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Installation

There are two versions of MMCV:

  • mmcv: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.

  • mmcv-lite: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.

警告

Do not install both versions in the same environment, otherwise you may encounter errors like ModuleNotFound. You need to uninstall one before installing the other. Installing the full version is highly recommended if CUDA is avaliable.

Install mmcv

Before installing mmcv, make sure that PyTorch has been successfully installed following the PyTorch official installation guide. This can be verified using the following command

python -c 'import torch;print(torch.__version__)'

If version information is output, then PyTorch is installed.

Install with pip

Use the following command to check the version of CUDA and PyTorch

python -c 'import torch;print(torch.__version__);print(torch.version.cuda)'

Select the appropriate installation command depending on the type of system, CUDA version, PyTorch version, and MMCV version






If you do not find a corresponding version in the dropdown box above, you probably do not have a pre-built package corresponding to the PyTorch or CUDA or mmcv version, at which point you can build mmcv from source.




注解


mmcv is only compiled on PyTorch 1.x.0 because the compatibility
usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you
can install mmcv compiled with PyTorch 1.x.0 and it usually works well.
For example, if your PyTorch version is 1.8.1, you can feel free to choose 1.8.x.






注解


If you would like to use opencv-python-headless instead of opencv-python,
e.g., in a minimum container environment or servers without GUI,
you can first install it before installing MMCV to skip the installation of opencv-python.


Alternatively, if it takes too long to install a dependency library, you can specify the pypi source


mim install mmcv -i https://pypi.tuna.tsinghua.edu.cn/simple







You can run check_installation.py to check the installation of mmcv after running the installation commands.






Using mmcv with Docker


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#main: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=2.0.0 .





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=1.11.0 \
    --build-arg CUDA=11.3 \
    --build-arg CUDNN=8 \
    --build-arg MMCV=2.0.0 .





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








Install mmcv-lite


If you need to use PyTorch-related modules, make sure PyTorch has been successfully installed in your environment by referring to the PyTorch official installation guide.


pip install mmcv-lite