Build a Conda Environment with GPU Support for Horovod

In this section we describe how to build Conda environments for deep learning projects using Horovod to enable distributed training across multiple GPUs (either on the same node or spread across multuple nodes).

Installing the NVIDIA CUDA Toolkit

Install NVIDIA CUDA Toolkit 10.1 (documentation) which is the most recent version of NVIDIA CUDA Toolkit supported by 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 add the appropriate version of the cudatoolkit package to your environment.yml file. Unfortunately, for the moment at least, the cudatoolkit packages available via Conda do not include the NVIDIA CUDA Compiler (NVCC), which is required in order to build Horovod extensions for PyTorch, TensorFlow, or MXNet.

What about the cudatoolkit-dev package?

While there are cudatoolkit-dev packages available from conda-forge that do include NVCC, we have had difficulty getting these packages to consistently install properly. Some of the available builds require manual intervention to accept license agreements, making these builds unsuitable for installing on remote systems (which is critical functionality). Other builds seems to work on Ubuntu but not on other flavors of Linux.

Despite this, we would encourage you to try adding cudatoolkit-dev to your environment.yml file and see what happens! The package is well maintained so perhaps it will become more stable in the future.

Use the nvcc_linux-64 meta-package

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

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

Channel Priority

Use the recommended channel priorities. Note that conda-forge has priority over defaults and pytorch has priority over conda-forge.

name: null

channels:
- pytorch
- conda-forge
- defaults

Dependencies

There are a few things worth noting about the dependencies.

1. Even though you have installed the NVIDIA CUDA Toolkit manually, you should still use Conda to manage the other required CUDA components such as cudnn and nccl (and the optional cupti).
2. Use two meta-packages, 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, you should instead opt for mpi4py Conda package which provides a CUDA-aware build of OpenMPI.
4. Horovod also support the Gloo collective communications library that can be used in place of MPI. Include cmake 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 Gloo library extensions will be built
- cudnn=7.6
- cupti=10.1
- cxx-compiler=1.0 # insures 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 # configures environment to be "cuda-aware"
- pip=20.0
- pip:
    - mxnet-cu101mkl==1.6.* # MXNET is installed prior to horovod
    - -r file:requirements.txt
- python=3.7
- pytorch=1.5
- tensorboard=2.1
- tensorflow-gpu=2.1
- torchvision=0.6

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 we 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.*
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 ensures that Horovod will be re-built whenever the Conda environment is re-built.

Building the 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, create the Conda environment in a sub-directory env of 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_OPERATIONS=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 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 may need to rebuild the JupyterLab application.

For simplicity, we typically include the instructions for re-building JupyterLab in a postBuild script. Here is what this script looks like in the example Horovod environments.

jupyter labextension install --no-build jupyterlab-nvdashboard
jupyter labextension install --no-build jupyterlab_tensorboard
jupyter lab build

Use the following commands to source the postBuild script.

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

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

Verifying the Conda environment

After building the Conda environment, 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 v0.19.4:
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

Wrapping it all up in a Bash script

We typically wrap these commands into a shell script create-conda-env.sh. Running the shell script will set the Horovod build variables, create the Conda environment, activate the Conda environment, and build 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_OPERATIONS=NCCL
conda env create --prefix $ENV_PREFIX --file environment.yml --force
conda activate $ENV_PREFIX
. postBuild

We recommend that you put scripts inside a bin directory in your project root directory. Run the script from the project root directory as follows.

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

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 we add (remove) dependencies we prefer to re-run the Bash script which will re-build both the Conda environment and JupyterLab.

$ ./bin/create-conda-env.sh