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:
  ubuntu-finetune:
    build: 
      context: .
      dockerfile: Dockerfile.update
      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"
        NV_PEER_MEM_VERSION: "1.2"
        # CUDA_VERSION: "12.1.0"
        TAG_VERSION: "12.4.1"
        PYTORCH_VERSION: "2.3.0"
        TORCHVISION_VERSION: "0.18.0"
        TORCHAUDIO_VERSION: "2.3.0"
        DS_BUILD_OPS: 1
        USE_CUDA: 1
        USE_ROCM: 0
        USE_XPU: 0
        CUDA: cu121
        CUDA_ARCH_LIST: "80;90" # for RTX 4090, all : "80;86;89;90"
        TORCH_CUDA_ARCH_LIST: "8.0;9.0+PTX" # all "6.0;6.1;6.2;7.0;7.5;8.0;8.6;8.9;9.0"
        SETUPTOOLS_VERSION: "69.5.1"
# Hopper (H100): 90a
# Ampere (A100, RTX 30 series): 80
# Turing (RTX 20 series, Titan RTX): 75
# Volta (V100): 70
# Pascal (P100, GTX 10 series): 60;61
# Maxwell (GTX 900 series): 50;53
        DCUTLASS_NVCC_ARCHS: "80;90a" # 90a for H100 GPU 89:GeForce RTX 4090 
        DEEPSPEED_VERSION: "master"
        DEEPSPEED_INSTALL_FLAGS: "--allow_sudo"
        # HTTP_PROXY: "http://127.0.0.1:15777"
        # HTTPS_PROXY: "http://127.0.0.1:15777"
        CACHEBUST: 1
        NV_DRIVER_VERSION: "535"
        GO_VERSION: "1.21.13"
    env_file:
      - .env
    volumes:
      - ../src:/work
      - /root/PDF-Extract-Kit/models:/models
      - /mnt/beegfs/downloads:/mnt/beegfs/downloads
      - ./results:/results
      - /mnt/beegfs/pdf_clean/yanbaodata:/data
      - ./start_mineru_update_mpi.py:/start_mineru_update_mpi.py
      - ./magic-pdf.json:/root/magic-pdf.json
      - ./hostfile:/hostfile
      - ./hostfile_mpich:/hostfile_mpich
      - ./mpi_test.py:/mpi_test.py
    container_name: ubuntu-finetune
    pull_policy: if_not_present
    ulimits:
      memlock:
        soft: -1
        hard: -1
    # tty: true
    # stdin_open: true
    restart: unless-stopped
    image: hotwa/deepspeed:pt23_update
    privileged: true
    cap_add:
      - ALL
      - CAP_SYS_PTRACE
    shm_size: '2000gb'
    devices:
      - /dev/infiniband/rdma_cm
      - /dev/infiniband/uverbs0
      - /dev/infiniband/uverbs1
      - /dev/infiniband/uverbs2
      - /dev/infiniband/uverbs3
      - /dev/infiniband/uverbs4
      - /dev/infiniband/uverbs5
      - /dev/infiniband/uverbs6
      - /dev/infiniband/uverbs7
      - /dev/infiniband/uverbs8
    ipc: host
    # ports:
    #   - 3228:2222
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
      - TMPDIR=/var/tmp
      - MAGIC_PDF_METHOD=auto
      - MAGIC_PDF_MODEL_MODE=full
      - MAGIC_PDF_INSIDE_MODEL=true
      - MAX_PROCESSES_PER_GPU=10
      - PDF_DIR=/data
      - UCX_NET_DEVICES=mlx5_0:1,mlx5_1:1,mlx5_2:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1,mlx5_8:1
    network_mode: host
    # networks:
    #   - network_finetune
    command: ["/usr/sbin/sshd", "-D"]
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

# networks:
#   network_finetune:
#     name: network_finetune