cd ~
python -c "import mmagic; print(mmagic.__version__)"
# Example output: 1.0.0
The installation is successful if the version number is output correctly.
You may be curious about what -e . means when supplied with pip install.
Here is the description:
-e means editable mode.
When import mmagic, modules under the cloned directory are imported.
If pip install without -e, pip will copy cloned codes to somewhere like lib/python/site-package.
Consequently, modified code under the cloned directory takes no effect unless pip install again.
Thus, pip install with -e is particularly convenient for developers. If some codes are modified, new codes will be imported next time without reinstallation.
. means code in this directory
You can also use pip install -e .[all], which will install more dependencies, especially for pre-commit hooks and unittests.
Customize installation
CUDA Version
When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
Please make sure the GPU driver satisfies the minimum version requirements.
See this table for more information.
Installing CUDA runtime libraries is enough if you follow our best practices,
because no CUDA code will be compiled locally.
However, if you hope to compile MMCV from source or develop other CUDA operators,
you need to install the complete CUDA toolkit from NVIDIA’s website,
and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in conda install command.
Install MMCV without MIM
MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way.
MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.
To install MMCV with pip instead of MIM, please follow MMCV installation guides.
This requires manually specifying a find-url based on PyTorch version and its CUDA version.
For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.
pip install 'mmcv>=2.0.0' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
Using MMagic with Docker
We provide a Dockerfile to build an image.
Ensure that your docker version >=19.03.
# build an image with PyTorch 1.8, CUDA 11.1
# If you prefer other versions, just modified the Dockerfile
docker build -t mmagic docker/
Run it with
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmagic/data mmagic
Trouble shooting
If you have some issues during the installation, please first view the FAQ page.
You may open an issue on GitHub if no solution is found.
Developing with multiple MMagic versions
The train and test scripts already modify the PYTHONPATH to ensure the script uses the MMagic in the current directory.
To use the default MMagic installed in the environment rather than that you are working with, you can remove the following line in those scripts
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
