Skip to content
This repository has been archived by the owner on Mar 19, 2024. It is now read-only.

Latest commit

 

History

History
174 lines (128 loc) · 6 KB

INSTALL.md

File metadata and controls

174 lines (128 loc) · 6 KB
Raw

Installation

Our installation is simple and we provide pre-built binaries (pip, conda) and also instructions for building from source (pip, conda).

Table of Contents

Requirements

At a high level, project requires following system dependencies.

  • Linux
  • Python>=3.6.2 and <3.9
  • PyTorch>=1.4
  • torchvision (matching PyTorch install)
  • CUDA (must be a version supported by the pytorch version)
  • OpenCV (optional)

Installing VISSL from source (recommended)

The following instructions assume that you have desired CUDA version installed and working.

Install from source in Conda environment

Step 1: Create Conda environment

If you don't have anaconda, run this bash scrip to install conda.

conda create -n vissl_env python=3.7
source activate vissl_env

Step 2: Install PyTorch (conda)

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

Step 3: Install APEX (conda)

conda install -c vissl apex

Step 4: Install VISSL

# clone vissl repository
cd $HOME && git clone --recursive https://github.com/facebookresearch/vissl.git && cd $HOME/vissl/
# Optional, checkout stable v0.1.6 branch. While our docs are versioned, the tutorials
# use v0.1.6 and the docs are more likely to be up-to-date.
git checkout v0.1.6
git checkout -b v0.1.6
# install vissl dependencies
pip install --progress-bar off -r requirements.txt
pip install opencv-python
# update classy vision install to commit stable for vissl.
# Note: If building from vissl main, use classyvision main.
pip uninstall -y classy_vision
pip install classy-vision@https://github.com/facebookresearch/ClassyVision/tarball/4785d5ee19d3bcedd5b28c1eb51ea1f59188b54d
# update fairscale install to commit stable for vissl.
pip uninstall -y fairscale
pip install fairscale@https://github.com/facebookresearch/fairscale/tarball/df7db85cef7f9c30a5b821007754b96eb1f977b6
# install vissl dev mode (e stands for editable)
pip install -e ".[dev]"
# verify installation
python -c 'import vissl, apex'

Install from source in PIP environment

Step 1: Create Virtual environment (pip)

python3 -m venv ~/venv
. ~/venv/bin/activate

Step 2: Install PyTorch (pip)

pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html

Step 3: Install APEX (pip)

pip install -f https://dl.fbaipublicfiles.com/vissl/packaging/apexwheels/py37_cu101_pyt171/download.html apex

Step 4: Install VISSL

Follow step4 instructions from the PIP installation

Installing VISSL from pre-built binaries

Install VISSL conda package

This assumes you have CUDA 10.2.

conda create -n vissl python=3.8
conda activate vissl
conda install -c pytorch pytorch=1.7.1 torchvision cudatoolkit=10.2
conda install -c vissl -c iopath -c conda-forge -c pytorch -c defaults apex vissl

The package also contains code for the fairscale and ClassyVision libraries. Ensure you do not have them installed separately.

For other versions of PyTorch, Python, CUDA, please modify the above instructions with the desired version. VISSL provides Apex packages for all combinations of pytorch, python and compatible cuda.

Install VISSL pip package

This example is with pytorch 1.5.1 and cuda 10.1. Please modify the PyTorch version, cuda version and accordingly apex version below for the desired settings.

Step 1: Create Virtual environment (pip)

python3 -m venv ~/venv
. ~/venv/bin/activate

Step 2: Install PyTorch, OpenCV and APEX (pip)

  • We use PyTorch=1.5.1 with CUDA 10.1 in the following instruction (user can chose their desired version).
  • There are several ways to install opencv, one possibility is as follows.
  • For APEX, we provide pre-built binary built with optimized C++/CUDA extensions provided by APEX.
pip install torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install opencv-python
pip install -f https://dl.fbaipublicfiles.com/vissl/packaging/apexwheels/py38_cu101_pyt151/download.html apex

Note that, for the APEX install, you need to get the versions of CUDA, PyTorch, and Python correct in the URL. We provide APEX versions with all possible combinations of Python, PyTorch, CUDA. Select the right APEX Wheels if you desire a different combination.

On Google Colab, everything until this point is already set up. You install APEX there as follows.

import sys
import torch
version_str="".join([
    f"py3{sys.version_info.minor}_cu",
    torch.version.cuda.replace(".",""),
    f"_pyt{torch.__version__[0:5:2]}"
])
!pip install -f https://dl.fbaipublicfiles.com/vissl/packaging/apexwheels/{version_str}/download.html apex

Step 3: Install VISSL

pip install vissl
# verify installation
python -c 'import vissl'

The package also contains code for the ClassyVision library. Ensure you do not have it installed separately.

That's it! You are now ready to use this code.

Optional: Install Apex from source (common for both pip and conda)

Apex installation requires that you have a latest nvcc so the c++ extensions can be compiled with latest gcc (>=7.4). Check the APEX website for more instructions.

# see https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#virtual-architecture-feature-list to select cuda architecture you want to build
CUDA_VER=10.1 TORCH_CUDA_ARCH_LIST="5.0;5.2;5.3;6.0;6.1;6.2;7.0;7.5" ./docker/common/install_apex.sh