytorch=1.4
  - tensorboard=2.1
  - tensorflow-gpu=2.1
  - torchvision=0.5

The requirements.txt File

The requirements.txt file is where all of the pip dependencies, including Horovod itself, are listed for installation. In addition to Horovod I typically will also use pip to install JupyterLab extensions to enable GPU and CPU resource monitoring via jupyterlab-nvdashboard and Tensorboard support via jupyter-tensorboard.

horovod==0.19.*
jupyterlab-nvdashboard==0.2.* # server-side component; client-side component installed in postBuild
jupyter-tensorboard==0.2.*

# make sure horovod is re-compiled if environment is re-built
--no-binary=horovod

Note the use of the --no-binary option at the end of the file. Including this option insures that Horovod will be re-built whenever the Conda environment is re-built.

The complete requirements.txt file is available on GitHub.

Building Conda Environment

After adding any necessary dependencies that should be downloaded via conda to the environment.yml file and any dependencies that should be downloaded via pip to the requirements.txt file you create the Conda environment in a sub-directory ./envof your project directory by running the following commands.

export ENV_PREFIX=$PWD/env
export HOROVOD_CUDA_HOME=$CUDA_HOME
export HOROVOD_NCCL_HOME=$ENV_PREFIX
export HOROVOD_GPU_ALLREDUCE=NCCL
export HOROVOD_GPU_BROADCAST=NCCL
conda env create --prefix $ENV_PREFIX --file environment.yml --force

By default Horovod will try and build extensions for all detected frameworks. See the Horovod documentation on environment variables for the details on additional environment variables that can be set prior to building Horovod.

Once the new environment has been created you can activate the environment with the following command.

conda activate $ENV_PREFIX

The postBuild File

If you wish to use any JupyterLab extensions included in the environment.yml and requirements.txt files then you need to rebuild the JupyterLab application using the following commands to source the postBuild script.

conda activate $ENV_PREFIX # optional if environment already active
. postBuild

Wrapping it all up in a Bash script

I typically wrap these commands into a shell script ./bin/create-conda-env.sh. Running the shell script will set the Horovod build variables, create the Conda environment, activate the Conda environment, and built JupyterLab with any additional extensions.

#!/bin/bash --login

set -e

export ENV_PREFIX=$PWD/env
export HOROVOD_CUDA_HOME=$CUDA_HOME
export HOROVOD_NCCL_HOME=$ENV_PREFIX
export HOROVOD_GPU_ALLREDUCE=NCCL
export HOROVOD_GPU_BROADCAST=NCCL

conda env create --prefix $ENV_PREFIX --file environment.yml --force
conda activate $ENV_PREFIX
. postBuild

I typically put scripts inside a ./bin directory in my project root directory. The script should be run from the project root directory as follows.

./bin/create-conda-env.sh # assumes that $CUDA_HOME is set properly

Verifying the Conda environment

After building the Conda environment you can check that Horovod has been built with support for the deep learning frameworks TensorFlow, PyTorch, Apache MXNet, and the contollers MPI and Gloo with the following command.

conda activate $ENV_PREFIX # optional if environment already active
horovodrun --check-build

You should see output similar to the following.

Horovod v0.19.1:

Available Frameworks:
    [X] TensorFlow
    [X] PyTorch
    [X] MXNet

Available Controllers:
    [X] MPI
    [X] Gloo

Available Tensor Operations:
    [X] NCCL
    [ ] DDL
    [ ] CCL
    [X] MPI
    [X] Gloo

Listing the contents of the Conda environment

To see the full list of packages installed into the environment run the following command.

conda activate $ENV_PREFIX # optional if environment already active
conda list

Updating the Conda environment

If you add (remove) dependencies to (from) the environment.yml file or the requirements.txt file after the environment has already been created, then you can re-create the environment with the following command.

conda env create --prefix $ENV_PREFIX --file environment.yml --force

However, whenever I add new dependencies I prefer to re-run the Bash script which will re-build both the Conda environment and JupyterLab.

./bin/create-conda-env.sh

Summary

Finding a reproducible process for building Horovod extensions for my deep learning projects was tricky. Key to my solution is the use of meta-packages from conda-forge to insure that the appropriate compilers are installed and that the resulting Conda environment is aware of the system installed NVIDIA CUDA Toolkit. The second key is to use the --no-binary flag in the requirements.txt file to insure that Horovod is re-built whenever the Conda environment is re-built.

If you like my approach then you can make use of the template repository on GitHub to get started with your next Horovod data science project!