Shortcuts

Installation

There are two versions of MMCV:

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

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

Warning

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-full

Note

Before installing mmcv-full, 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-full version, at which point you can build mmcv-full from source.

Note

mmcv-full 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-full 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.

Note

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-full -i https://pypi.tuna.tsinghua.edu.cn/simple

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

Using mmcv-full 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#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.

Install mmcv

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
Read the Docs v: latest
Versions
master
latest
2.x
v1.7.0
v1.6.2
v1.6.1
v1.6.0
v1.5.3
v1.5.2_a
v1.5.1
v1.5.0
v1.4.8
v1.4.7
v1.4.6
v1.4.5
v1.4.4
v1.4.3
v1.4.2
v1.4.1
v1.4.0
v1.3.18
v1.3.17
v1.3.16
v1.3.15
v1.3.14
v1.3.13
v1.3.12
v1.3.11
v1.3.10
v1.3.9
v1.3.8
v1.3.7
v1.3.6
v1.3.5
v1.3.4
v1.3.3
v1.3.2
v1.3.1
v1.3.0
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.