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+# Installation
+
+<!-- TOC -->
+
+- [Requirements](#requirements)
+- [Prepare Environment](#prepare-environment)
+- [Install MMPose](#install-mmpose)
+- [Install with CPU only](#install-with-cpu-only)
+- [A from-scratch setup script](#a-from-scratch-setup-script)
+- [Another option: Docker Image](#another-option-docker-image)
+- [Developing with multiple MMPose versions](#developing-with-multiple-mmpose-versions)
+
+<!-- TOC -->
+
+## Requirements
+
+- Linux (Windows is not officially supported)
+- Python 3.6+
+- PyTorch 1.3+
+- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
+- GCC 5+
+- [mmcv](https://github.com/open-mmlab/mmcv) (Please install the latest version of mmcv-full)
+- Numpy
+- cv2
+- json_tricks
+- [xtcocotools](https://github.com/jin-s13/xtcocoapi)
+
+Optional:
+
+- [mmdet](https://github.com/open-mmlab/mmdetection) (to run pose demos)
+- [mmtrack](https://github.com/open-mmlab/mmtracking) (to run pose tracking demos)
+- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html) (to run 3d mesh demos)
+- [smplx](https://github.com/vchoutas/smplx) (to run 3d mesh demos)
+
+## Prepare environment
+
+a. Create a conda virtual environment and activate it.
+
+```shell
+conda create -n open-mmlab python=3.7 -y
+conda activate open-mmlab
+```
+
+b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g.,
+
+```shell
+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](https://pytorch.org/).
+
+`E.g.1` If you have CUDA 10.2 installed under `/usr/local/cuda` and would like to install PyTorch 1.8.0,
+you need to install the prebuilt PyTorch with CUDA 10.2.
+
+```shell
+conda install pytorch==1.8.0 torchvision==0.9.0 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 1.7.0.,
+you need to install the prebuilt PyTorch with CUDA 9.2.
+
+```shell
+conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=9.2 -c pytorch
+```
+
+If you build PyTorch from source instead of installing the pre-built package, you can use more CUDA versions such as 9.0.
+
+## Install MMPose
+
+a. Install mmcv, we recommend you to install the pre-built mmcv as below.
+
+```shell
+# pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.9.0/index.html
+# We can ignore the micro version of PyTorch
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.9/index.html
+```
+
+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.
+
+See [here](https://github.com/open-mmlab/mmcv#installation) for different versions of MMCV compatible to different PyTorch and CUDA versions.
+
+Optionally you can choose to compile mmcv from source by the following command
+
+```shell
+git clone https://github.com/open-mmlab/mmcv.git
+cd mmcv
+MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full, which contains cuda ops, will be installed after this step
+# OR pip install -e .  # package mmcv, which contains no cuda ops, will be installed after this step
+cd ..
+```
+
+**Important:** 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`.
+
+b. Clone the mmpose repository
+
+```shell
+git clone git@github.com:open-mmlab/mmpose.git # or git clone https://github.com/open-mmlab/mmpose
+cd mmpose
+```
+
+c. Install build requirements and then install mmpose
+
+```shell
+pip install -r requirements.txt
+pip install -v -e .  # or "python setup.py develop"
+```
+
+If you build MMPose on macOS, replace the last command with
+
+```shell
+CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
+```
+
+d. Install optional modules
+
+- [mmdet](https://github.com/open-mmlab/mmdetection) (to run pose demos)
+- [mmtrack](https://github.com/open-mmlab/mmtracking) (to run pose tracking demos)
+- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html) (to run 3d mesh demos)
+- [smplx](https://github.com/vchoutas/smplx) (to run 3d mesh demos)
+
+```{note}
+1. The git commit id will be written to the version number with step c, 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.
+
+1. Following the above instructions, mmpose 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).
+
+1. If you would like to use `opencv-python-headless` instead of `opencv-python`,
+   you can install it before installing MMCV.
+
+1. If you have `mmcv` installed, you need to firstly uninstall `mmcv`, and then install `mmcv-full`.
+
+1. Some dependencies are optional. Running `python setup.py develop` will only install the minimum runtime requirements.
+   To use optional dependencies like `smplx`, either install them with `pip install -r requirements/optional.txt`
+   or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`,
+   valid keys for the `[optional]` field are `all`, `tests`, `build`, and `optional`) like `pip install -v -e .[tests,build]`.
+```
+
+## Install with CPU only
+
+The code can be built for CPU only environment (where CUDA isn't available).
+
+In CPU mode you can run the demo/demo.py for example.
+
+## A from-scratch setup script
+
+Here is a full script for setting up mmpose with conda and link the dataset path (supposing that your COCO dataset path is $COCO_ROOT).
+
+```shell
+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
+
+# install the latest mmcv-full
+# Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one.
+# See [here](https://github.com/open-mmlab/mmcv#installation) for different versions of MMCV compatible to different PyTorch and CUDA versions.
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+
+# install mmpose
+git clone https://github.com/open-mmlab/mmpose.git
+cd mmpose
+pip install -r requirements.txt
+pip install -v -e .
+
+mkdir data
+ln -s $COCO_ROOT data/coco
+```
+
+## Another option: Docker Image
+
+We provide a [Dockerfile](/docker/Dockerfile) to build an image.
+
+```shell
+# build an image with PyTorch 1.6.0, CUDA 10.1, CUDNN 7.
+docker build -f ./docker/Dockerfile --rm -t mmpose .
+```
+
+**Important:** Make sure you've installed the [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker).
+
+Run the following cmd:
+
+```shell
+docker run --gpus all\
+ --shm-size=8g \
+ -it -v {DATA_DIR}:/mmpose/data mmpose
+```
+
+## Developing with multiple MMPose versions
+
+The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMPose in the current directory.
+
+To use the default MMPose installed in the environment rather than that you are working with, you can remove the following line in those scripts.
+
+```shell
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+```