README.md

CIFAR 10 Horovod Example

This example uses the Deep Layer Aggregation method to train on the CIFAR10 dataset.

Installation with Pipenv

1. Install OpenMPI if you wish to be able to run a distributed workload locally.

2. Install Pipenv which is a dependency management tool with a locking mechanism (similar to Anaconda).

3. Clone this repository and run:

   export HOROVOD_WITH_PYTORCH=1
   export HOROVOD_WITH_MPI=1
   export HOROVOD_WITHOUT_GLOO=1
   
   # If GPU
   # export HOROVOD_CUDA_HOME=/usr/local/cuda
   # export HOROVOD_GPU=CUDA
   pipenv install

   This command creates a virtualenv based on the Pipfile and Pipfile.lock.

Usage

With Docker

Prepare the directories:

mkdir -p "$(pwd)/data"
# Download CIFAR-10 dataset
curl -fsSL https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz -o "$(pwd)/data/cifar-10-python.tar.gz"
tar -C $(pwd)/data/ -xvzf "$(pwd)/data/cifar-10-python.tar.gz"
mkdir -p "$(pwd)/checkpoint"

Run the model:

docker run \
  --rm \
  -v "$(pwd)/data:/data" \
  -v "$(pwd)/checkpoint:/checkpoint" \
  -u 1000:1000 \
  --entrypoint /bin/sh \
  ghcr.io/deepsquare-io/cifar-10-example:latest \
  -c '\
  mpirun \
  -np 4 \
  /.venv/bin/python3 \
  /app/main.py \
  --no-cuda \
  --horovod \
  --checkpoint_in=/checkpoint/ckpt.pth \
  --checkpoint_out=/checkpoint/ckpt.pth \
  --dataset=/data
'

With Pipenv

Prepare the directories:

mkdir -p "$(pwd)/data"
# Download CIFAR-10 dataset
curl -fsSL https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz -o "$(pwd)/data/cifar-10-python.tar.gz"
tar -C $(pwd)/data/ -xvzf "$(pwd)/data/cifar-10-python.tar.gz"
mkdir -p "$(pwd)/checkpoint"

Run the model:

pipenv shell
mpirun \
  -np 4 \
  python3 \
  main.py \
  --no-cuda \
  --horovod \
  --checkpoint_in="$(pwd)/checkpoint/ckpt.pth" \
  --checkpoint_out="$(pwd)/checkpoint/ckpt.pth" \
  --dataset="$(pwd)/data"
'