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docker中使用GPU+rocksdb的詳細(xì)教程

更新時(shí)間：2023年10月16日 10:21:05 作者：naturliche

這篇文章主要介紹了docker中使用GPU+rocksdb,本文給大家介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或工作具有一定的參考借鑒價(jià)值，需要的朋友可以參考下

配置環(huán)境

 dell@dell-Precision-3630-Tower  ~  lsb_release -a
No LSB modules are available.
Distributor ID:	Ubuntu
Description:	Ubuntu 20.04.6 LTS
Release:	20.04
Codename:	focal

dell@dell-Precision-3630-Tower  ~  nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0

 dell@dell-Precision-3630-Tower  ~  docker version
Client: Docker Engine - Community
 Version:           24.0.6
 API version:       1.43
 Go version:        go1.20.7


 OS/Arch:           linux/amd64
 Context:           default

Server: Docker Engine - Community
 Engine:
  Version:          24.0.6
  API version:      1.43 (minimum version 1.12)
  Go version:       go1.20.7

  OS/Arch:          linux/amd64
  Experimental:     false
 containerd:
  Version:          1.6.24

 runc:
  Version:          1.1.9

 docker-init:
  Version:          0.19.0


#安裝方式：sudo apt-get install libcudnn8-dev=8.9.2.26-1+cuda11.8
cudnn:libcudnn8-dev=8.9.2.26-1+cuda11.8

目錄結(jié)構(gòu)

nvidia-docker和從docker 19開始提供的nvidia-container-toolkit的區(qū)別：

nvidia-docker 概述：

nvidia-docker 是最初用于在 Docker 容器中提供 GPU 支持的工具。
命令：nvidia-docker 具有自己的命令行工具，并且最初被設(shè)計(jì)為 docker 命令的替代品。你可以用 nvidia-docker run 來啟動(dòng)一個(gè)使用 GPU 的容器。
插件：nvidia-docker 版本 1 和 2 都使用了 Docker 插件系統(tǒng)。版本 2 是 Docker 插件的一種形式，允許用戶使用 --runtime=nvidia 標(biāo)志與標(biāo)準(zhǔn) docker 命令一起使用。

nvidia-container-toolkit

概述：在 Docker 19.03 版本之后，Docker 引入了一個(gè)名為 GPU 的設(shè)備請(qǐng)求特性。nvidia-container-toolkit 是一個(gè)新的工具，允許用戶使用這個(gè)新特性，而不再需要 nvidia-docker 的自定義運(yùn)行時(shí)。
命令：與使用 nvidia-docker 不同，使用 nvidia-container-toolkit，你可以使用常規(guī)的 docker 命令，但是添加一個(gè) --gpus 參數(shù)來啟用 GPU 支持。例如：docker run --gpus all nvidia/cuda:10.0-base nvidia-smi。
集成：它更緊密地集成到 Docker CLI 中，允許更好的兼容性和使用體驗(yàn)。

比較和推薦使用

nvidia-docker 版本 1 已經(jīng)棄用，而版本 2 在某些用例中仍然被使用，但逐漸被 nvidia-container-toolkit 替代。
對(duì)于 Docker 19.03 及更高版本，官方推薦使用 nvidia-container-toolkit，因?yàn)樗峁┝艘粋€(gè)更簡潔和標(biāo)準(zhǔn)的方式來在容器中使用 GPU。
使用 nvidia-container-toolkit 允許開發(fā)者和運(yùn)維團(tuán)隊(duì)在不更改工作流的情況下，簡單地將 GPU 支持添加到他們現(xiàn)有的 Docker 容器中。
盡管在一些老的代碼和項(xiàng)目中你仍然可能會(huì)看到 nvidia-docker 的使用，但新的項(xiàng)目和開發(fā)通常應(yīng)該使用 nvidia-container-toolkit，除非有明確的理由不這樣做。

docker安裝GPU工具箱nvidia-container-toolkit

參考鏈接：

https://zhuanlan.zhihu.com/p/544713249

sudo apt install curl
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 tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

docker拉取含cuda的鏡像建立鏡像

去Nvidia官網(wǎng)下載cuda版本的Docker：https://hub.docker.com/r/nvidia/cuda

images包含的三種風(fēng)格：

base: Includes the CUDA runtime (cudart)
runtime: Builds on the base and includes the CUDA math libraries, and NCCL. A runtime image that also includes cuDNN is available.
devel: Builds on the runtime and includes headers, development tools for building CUDA images. These images are particularly useful for multi-stage builds.

NVIDIA Container Toolkit

The NVIDIA Container Toolkit for Docker is required to run CUDA images.

For CUDA 10.0, nvidia-docker2 (v2.1.0) or greater is recommended. It is also recommended to use Docker 19.03.

還是自己寫一個(gè)鏡像吧，該鏡像擁有cudn，rocksdb環(huán)境

# from official ubuntu 20.04
# FROM ubuntu:20.04
# docker pull nvidia/cuda:11.8.0-devel-ubuntu20.04
FROM nvidia/cuda:11.8.0-devel-ubuntu20.04

# RUN mv /etc/apt/sources.list /etc/apt/sources_backup.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal main restricted " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-updates main restricted " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal universe " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-updates universe " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal multiverse " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-updates multiverse " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-backports main restricted universe multiverse " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-security main restricted " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-security universe " >> /etc/apt/sources.list && \
# echo "deb http://mirrors.ustc.edu.cn/ubuntu/ focal-security multiverse " >> /etc/apt/sources.list && \
# echo "deb http://archive.canonical.com/ubuntu focal partner " >> /etc/apt/sources.list
# update system
RUN ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && echo 'Asia/Shanghai' >/etc/timezone \ 
    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub \
    && apt clean && apt update && apt install -yq --no-install-recommends sudo \
    && sudo apt install -yq --no-install-recommends python3 python3-pip libgl1-mesa-glx libglib2.0-0 libsm6 libxext6 libxrender-dev openssh-server \
    && sudo pip3 install --upgrade pip \
    && sudo pip3 config set global.index-url https://mirrors.aliyun.com/pypi/simple \
    && sudo pip3 install setuptools

RUN apt-get update && apt-get upgrade -y
# install basic tools
RUN apt-get install -y vim wget curl
# install tzdata noninteractive
RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata
# install git and default compilers
RUN apt-get install -y git gcc g++ clang clang-tools
# install basic package
RUN apt-get install -y lsb-release software-properties-common gnupg
# install gflags, tbb
RUN apt-get install -y libgflags-dev libtbb-dev
# install compression libs
RUN apt-get install -y libsnappy-dev zlib1g-dev libbz2-dev liblz4-dev libzstd-dev
# install cmake
RUN apt-get install -y cmake
RUN apt-get install -y libssl-dev
# install clang-13
WORKDIR /root
RUN wget https://apt.llvm.org/llvm.sh
RUN chmod +x llvm.sh
RUN ./llvm.sh 13 all
# install gcc-7, 8, 10, 11, default is 9
RUN apt-get install -y gcc-7 g++-7
RUN apt-get install -y gcc-8 g++-8
RUN apt-get install -y gcc-10 g++-10
RUN echo "deb https://ppa.launchpadcontent.net/ubuntu-toolchain-r/test/ubuntu focal main" |tee -a /etc/apt/sources.list
RUN echo "deb-src https://ppa.launchpadcontent.net/ubuntu-toolchain-r/test/ubuntu focal main" |tee -a /etc/apt/sources.list
RUN curl -sL "http://keyserver.ubuntu.com/pks/lookup?op=get&search=0x60C317803A41BA51845E371A1E9377A2BA9EF27F" |apt-key add
#RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 60C317803A41BA51845E371A1E9377A2BA9EF27F
RUN add-apt-repository -y ppa:ubuntu-toolchain-r/test
RUN apt-get update && apt-get upgrade -y
#RUN apt-get install -y gcc-11 g++-11
# install apt-get install -y valgrind
RUN apt-get install -y valgrind
# install folly depencencies
RUN apt-get install -y libgoogle-glog-dev
# install openjdk 8
RUN apt-get install -y openjdk-8-jdk
ENV JAVA_HOME /usr/lib/jvm/java-1.8.0-openjdk-amd64
# install mingw
RUN apt-get install -y mingw-w64

# install gtest-parallel package
RUN git clone --single-branch --branch master --depth 1 https://github.com/google/gtest-parallel.git ~/gtest-parallel
ENV PATH $PATH:/root/gtest-parallel

# install libprotobuf for fuzzers test
RUN apt-get install -y ninja-build binutils liblzma-dev libz-dev pkg-config autoconf libtool
#解決GnuTLS recv error
RUN apt-get update
RUN apt-get upgrade
RUN apt-get install --reinstall ca-certificates
RUN git clone --branch v1.0 https://github.com/google/libprotobuf-mutator.git ~/libprotobuf-mutator && cd ~/libprotobuf-mutator && git checkout ffd86a32874e5c08a143019aad1aaf0907294c9f && mkdir build && cd build && cmake .. -GNinja -DCMAKE_C_COMPILER=clang-13 -DCMAKE_CXX_COMPILER=clang++-13 -DCMAKE_BUILD_TYPE=Release -DLIB_PROTO_MUTATOR_DOWNLOAD_PROTOBUF=ON && ninja && ninja install
ENV PKG_CONFIG_PATH /usr/local/OFF/:/root/libprotobuf-mutator/build/external.protobuf/lib/pkgconfig/
ENV PROTOC_BIN /root/libprotobuf-mutator/build/external.protobuf/bin/protoc

#install the latest google benchmark
RUN git clone --depth 1 --branch v1.7.0 https://github.com/google/benchmark.git ~/benchmark
RUN cd ~/benchmark && mkdir build && cd build && cmake .. -GNinja -DCMAKE_BUILD_TYPE=Release -DBENCHMARK_ENABLE_GTEST_TESTS=0 && ninja && ninja install

# # clean up
# RUN rm -rf /var/lib/apt/lists/*
# RUN rm -rf /root/benchmark

#以下為build-image.sh
#!/usr/bin/env bash
SHELL_HOME=$(
  cd "$(dirname "$0")" || exit
  pwd
)
source "${SHELL_HOME}/../dev.conf"

# docker build --build-arg \
#   --build-arg http_proxy= xxx\
#   --build-arg https_proxy= xxx\
#   --build-arg all_proxy=socks5 \
#   --tag "${IMAGE_NAME}:${IMAGE_VERSION}" "${SHELL_HOME}"

docker build --tag "${IMAGE_NAME}:${IMAGE_VERSION}" "${SHELL_HOME}"

運(yùn)行容器

參考鏈接：https://blog.csdn.net/Maid_Li/article/details/124952650

在啟動(dòng)docker容器的時(shí)候要注意加一些cuda的參數(shù)

--gpus all和-e NVIDIA_VISIBLE_DEVICES=all選擇這個(gè)容器可見的顯卡，直接全部就完事了
-e NVIDIA_DRIVER_CAPABILITIES=compute,utility配置了一些cuda必備的包如nvidia-smi之類的
以下為start.sh

#!/usr/bin/env bash

#當(dāng)前腳本路徑
SHELL_HOME=$(
  cd "$(dirname "$0")" || exit
  pwd
)
source "${SHELL_HOME}"/../dev.conf
source "${SHELL_HOME}"/utilities/rocks.conf

CONTAINER_NAME="rocksdb-gpu"

# work dir inside the dev container
SOURCE_DIR_INSIDE="/home/baum/GPU_ROCKS"
#本地源代碼目錄 
SOURCE_DIR="/nvme/baum/git-project/GPU_ROCKS"
WORK_DIR=/rocks
RECREATE_CONTAINER=""

#我執(zhí)行的./start.sh -s /nvme/baum/git-project/GPU_ROCKS
function show_usage() {
  echo "
  Start a gdb container for Rocksdb.

  Usage:
    ./start.sh
    ./start.sh -s /path/to/your/cockroachdb/home


  Options:
    -s                Project path of crdb, default is '${HOME}/go/src/github.com/cockroachdb'.
    -r                Recreate the dev container.
    -h                Show this message.
  "
  exit
}

while getopts "s:hr" opt; do
  case $opt in
  s)
    SOURCE_DIR=${OPTARG}
    ;;
  r)
    RECREATE_CONTAINER="true"
    ;;
  h)
    show_usage
    ;;
  *)
    show_usage
    ;;
  esac
done

CONTAINER_RUNNING=$(docker container ls | grep "${CONTAINER_NAME}")
CONTAINER_EXISTED=$(docker container ls -a | grep "${CONTAINER_NAME}")

if [[ ${RECREATE_CONTAINER} == "true" && -n ${CONTAINER_EXISTED} ]]; then
  echo "remove the existing rocksdb-gpu container ..."
  docker rm -f "${CONTAINER_NAME}"
  CONTAINER_EXISTED=""
fi

echo "current SOURCE_DIR is '${SOURCE_DIR}'"

if [[ -z ${CONTAINER_EXISTED} ]]; then
  echo "staring the rocksdb-gpu environment 1 ..."
  #-v 掛載目錄，將前一個(gè)映射到后一個(gè)
  docker run -it -v "${SOURCE_DIR}":/rocks \
    -v "${SOURCE_DIR}":${SOURCE_DIR_INSIDE} \
    --name ${CONTAINER_NAME} \
    --publish "${ROCKS_PORT}"-"${GDB_PORT}":"${ROCKS_PORT}"-"${GDB_PORT}" \
    --network=rocksdb-br \
    --gpus all \
    -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
    -e NVIDIA_VISIBLE_DEVICES=all \
    --workdir ${WORK_DIR} \
    "${IMAGE_NAME}:${IMAGE_VERSION}" \
    bash
  exit
fi

if [[ -z ${CONTAINER_RUNNING} ]]; then
  echo "starting rocksdb-gpu environment 2 ..."
  docker start "${CONTAINER_NAME}"
fi

echo "logging into rocksdb-gpu environment '${CONTAINER_NAME}' ..."
docker exec -it "${CONTAINER_NAME}" bash

網(wǎng)絡(luò)配置

本地16017-16019映射到容器16017-16019

#init-docker-network.sh
#!/usr/bin/env bash
SHELL_HOME=$(
  cd "$(dirname "$0")" || exit
  pwd
)
source "${SHELL_HOME}"/dev.conf
echo "create network bridge for rocks ..."
docker network create --subnet="${SUBNET}" "${BRIDGE_NAME}"
docker network list

參考鏈接：

https://github.com/cnstark/pytorch-docker/blob/main/scripts/build_2.0.1_py3.9.17_cuda11.8.0_devel_ubuntu20.04.sh

https://zhuanlan.zhihu.com/p/544713249

到此這篇關(guān)于docker中使用GPU+rocksdb的文章就介紹到這了,更多相關(guān)docker使用GPU內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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