• Linux (Windows is not officially supported)

  • Python 3.6+

  • PyTorch 1.3 or higher

  • CUDA 9.0 or higher

  • NCCL 2

  • GCC 4.9 or higher

  • mmcv

Install mmediting

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

b. Install PyTorch and torchvision following the official instructions, e.g.,

conda install pytorch torchvision -c pytorch

Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

E.g. 1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

E.g. 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

If you build PyTorch from source instead of installing the prebuilt pacakge, you can use more CUDA versions such as 9.0.

c. Clone the mmediting repository.

git clone
cd mmediting

d. Install build requirements and then install mmediting.

pip install -r requirements.txt
pip install -v -e .  # or "python develop"

If you build mmediting on macOS, replace the last command with

CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .


  1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

    Important: Be sure to remove the ./build folder if you reinstall mmedit with a different CUDA/PyTorch version.

    pip uninstall mmedit
    rm -rf ./build
    find . -name "*.so" | xargs rm
  2. Following the above instructions, mmediting is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).

  3. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

  4. Some models (such as EDVR in restorers) depend on CUDA ops in mmcv-full which is listed in requirements.txt. Install it with the default command pip install -r requirements.txt need to compile CUDA ops locally and it may take up to 10 mins. Another option is to install pre-compiled mmcv-full, visit MMCV github page for concrete instructions. Moreover, if the model you intend to use does not depend on CUDA ops, you could also install the lite version of mmcv with pip install mmcv in which CUDA ops is excluded.

Install with CPU only

The code can be built for CPU only environment (where CUDA isn’t available).

However some functionality is gone in this mode:

  • Deformable Convolution

So if you try to run inference with a model containing deformable convolution you will get an error.

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.5, CUDA 10.1
docker build -t mmediting docker/

Run it with

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmediting/data mmediting

A from-scratch setup script

Here is a full script for setting up mmediting with conda.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install -c pytorch pytorch torchvision -y
git clone
cd mmediting
pip install -r requirements.txt
pip install -v -e .