Prerequisites

• Linux

• Python 3.7+

• PyTorch 1.5+

• CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)

• GCC 5+

• MMCV

Note: You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.

Installation

Prepare environment

1. Create a conda virtual environment and activate it.

   conda create -n openmmlab python=3.7 -y
   conda activate openmmlab

2. Install PyTorch and torchvision following the official instructions.

   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 10.2 installed under /usr/local/cuda and would like to install PyTorch 1.10, you need to install the prebuilt PyTorch with CUDA 10.2.

   conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

   E.g.2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.5.1, you need to install the prebuilt PyTorch with CUDA 9.2.

   conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=9.2 -c pytorch

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

Install MMRazor

It is recommended to install MMRazor with MIM, which automatically handles the dependencies of OpenMMLab projects, including mmcv and other python packages.

pip install openmin
min install mmrazor

Or you can still install MMRazor manually

1. Install mmcv-full.

   pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html

   Please replace {cu_version} and {torch_version} in the url to your desired one. For example, to install the latest mmcv-full with CUDA 10.2 and PyTorch 1.10.0, use the following command:

   pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html

   See here for different versions of MMCV compatible to different PyTorch and CUDA versions.

   Optionally, you can compile mmcv from source if you need to develop both mmcv and mmdet. Refer to the guide for details.

2. Install MMRazor.

   You can simply install mmrazor with the following command:

   pip install mmrazor

   ​ or:

   pip install git+https://github.com/open-mmlab/mmrazor.git # install the master branch

   ​ Instead, if you would like to install MMRazor in dev mode, run following:

   git clone https://github.com/open-mmlab/mmrazor.git
   cd mmrazor
   pip install -v -e .  # or "python setup.py develop"

   Note:

   • When MMRazor is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it.
   • Currently, running pip install -v -e . will install mmcls, mmdet, mmsegmentation. We will work on minimum runtime requirements in future.

A from-scratch setup script

conda create -n openmmlab python=3.7 -y
conda activate openmmlab

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
# install the latest mmcv
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html
# install mmrazor
git clone https://github.com/open-mmlab/mmrazor.git
cd mmrazor
pip install -v -e .