models/grit_src/third_party/CenterNet2/docker/README.md

Use the container (with docker ≥ 19.03)

cd docker/
# Build:
docker build --build-arg USER_ID=$UID -t detectron2:v0 .
# Launch (require GPUs):
docker run --gpus all -it \
  --shm-size=8gb --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
  --name=detectron2 detectron2:v0

# Grant docker access to host X server to show images
xhost +local:`docker inspect --format='{{ .Config.Hostname }}' detectron2`

Use the container (with docker-compose ≥ 1.28.0)

Install docker-compose and nvidia-docker-toolkit, then run:

cd docker && USER_ID=$UID docker-compose run detectron2

Use the deployment container (to test C++ examples)

After building the base detectron2 container as above, do:

# Build:
docker build -t detectron2-deploy:v0 -f deploy.Dockerfile .
# Launch:
docker run --gpus all -it detectron2-deploy:v0

Using a persistent cache directory

You can prevent models from being re-downloaded on every run, by storing them in a cache directory.

To do this, add --volume=$HOME/.torch/fvcore_cache:/tmp:rw in the run command.

Install new dependencies

Add the following to Dockerfile to make persistent changes.

RUN sudo apt-get update && sudo apt-get install -y vim

Or run them in the container to make temporary changes.