106 lines
3.5 KiB
YAML
106 lines
3.5 KiB
YAML
version: '3.8'
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# 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：
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# FusedAdam - 提供融合优化的Adam优化器，适用于GPU。
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# FusedLamb - 类似FusedAdam，针对LAMB优化器，适用于大规模分布式训练。
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# SparseAttention - 用于高效计算稀疏注意力机制。
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# Transformer - 提供Transformer模型的高效实现。
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# TransformerInference - 专门用于Transformer模型的推理优化。
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# CPUAdam - 针对CPU优化的Adam优化器。
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# CPULion - 针对CPU的Lion优化器。
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# Quantizer - 提供量化支持，以减少模型大小和提高推理速度。
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# RandomLTD - 用于随机层裁剪的优化器。
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# StochasticTransformer - 支持随机Transformer模型的训练和推理。
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# 检测系统总内存（以GB为单位）
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# TOTAL_MEM=$(awk '/MemTotal/ {printf "%.0f\n", $2/1024/1024}' /proc/meminfo)
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# echo "Docker Compose 文件已生成，shm_size 设置为 ${TOTAL_MEM}GB。"
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services:
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ubuntu-finetune:
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build:
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context: .
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dockerfile: Dockerfile.zly
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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
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PYTHON_VERSION: "3.10"
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GO_VERSION: "1.21.13"
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NV_PEER_MEM_VERSION: "1.2"
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TAG_VERSION: "12.1.1-cudnn8-devel-ubuntu22.04"
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PYTORCH_VERSION: "2.3.0"
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TORCHVISION_VERSION: "0.18.0"
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TORCHAUDIO_VERSION: "2.3.0"
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DS_BUILD_OPS: 1
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USE_CUDA: 1
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USE_ROCM: 0
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USE_XPU: 0
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CUDA: "cu121"
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CUDA_ARCH_LIST: "80;89;90" # for RTX 4090, all : "80;86;89;90"
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TORCH_CUDA_ARCH_LIST: "8.0;8.9;9.0+PTX" # all "6.0;6.1;6.2;7.0;7.5;8.0;8.6;8.9;9.0"
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SETUPTOOLS_VERSION: "69.5.1"
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# Hopper (H100): 90a
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# Ampere (A100, RTX 30 series): 80
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# Turing (RTX 20 series, Titan RTX): 75
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# Volta (V100): 70
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# Pascal (P100, GTX 10 series): 60;61
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# Maxwell (GTX 900 series): 50;53
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DCUTLASS_NVCC_ARCHS: "80;89,90a" # 90a for H100 GPU 89:GeForce RTX 4090
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DEEPSPEED_VERSION: "master"
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DEEPSPEED_INSTALL_FLAGS: "--allow_sudo"
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# HTTP_PROXY: "http://127.0.0.1:15777"
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# HTTPS_PROXY: "http://127.0.0.1:15777"
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CACHEBUST: 1
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NV_DRIVER_VERSION: "535"
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GO_VERSION: "1.21.13"
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env_file:
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- .env
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# volumes:
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# - ../src:/work
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container_name: ubuntu-finetune
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pull_policy: if_not_present
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ulimits:
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memlock:
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soft: -1
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hard: -1
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# tty: true
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# stdin_open: true
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restart: unless-stopped
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image: hotwa/deepspeed:pt23_828
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privileged: true
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cap_add:
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- ALL
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- CAP_SYS_PTRACE
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shm_size: '32gb'
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# devices:
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# - /dev/infiniband/rdma_cm
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# - /dev/infiniband/uverbs0
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# - /dev/infiniband/uverbs1
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# - /dev/infiniband/uverbs2
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# - /dev/infiniband/uverbs3
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# - /dev/infiniband/uverbs4
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# - /dev/infiniband/uverbs5
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# - /dev/infiniband/uverbs6
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# - /dev/infiniband/uverbs7
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# - /dev/infiniband/uverbs8
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ipc: host
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# ports:
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# - 3228:2222
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility
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- TMPDIR=/var/tmp
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network_mode: host
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# networks:
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# - network_finetune
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command: ["/usr/sbin/sshd", "-D"]
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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# networks:
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# network_finetune:
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# name: network_finetune
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