mirror of https://github.com/open-mmlab/mmcv.git
[DOC] build on windows (#663)
* previous doc * init version of windows installation * refine doc * merge build instructions * fix styles * add some detail * change an item to note Co-authored-by: Kai Chen <chenkaidev@gmail.com>pull/494/merge
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README.md
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README.md
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@ -40,8 +40,6 @@ There are two versions of MMCV:
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**Note**: 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.
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### Install with pip
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a. Install the lite version.
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```python
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@ -115,58 +113,4 @@ pip install mmcv-full
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Note that the local compiling may take up to 10 mins.
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### Install from source
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After cloning the repo with
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```bash
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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```
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You can either
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- install the lite version
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```bash
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pip install -e .
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```
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- install the full version
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```bash
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MMCV_WITH_OPS=1 pip install -e .
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```
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If you are on macOS, add the following environment variables before the installing command.
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```bash
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++'
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```
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e.g.,
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```bash
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' MMCV_WITH_OPS=1 pip install -e .
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```
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If you are on Windows10, set the following environment variable before the installing command.
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```bash
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set MMCV_WITH_OPS=1
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```
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e.g.,
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```bash
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set MMCV_WITH_OPS=1
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pip install -e .
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```
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Note: If you would like to use `opencv-python-headless` instead of `opencv-python`,
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e.g., in a minimum container environment or servers without GUI,
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you can first install it before installing MMCV to skip the installation of `opencv-python`.
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### TroubleShooting
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If you meet issues when running or compiling mmcv, we list some common issues in [TROUBLESHOOTING.md](docs/trouble_shooting.md).
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If you would like to build MMCV from source, please refer to the [guide](https://mmcv.readthedocs.io/en/latest/build.html).
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@ -0,0 +1,231 @@
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## Build MMCV from source
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### Build on Linux or macOS
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After cloning the repo with
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```bash
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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```
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You can either
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- install the lite version
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```bash
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pip install -e .
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```
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- install the full version
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```bash
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MMCV_WITH_OPS=1 pip install -e .
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```
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If you are on macOS, add the following environment variables before the installing command.
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```bash
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++'
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```
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e.g.,
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```bash
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' MMCV_WITH_OPS=1 pip install -e .
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```
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Note: If you would like to use `opencv-python-headless` instead of `opencv-python`,
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e.g., in a minimum container environment or servers without GUI,
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you can first install it before installing MMCV to skip the installation of `opencv-python`.
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### Build on Windows
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Building MMCV on Windows is a bit more complicated than that on Linux.
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The following instructions show how to get this accomplished.
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#### Prerequisite
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The following software is required for building MMCV on windows.
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Install them first.
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- [Git](https://git-scm.com/download/win)
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- During installation, tick **add git to Path**.
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- [Visual Studio Community 2019](https://visualstudio.microsoft.com)
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- A compiler for C++ and CUDA codes.
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- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
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- Official distributions of Python should work too.
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- [CUDA 10.2](https://developer.nvidia.com/cuda-10.2-download-archive)
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- Not required for building CPU version.
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- Customize the installation if necessary. As a recommendation, skip the driver installation if a newer version is already installed.
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**You should know how to set up environment variables, especially `Path`, on Windows. The following instruction relies heavily on this skill.**
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#### Setup Python Environment
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1. Launch Anaconda prompt from Windows Start menu
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Do not use raw `cmd.exe` s instruction is based on PowerShell syntax.
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1. Create a new conda environment
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```shell
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conda create --name mmcv python=3.7 # 3.6, 3.7, 3.8 should work too as tested
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conda activate mmcv # make sure to activate environment before any operation
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```
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1. Install PyTorch. Choose a version based on your need.
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```shell
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conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
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```
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We only tested PyTorch version >= 1.6.0.
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1. Prepare MMCV source code
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```shell
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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```
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1. Install required Python packages
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```shell
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pip3 install -r requirements.txt
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```
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#### Build and install MMCV
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MMCV can be built in three ways:
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1. Lite version (without ops)
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In this way, only `flow_warp` module will be compiled as a Cython extension.
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1. Full version (CPU ops)
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In addition to `flow_warp` module, module `ops` will be compiled as a pytorch extension, but only x86 code will be compiled. The compiled ops can be executed on CPU only.
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1. Full version (CUDA ops)
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Both x86 and CUDA codes of `ops` module will be compiled. The compiled version can be run on both CPU and CUDA-enabled GPU (if implemented).
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##### Common steps
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1. Set up MSVC compiler
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Set Environment variable, add `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\Hostx86\x64` to `PATH`, so that `cl.exe` will be available in prompt, as shown below.
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```none
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(base) PS C:\Users\xxx> cl
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Microsoft (R) C/C++ Optimizing Compiler Version 19.27.29111 for x64
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Copyright (C) Microsoft Corporation. All rights reserved.
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usage: cl [ option... ] filename... [ / link linkoption... ]
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```
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For compatibility, we use the x86-hosted and x64-targeted compiler. note `Hostx86\x64` in the path.
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You may want to change the system language to English because pytorch will parse text output from `cl.exe` to check its version. However only utf-8 is recognized. Navigate to Control Panel -> Region -> Administrative -> Language for Non-Unicode programs and change it to English.
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##### Option 1: Build MMCV (lite version)
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After finishing above common steps, launch Anaconda shell from Start menu and issue the following commands:
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```shell
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# activate environment
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conda activate mmcv
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# change directory
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cd mmcv
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# build
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$env:MMCV_WITH_OPS = 0
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python setup.py build_ext # if success, cl will be launched to compile flow_warp.
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# install
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python setup.py develop
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# check
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pip list
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```
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##### Option 2: Build MMCV (full version with CPU)
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1. Finish above common steps
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1. Set up environment variables
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```shell
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$env:MMCV_WITH_OPS = 1
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$env:MAX_JOBS = 8 # based on your available number of CPU cores and amount of memory
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```
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1. Following build steps of the lite version
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```shell
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# activate environment
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conda activate mmcv
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# change directory
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cd mmcv
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# build
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python setup.py build_ext # if success, cl will be launched to compile flow_warp first and then ops
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# install
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python setup.py develop
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# check
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pip list
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```
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##### Option 3: Build MMCV (full version with CUDA)
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1. Finish above common steps
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1. Make sure `CUDA_PATH` or `CUDA_HOME` is already set in `envs` via `ls env:`, desired output is shown as below:
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```none
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(base) PS C:\Users\WRH> ls env:
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Name Value
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---- -----
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<... omit some lines ...>
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CUDA_PATH C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
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CUDA_PATH_V10_1 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
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CUDA_PATH_V10_2 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
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<... omit some lines ...>
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```
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This should already be done by CUDA installer. If not, or you have multiple version of CUDA tookit installed, set it with
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```shell
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$env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
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# OR
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$env:CUDA_HOME = $env:CUDA_PATH_V10_2 # if CUDA_PATH_V10_2 is in envs:
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```
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1. Set CUDA target arch
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```shell
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# Suppose you are using GTX 1080, which is of capability 6.1
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$env:TORCH_CUDA_ARCH_LIST="6.1"
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# OR build all suppoted arch, will be slow
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$env:TORCH_CUDA_ARCH_LIST="3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5"
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```
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Note: Check your the compute capability of your GPU from [here](https://developer.nvidia.com/cuda-gpus).
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1. Launch compiling the same way as CPU
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```shell
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$env:MMCV_WITH_OPS = 1
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$env:MAX_JOBS = 8 # based on available number of CPU cores and amount of memory
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# activate environment
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conda activate mmcv
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# change directory
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cd mmcv
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# build
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python setup.py build_ext # if success, cl will be launched to compile flow_warp first and then ops
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# install
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python setup.py develop
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# check
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pip list
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```
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**Note**: If you are compiling against PyTorch 1.6.0, you might meet some errors from PyTorch as described in [this issue](https://github.com/pytorch/pytorch/issues/42467).
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Follow [this pull request](https://github.com/pytorch/pytorch/pull/43380/files) to modify the source code in your local PyTorch installation.
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If you meet issues when running or compiling mmcv, we list some common issues in [TROUBLESHOOTING](./trouble_shooting.html).
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@ -16,6 +16,7 @@ Contents
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registry.md
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cnn.md
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ops.md
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build.md
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trouble_shooting.md
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api.rst
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