FoodSeg103/demo/mmcv/docker/README.md

Docker images

There are two Dockerfile files to build docker images, one to build an image with the mmcv pre-built package and the other with the mmcv development environment.

.
|-- README.md
|-- dev  # build with mmcv development environment
|   `-- Dockerfile
`-- release  # build with mmcv pre-built package
    `-- Dockerfile

Build docker images

Build with mmcv pre-built package

Build with local repository

git clone https://github.com/open-mmlab/mmcv.git && cd mmcv
docker build -t mmcv -f docker/release/Dockerfile .

Or build with remote repository

docker build -t mmcv https://github.com/open-mmlab/mmcv.git#master:docker/release

The Dockerfile installs latest released version of mmcv by default, but you can specify mmcv versions to install expected versions.

docker image build -t mmcv -f docker/release/Dockerfile --build-arg MMCV=2.0.0rc1 .

If you also want to use other versions of PyTorch and CUDA, you can also pass them when building docker images.

An example to build an image with PyTorch 1.11 and CUDA 11.3.

docker build -t mmcv -f docker/release/Dockerfile \
    --build-arg PYTORCH=1.9.0 \
    --build-arg CUDA=11.1 \
    --build-arg CUDNN=8 \
    --build-arg MMCV=2.0.0rc1 .

More available versions of PyTorch and CUDA can be found at dockerhub/pytorch.

Build with mmcv development environment

If you want to build an docker image with the mmcv development environment, you can use the following command

git clone https://github.com/open-mmlab/mmcv.git && cd mmcv
docker build -t mmcv -f docker/dev/Dockerfile --build-arg CUDA_ARCH=7.5 .

Note that CUDA_ARCH is the cumpute capability of your GPU and you can find it at Compute Capability.

The building process may take 10 minutes or more.

Run images

docker run --gpus all --shm-size=8g -it mmcv

See docker run for more usages.