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Zaida Zhou bd1da5ab47 | 1 year ago | |
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dev | 1 year ago | |
release | 1 year ago | |
README.md | 1 year ago |
There are two Dockerfile
files to build docker images, one to build an image with the mmcv-full 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 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-full by default, but you can specify mmcv versions to install expected versions.
docker image build -t mmcv -f docker/release/Dockerfile --build-arg MMCV=1.5.0 .
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=1.5.0 .
More available versions of PyTorch and CUDA can be found at dockerhub/pytorch.
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.
docker run --gpus all --shm-size=8g -it mmcv
See docker run for more usages.
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Python C++ Cuda Pickle Markdown other
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