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Docker Training Guide

Containerized Deployment

Prerequisites

Docker permissions — add your user to the docker group to avoid sudo:

sudo usermod -aG docker $USER

Log out and back in (or run newgrp docker) for the change to take effect.

NVIDIA Container Toolkit — required for GPU passthrough (--gpus all):

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Build Image

# Option 1: convenience script
bash docker/build.sh

# Option 2: manual build (from repo root)
docker build -t orchard-ml:latest -f docker/Dockerfile .

Execution Modes

Standard Mode (Performance Optimized):

docker run -it --rm \
  --gpus all \
  --shm-size=8g \
  -u $(id -u):$(id -g) \
  -v $(pwd)/dataset:/app/dataset \
  -v $(pwd)/outputs:/app/outputs \
  orchard-ml:latest \
  recipes/config_resnet_18.yaml

Strict Reproducibility Mode (Bit-Perfect Determinism):

docker run -it --rm \
  --gpus all \
  --shm-size=8g \
  -u $(id -u):$(id -g) \
  -e DOCKER_REPRODUCIBILITY_MODE=TRUE \
  -e PYTHONHASHSEED=42 \
  -v $(pwd)/dataset:/app/dataset \
  -v $(pwd)/outputs:/app/outputs \
  orchard-ml:latest \
  recipes/config_resnet_18.yaml

[!NOTE] - TORCH_HOME, MPLCONFIGDIR, IN_DOCKER, and CUBLAS_WORKSPACE_CONFIG are pre-set in the image and do not need to be passed at runtime - PYTHONHASHSEED must be set at container startup (before the Python interpreter loads) to guarantee hash determinism — the image default is 0, override with -e if a specific seed is needed - --gpus all requires NVIDIA Container Toolkit

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