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Gy-LuGy-Lu
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upload dockerfile, workflow and update readme
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‎.github/workflows/build_fastfold_wheel.py‎

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import requests
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from bs4 import BeautifulSoup
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import argparse
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import os
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import subprocess
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from packaging import version
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from functools import cmp_to_key
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WHEEL_TEXT_ROOT_URL = 'https://github.com/hpcaitech/public_assets/tree/main/colossalai/torch_build/torch_wheels'
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RAW_TEXT_FILE_PREFIX = 'https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/torch_build/torch_wheels'
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CUDA_HOME = os.environ['CUDA_HOME']
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--torch_version', type=str)
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return parser.parse_args()
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def get_cuda_bare_metal_version():
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raw_output = subprocess.check_output([CUDA_HOME + "/bin/nvcc", "-V"], universal_newlines=True)
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output = raw_output.split()
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release_idx = output.index("release") + 1
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release = output[release_idx].split(".")
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bare_metal_major = release[0]
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bare_metal_minor = release[1][0]
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return bare_metal_major, bare_metal_minor
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def all_wheel_info():
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page_text = requests.get(WHEEL_TEXT_ROOT_URL).text
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soup = BeautifulSoup(page_text)
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all_a_links = soup.find_all('a')
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wheel_info = dict()
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for a_link in all_a_links:
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if 'cuda' in a_link.text and '.txt' in a_link.text:
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filename = a_link.text
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torch_version, cuda_version = filename.rstrip('.txt').split('-')
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cuda_version = cuda_version.lstrip('cuda')
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if float(cuda_version) < 11.1:
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continue
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if torch_version not in wheel_info:
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wheel_info[torch_version] = dict()
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wheel_info[torch_version][cuda_version] = dict()
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file_text = requests.get(f'{RAW_TEXT_FILE_PREFIX}/{filename}').text
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lines = file_text.strip().split('\n')
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for line in lines:
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parts = line.split('\t')
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method, url, python_version = parts[:3]
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if float(python_version) < 3.8 or method == "conda":
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continue
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wheel_info[torch_version][cuda_version][python_version] = dict(url=url)
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return wheel_info
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def build_colossalai(wheel_info):
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cuda_version_major, cuda_version_minor = get_cuda_bare_metal_version()
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cuda_version_on_host = f'{cuda_version_major}.{cuda_version_minor}'
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for torch_version, cuda_versioned_wheel_info in wheel_info.items():
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for cuda_version, python_versioned_wheel_info in cuda_versioned_wheel_info.items():
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if cuda_version_on_host == cuda_version:
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for python_version, wheel_info in python_versioned_wheel_info.items():
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url = wheel_info['url']
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filename = url.split('/')[-1].replace('%2B', '+')
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cmd = f'bash ./build_fastfold_wheel.sh {url} {filename} {cuda_version} {python_version}'
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os.system(cmd)
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def main():
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args = parse_args()
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wheel_info = all_wheel_info()
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# filter wheels on condition
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all_torch_versions = list(wheel_info.keys())
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def _compare_version(a, b):
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if version.parse(a) > version.parse(b):
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return 1
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else:
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return -1
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all_torch_versions.sort(key=cmp_to_key(_compare_version))
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if args.torch_version != 'all':
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torch_versions = args.torch_version.split(',')
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# only keep the torch versions specified
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for key in all_torch_versions:
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if key not in torch_versions:
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wheel_info.pop(key)
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build_colossalai(wheel_info)
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if __name__ == '__main__':
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main()

‎.github/workflows/build_fastfold_wheel.sh‎

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#!/usr/bin/env bash
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url=${1}
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filename=${2}
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cuda_version=${3}
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python_version=${4}
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git reset --hard HEAD
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mkdir -p ./all_dist
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source activate base
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conda create -n $python_version -y python=$python_version
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source activate $python_version
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wget -nc -q -O ./$filename $url
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pip install ./$filename
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pip install numpy
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python setup.py bdist_wheel
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mv ./dist/* ./all_dist
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python setup.py clean
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conda deactivate
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conda env remove -n $python_version

‎.github/workflows/release_bdist.yml‎

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name: Release bdist wheel
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on:
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workflow_dispatch:
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inputs:
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torch_version:
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type: string
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description: torch version, separated by comma
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required: true
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default: "all"
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cuda_version:
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type: string
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description: cuda version, separated by comma
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required: true
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github_ref:
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type: string
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description: Branch or Tag
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default: 'main'
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required: true
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jobs:
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matrix_preparation:
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name: Prepare Container List
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runs-on: ubuntu-latest
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outputs:
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matrix: ${{ steps.set-matrix.outputs.matrix }}
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steps:
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- id: set-matrix
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env:
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TORCH_VERSIONS: ${{ inputs.torch_version }}
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CUDA_VERSIONS: ${{ inputs.cuda_version }}
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run: |
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echo $TORCH_VERSIONS
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echo $CUDA_VERSIONS
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IFS=','
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DOCKER_IMAGE=()
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for cv in $CUDA_VERSIONS
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do
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DOCKER_IMAGE+=("\"hpcaitech/cuda-conda:${cv}\"")
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done
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container=$( IFS=',' ; echo "${DOCKER_IMAGE[*]}" )
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container="[${container}]"
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echo "$container"
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echo "::set-output name=matrix::{\"container\":$(echo "$container")}"
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build:
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name: Release bdist wheels
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needs: matrix_preparation
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if: github.repository == 'hpcaitech/FastFold' && contains(fromJson('["FrankLeeeee", "feifeibear", "Shenggan", "Gy-Lu"]'), github.actor)
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runs-on: [self-hosted, gpu]
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strategy:
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fail-fast: false
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matrix: ${{fromJson(needs.matrix_preparation.outputs.matrix)}}
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container:
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image: ${{ matrix.container }}
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options: --gpus all --rm
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steps:
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- uses: actions/checkout@v2
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with:
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fetch-depth: 0
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- name: Copy scripts and checkout
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run: |
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cp -r ./.github/workflows/* ./
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ln -s /github/home/pip_wheels ./pip_wheels
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git checkout $git_ref
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env:
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git_ref: ${{ github.event.inputs.github_ref }}
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- name: Build bdist wheel
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run: |
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pip install beautifulsoup4 requests packaging
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python ./build_fastfold_wheel.py --torch_version $TORCH_VERSIONS
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env:
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TORCH_VERSIONS: ${{ inputs.torch_version }}
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- name: 🚀 Deploy
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uses: garygrossgarten/github-action-scp@release
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with:
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local: all_dist
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remote: ${{ secrets.PRIVATE_PYPI_DIR }}
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host: ${{ secrets.PRIVATE_PYPI_HOST }}
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username: ${{ secrets.PRIVATE_PYPI_USER }}
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password: ${{ secrets.PRIVATE_PYPI_PASSWD }}

‎README.md‎

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## Installation
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You will need Python 3.8 or later and [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads) 11.1 or above when you are installing from source.
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To install and use FastFold, you will need:
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+ Python 3.8 or later
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+ [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads) 11.1 or above
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+ PyTorch 1.10 or above
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For now, You can install FastFold:
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### Using Conda (Recommended)
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We highly recommend installing an Anaconda or Miniconda environment and install PyTorch with conda.
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Lines below would create a new conda environment called "fastfold":
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```shell
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git clone https://github.com/hpcaitech/FastFold
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cd FastFold
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```
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We highly recommend installing an Anaconda or Miniconda environment and install PyTorch with conda:
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```shell
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conda env create --name=fastfold -f environment.yml
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conda activate fastfold
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bash scripts/patch_openmm.sh
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python setup.py install
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```
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You can get the FastFold source and install it with setuptools:
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### Using PyPi
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You can download FastFold with pre-built CUDA extensions.
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```shell
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python setup.py install
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pip install fastfold -f https://release.colossalai.org/fastfold
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```
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## Use Docker
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### Build On Your Own
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Run the following command to build a docker image from Dockerfile provided.
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> Building FastFold from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing `docker build`. More details can be found [here](https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime).
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```shell
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cd ColossalAI
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docker build -t fastfold ./docker
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```
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Run the following command to start the docker container in interactive mode.
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```shell
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docker run -ti --gpus all --rm --ipc=host fastfold bash
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```
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## Usage

‎docker/Dockerfile‎

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FROM hpcaitech/cuda-conda:11.3
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# install dependency
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RUN yum install -y patch
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RUN conda install pytorch==1.10.0 torchvision torchaudio cudatoolkit=11.3 -c pytorch \
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&& conda install setuptools=59.5.0 openmm=7.5.1 pdbfixer -c conda-forge \
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&& conda install hmmer==3.3.2 hhsuite=3.3.0 kalign2=2.04 -c bioconda
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RUN pip install biopython==1.79 dm-tree==0.1.6 ml-collections==0.1.0 numpy==1.21.2 \
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PyYAML==5.4.1 requests==2.26.0 scipy==1.7.1 tqdm==4.62.2 typing-extensions==3.10.0.2 einops
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RUN pip install colossalai==0.1.8+torch1.10cu11.3 -f https://release.colossalai.org
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# prepare environment
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Run git clone https://github.com/hpcaitech/FastFold.git\
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&& cd ./FastFold \
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&& /bin/bash scripts/patch_openmm.sh \
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&& python setup.py install

‎setup.py‎

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setup(
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name='fastfold',
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version='0.1.0-beta',
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version='0.1.0',
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packages=find_packages(exclude=(
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'assets',
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'benchmark',

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