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AlphaFold部署docker方法

作者：互联网

安装要求

AlphaFold 部署具有一定难度，体现在硬件、软件、数据、文档等多个层面。

1、安装nvida显卡驱动

https://www.nvidia.cn/drivers/unix/

chmod +x NVIDIA-Linux-x86_64-470.94.run

./NVIDIA-Linux-x86_64-470.94.run

1.1出现如下错误，nouveau没有disable：

 解决办法：

sudo vim /etc/modprobe.d/blacklist-nouveau.conf
#插入以下两行
blacklist nouveau 
options nouveau modeset=0
#用:wq保存退出后
sudo update-initramfs -u
#重启电脑，安装驱动，可能会没有图形界面，nvidia驱动安装好后重启可以恢复界面

 2、安装docker

curl https://get.docker.com | sh && sudo systemctl --now enable docker

 3、安装nvidia container toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list 

sudo apt-get update

sudo apt-get install -y nvidia-docker2


#失败可以尝试以下方法：
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
sudo apt-key add -

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

sudo apt-get install -y nvidia-docker2

安装好后重启服务，并验证安装是否成功：

sudo systemctl restart docker

sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi#或者nvidia-smi

 出现类似界面即为安装成功：

 

 4、下载alphafold 数据 Genetic databases并按照图示的结构放置，也可以拆分存放：

 

 其中mmcif数据下载时可能会出现损坏的情况，损坏文件个数少的时候可以在PDB数据库中下载对应文件，多的话就重新下吧。

 在alphafold/docker/run_docker.py中将相应数据集路径修改为本地绝对路径：

 # You can individually override the following paths if you have placed the
  # data in locations other than the FLAGS.data_dir.

  # Path to the Uniref90 database for use by JackHMMER.
  uniref90_database_path = os.path.join(
      FLAGS.data_dir, 'uniref90', 'uniref90.fasta')

  # Path to the Uniprot database for use by JackHMMER.
  uniprot_database_path = os.path.join(
      FLAGS.data_dir, 'uniprot', 'uniprot.fasta')

  # Path to the MGnify database for use by JackHMMER.
  mgnify_database_path = os.path.join(
      FLAGS.data_dir, 'mgnify', 'mgy_clusters_2018_12.fa')

  # Path to the BFD database for use by HHblits.
  bfd_database_path = os.path.join(
      FLAGS.data_dir, 'bfd',
      'bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')

  # Path to the Small BFD database for use by JackHMMER.
  small_bfd_database_path = os.path.join(
      FLAGS.data_dir, 'small_bfd', 'bfd-first_non_consensus_sequences.fasta')

  # Path to the Uniclust30 database for use by HHblits.
  uniclust30_database_path = os.path.join(
      FLAGS.data_dir, 'uniclust30', 'uniclust30_2018_08', 'uniclust30_2018_08')

  # Path to the PDB70 database for use by HHsearch.
  pdb70_database_path = os.path.join(FLAGS.data_dir, 'pdb70', 'pdb70')

  # Path to the PDB seqres database for use by hmmsearch.
  pdb_seqres_database_path = os.path.join(
      FLAGS.data_dir, 'pdb_seqres', 'pdb_seqres.txt')

  # Path to a directory with template mmCIF structures, each named <pdb_id>.cif.
  template_mmcif_dir = os.path.join(FLAGS.data_dir, 'pdb_mmcif', 'mmcif_files')

  # Path to a file mapping obsolete PDB IDs to their replacements.
  obsolete_pdbs_path = os.path.join(FLAGS.data_dir, 'pdb_mmcif', 'obsolete.dat')

5、下载alphafold

#先安装git
sudo apt-get install git

#将alphafold clone到本地
git clone https://github.com.cnpmjs.org/deepmind/alphafold.git

cd alphafold

docker build -f docker/Dockerfile -t alphafold .#会下载很多依赖包

pip3 install -r docker/requirements.txt

6、测试

python3 docker/run_docker.py \
--fasta_paths=T1050.fasta \
--max_template_date=2020-05-14 \
--data_dir=$DOWNLOAD_DIR #数据没有拆分，填数据路径，已经拆分并且在docker已经配置好可以随便填

参考

GitHub - deepmind/alphafold: Open source code for AlphaFold.

AlphaFold Deployment and Optimization on HPC Platform (sjtu.edu.cn)

安装nvidia-docker2报错E: Unable to locate package nvidia-docker2 - 神奇二进制 - 博客园 (cnblogs.com)

全面加速 GitHub，git clone 太慢的 9 种解决办法 - 云+社区 - 腾讯云 (tencent.com)

禁用Nouveau，安装Linux Nvidia 显卡驱动_JL-LOVE-CSDN博客_禁用nouveau驱动

标签：database,部署,sudo,nvidia,AlphaFold,path,docker,dir
来源： https://blog.csdn.net/Maiya19724/article/details/122329317