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docker/Dockerfile

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+# Copyright 2024 DeepMind Technologies Limited
+#
+# AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
+# this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/
+#
+# To request access to the AlphaFold 3 model parameters, follow the process set
+# out at https://github.com/google-deepmind/alphafold3. You may only use these
+# if received directly from Google. Use is subject to terms of use available at
+# https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md
+
+FROM nvidia/cuda:12.6.0-base-ubuntu22.04
+
+# Some RUN statements are combined together to make Docker build run faster.
+# Get latest package listing, install software-properties-common, git and wget.
+# git is required for pyproject.toml toolchain's use of CMakeLists.txt.
+RUN apt update --quiet \
+    && apt install --yes --quiet software-properties-common \
+    && apt install --yes --quiet git wget
+
+# Get apt repository of specific Python versions. Then install Python. Tell APT
+# this isn't an interactive TTY to avoid timezone prompt when installing.
+RUN add-apt-repository ppa:deadsnakes/ppa \
+    && DEBIAN_FRONTEND=noninteractive apt install --yes --quiet python3.11 python3-pip python3.11-venv python3.11-dev
+RUN python3.11 -m venv /alphafold3_venv
+ENV PATH="/hmmer/bin:/alphafold3_venv/bin:$PATH"
+
+# Install HMMER. Do so before copying the source code, so that docker can cache
+# the image layer containing HMMER.
+RUN mkdir /hmmer_build /hmmer ; \
+    wget http://eddylab.org/software/hmmer/hmmer-3.4.tar.gz --directory-prefix /hmmer_build ; \
+    (cd /hmmer_build && tar zxf hmmer-3.4.tar.gz && rm hmmer-3.4.tar.gz) ; \
+    (cd /hmmer_build/hmmer-3.4 && ./configure --prefix /hmmer) ; \
+    (cd /hmmer_build/hmmer-3.4 && make -j8) ; \
+    (cd /hmmer_build/hmmer-3.4 && make install) ; \
+    (cd /hmmer_build/hmmer-3.4/easel && make install) ; \
+    rm -R /hmmer_build
+
+# Copy the AlphaFold 3 source code from the local machine to the container and
+# set the working directory to there.
+COPY . /app/alphafold
+WORKDIR /app/alphafold
+
+# Install the Python dependencies AlphaFold 3 needs.
+RUN pip3 install -r dev-requirements.txt
+RUN pip3 install --no-deps .
+# Build chemical components database (this binary was installed by pip).
+RUN build_data
+
+# To work around a known XLA issue causing the compilation time to greatly
+# increase, the following environment variable setting XLA flags must be enabled
+# when running AlphaFold 3:
+ENV XLA_FLAGS="--xla_gpu_enable_triton_gemm=false"
+# Memory settings used for folding up to 5,120 tokens on A100 80 GB.
+ENV XLA_PYTHON_CLIENT_PREALLOCATE=true
+ENV XLA_CLIENT_MEM_FRACTION=0.95
+
+CMD ["python3", "run_alphafold.py"]

docker/README.md

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+```shell
+
+```
+
+```shell
+python run_alphafold.py \
+    --db_dir=/app/alphafold/alphafold3_database \
+    --json_path=/app/alphafold/alphafold_input.json \
+    --model_dir=/app/alphafold/models \
+    --output_dir=/app/alphafold/af_output \
+    --run_data_pipeline true \
+    --run_inference true
+```

docker/docker-compose.yml

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+version: '3.8'
+
+# DeepSpeed支持多种C++/CUDA扩展（ops），这些ops旨在优化深度学习的训练和推理过程。以下是一些主要的DeepSpeed ops及其功能：
+
+# FusedAdam - 提供融合优化的Adam优化器，适用于GPU。
+# FusedLamb - 类似FusedAdam，针对LAMB优化器，适用于大规模分布式训练。
+# SparseAttention - 用于高效计算稀疏注意力机制。
+# Transformer - 提供Transformer模型的高效实现。
+# TransformerInference - 专门用于Transformer模型的推理优化。
+# CPUAdam - 针对CPU优化的Adam优化器。
+# CPULion - 针对CPU的Lion优化器。
+# Quantizer - 提供量化支持，以减少模型大小和提高推理速度。
+# RandomLTD - 用于随机层裁剪的优化器。
+# StochasticTransformer - 支持随机Transformer模型的训练和推理。
+# 检测系统总内存（以GB为单位）
+# TOTAL_MEM=$(awk '/MemTotal/ {printf "%.0f\n", $2/1024/1024}' /proc/meminfo)
+# echo "Docker Compose 文件已生成，shm_size 设置为 ${TOTAL_MEM}GB。"
+
+services:
+  alphafold3:
+    build: 
+      context: .
+      dockerfile: Dockerfile
+      args: # PyTorch版本、Python版本与pytorch_lightning版本的对应关系表 https://blog.csdn.net/qq_41813454/article/details/137421822
+        PYTHON_VERSION: "3.10"
+        # CUDA_VERSION: "12.1.0"
+        TAG_VERSION: "12.4.1"
+    # env_file:
+    #   - .env
+    volumes:
+      - /mnt/d/alphafold3_database:/app/alphafold/alphafold3_database
+      - /mnt/d/models:/app/alphafold/models
+      - /mnt/d/alphafold_input.json:/app/alphafold/alphafold_input.json
+    container_name: alphafold3
+    pull_policy: if_not_present
+    ulimits:
+      memlock:
+        soft: -1
+        hard: -1
+    restart: unless-stopped
+    image: cford38/alphafold3:latest
+    privileged: true
+    cap_add:
+      - ALL
+      - CAP_SYS_PTRACE
+    shm_size: '32gb'
+    ipc: host
+    # ports:
+    #   - 3228:2222
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all
+      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
+    network_mode: host
+    # networks:
+    #   - network_finetune
+    command: ["tail", "-f", "/dev/null"]
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+
+# networks:
+#   network_finetune:
+#     name: network_finetune

docker/dockerignore

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+dockerignore
+Dockerfile