Docker部署Yolo v5 / v7项目_docker yolov5

作者：小桥流水78 | 2024-07-20 22:38:42

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docker yolov5

一：系统环境

1.Ubuntu 22.04.3 LTS


lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ cat /etc/os-release
PRETTY_NAME="Ubuntu 22.04.4 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.4 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
UBUNTU_CODENAME=jammy
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$

2.安装Nvidia驱动以及Cuda驱动

Nvidia驱动下载地址：Official Drivers | NVIDIA

下载对应系统的驱动版本

Cuda驱动：CUDA Toolkit 12.4 Update 1 Downloads | NVIDIA Developer

安装好之后请自行核对驱动是否正确


lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ nvidia-smi
Wed May  1 21:14:55 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06              Driver Version: 545.29.06    CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla P40                      Off | 00000000:02:00.0 Off |                  Off |
| N/A   37C    P0              52W / 250W |    174MiB / 24576MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
 
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A      1550      G   /usr/lib/xorg/Xorg                           22MiB |
|    0   N/A  N/A      3907    C+G   ...libexec/gnome-remote-desktop-daemon      149MiB |
+---------------------------------------------------------------------------------------+
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Mar_28_02:18:24_PDT_2024
Cuda compilation tools, release 12.4, V12.4.131
Build cuda_12.4.r12.4/compiler.34097967_0
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$

3.Docker以及Docker-Compose

需要提前安装好Docker，最好Docker版本在19.x.x 以上，19版本以下的需要安装Nvidia-docker

Docker19 和 Docker-compose2.x 开始提供调用显卡的支持，除此之外还需要安装 nvidia-container-toolkit 用于提供Docker Container调用Nvidia显卡的支持。

Docker/Docker-compose的安装可以见我的另一篇博客：Linux离线安装Docker，Docker-compose适用于CentOS7，RockyLinux8，Ubuntu22.04，Ubuntu20.04-CSDN博客


lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ docker -v
Docker version 26.1.0, build 9714adc
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ docker-compose -v
Docker Compose version v2.26.1
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$

4.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 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

二：Dockerfile文件

我这是是基于Ubuntu 20.04 系统 Cuda 12.1 pytorch 2.2.2 版本构建文件

BaseImage

可以去Nvidia的官方Docker 镜像仓库中搜索你需要使用的Cuda版本以及对应系统的镜像

我这里选定的是 12.2.0-base-ubuntu20.04 因为我的系统是Ubuntu 22.04 我比较熟悉Ubuntu系

Python Version

去Python的官方下载地址中选择你所使用的Python版本

我开发使用的是 Python 3.9.18 所以我的Dockerfilw文件中就自动安装了Python 3.9.18版本

PyTorch Version

我选用的是 PyTorch 2.2.2 原本在我写文章测试的时候还是最新版本，现在最新发布了2.3.0

直接进入PyTorch 的历史版本页面，选择你需要的版本：

基于这些基础环境构建Dockerfile文件，基于官方Cuda镜像 nvidia/cuda:12.2.0-base-ubuntu20.04
，安装Python 3.9.18 之后安装Yolo v5运行时所需要的包，并安装PyTorch，其中具体的版本搭配需要看你所需的版本


# CUDA基础镜像
FROM nvidia/cuda:12.2.0-base-ubuntu20.04
 
# 安装基础包
RUN apt update && \
    apt install -y \
        wget build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev \
        libreadline-dev libffi-dev libsqlite3-dev libbz2-dev liblzma-dev && \
    apt clean && \
    rm -rf /var/lib/apt/lists/*
 
WORKDIR /temp
 
# 下载python
RUN wget https://www.python.org/ftp/python/3.9.18/Python-3.9.18.tgz && \
    tar -xvf Python-3.9.18.tgz
 
# 编译&安装python
RUN cd Python-3.9.18 && \
    ./configure --enable-optimizations && \
    make && \
    make install
 
WORKDIR /workspace
 
RUN rm -r /temp && \
    ln -s /usr/local/bin/python3 /usr/local/bin/python && \
    ln -s /usr/local/bin/pip3 /usr/local/bin/pip
 
# 安装 pytorch
RUN pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu121 
 
# 安装需要的插件
RUN pip3 install absl-py==2.1.0 Brotli==1.0.9 cachetools==5.3.3 certifi==2024.2.2 \
    charset-normalizer==3.3.2 colorama==0.4.6 contourpy==1.1.1 cycler==0.12.1 \
    filelock==3.13.1 fonttools==4.49.0 fsspec==2023.4.0 gmpy2==2.1.2 \
    google-auth==2.28.1 google-auth-oauthlib==1.0.0 grpcio==1.62.0 idna==3.6 \
    importlib-metadata==7.0.1 importlib-resources==6.1.2 Jinja2==3.1.2 \
    kiwisolver==1.4.5 Markdown==3.5.2 MarkupSafe==2.1.3 matplotlib==3.7.5 \
    mpmath==1.3.0 opencv-python==4.9.0.80 opencv-python-headless==4.9.0.80 \
    networkx==3.0 numpy==1.23.5 oauthlib==3.2.2 \
    packaging==23.2 pandas==2.0.3 pillow==10.2.0 protobuf==4.25.3 pyasn1==0.5.1 \
    pyasn1-modules==0.3.0 pycocotools==2.0.7 pyparsing==3.1.1 PySocks==1.7.1 \
    python-dateutil==2.9.0.post0 pytz==2024.1 PyYAML==6.0.1 requests==2.31.0 \
    requests-oauthlib==1.3.1 rsa==4.9 scipy==1.10.1 seaborn==0.13.2 \
    setuptools==68.2.2 six==1.16.0 sympy==1.12 tensorboard==2.14.0 \
    tensorboard-data-server==0.7.2 thop==0.1.1.post2209072238 tqdm==4.66.2 \
    triton==2.2.0 typing-extensions==4.8.0 tzdata==2024.1 urllib3==2.1.0 \
    Werkzeug==3.0.1 wheel==0.41.2 zipp==3.17.0  && \
    rm -r /root/.cache/pip
 
# 安装 ffmpeg
ENV TZ=Asia/Shanghai \
    DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
 
WORKDIR /yoloTrain

构建镜像

docker build -t image_name:image_tag -f ./dockerfile_path .


lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ docker build -t lemon/pytorch-yolo5:test_2 -f ./Dockerfile-py3918-cuda121-pytorch222 .
[+] Building 0.1s (15/15) FINISHED                                                                                                                                                                 docker:default
 => [internal] load build definition from Dockerfile-py3918-cuda121-pytorch222                                                                                                                               0.0s
 => => transferring dockerfile: 2.40kB                                                                                                                                                                       0.0s
 => [internal] load metadata for docker.io/nvidia/cuda:12.2.0-base-ubuntu20.04                                                                                                                               0.0s
 => [internal] load .dockerignore                                                                                                                                                                            0.0s
 => => transferring context: 2B                                                                                                                                                                              0.0s
 => [ 1/11] FROM docker.io/nvidia/cuda:12.2.0-base-ubuntu20.04                                                                                                                                               0.0s
 => CACHED [ 2/11] RUN apt update &&     apt install -y         wget build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev         libreadline-dev libffi-dev libsqlite3-dev libbz2  0.0s
 => CACHED [ 3/11] WORKDIR /temp                                                                                                                                                                             0.0s
 => CACHED [ 4/11] RUN wget https://www.python.org/ftp/python/3.9.18/Python-3.9.18.tgz &&     tar -xvf Python-3.9.18.tgz                                                                                     0.0s
 => CACHED [ 5/11] RUN cd Python-3.9.18 &&     ./configure --enable-optimizations &&     make &&     make install                                                                                            0.0s
 => CACHED [ 6/11] WORKDIR /workspace                                                                                                                                                                        0.0s
 => CACHED [ 7/11] RUN rm -r /temp &&     ln -s /usr/local/bin/python3 /usr/local/bin/python &&     ln -s /usr/local/bin/pip3 /usr/local/bin/pip                                                             0.0s
 => CACHED [ 8/11] RUN pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu121                                                                    0.0s
 => CACHED [ 9/11] RUN pip3 install absl-py==2.1.0 Brotli==1.0.9 cachetools==5.3.3 certifi==2024.2.2     charset-normalizer==3.3.2 colorama==0.4.6 contourpy==1.1.1 cycler==0.12.1     filelock==3.13.1 fon  0.0s
 => CACHED [10/11] RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y                                                                                                                          0.0s
 => CACHED [11/11] WORKDIR /yoloTrain                                                                                                                                                                        0.0s
 => exporting to image                                                                                                                                                                                       0.0s
 => => exporting layers                                                                                                                                                                                      0.0s
 => => writing image sha256:7a558d670b90c1b8939c735a952e6c31ef51744c945406d0c6da931eb3a43399                                                                                                                 0.0s
 => => naming to docker.io/lemon/pytorch-yolo5:test_2                                                                                                                                                        0.0s
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ docker images
REPOSITORY            TAG                                            IMAGE ID       CREATED         SIZE
lemon/pytorch-yolo5   test_1                                         7a558d670b90   3 hours ago     10.2GB
lemon/pytorch-yolo5   test_2                                         7a558d670b90   3 hours ago     10.2GB
lemon/pytorch         pytorch2.2.2-py3.8.10-cuda12.2.0-ubuntu20.04   0e94c70d8f33   2 days ago      51.9GB
lemon/pytorch-yolo    v1                                             5a9259d2aead   2 days ago      9.9GB
lemon/pytorch_test    pytorch2.2.2-py3.8.10-cuda12.2.0-ubuntu20.04   5a9259d2aead   2 days ago      9.9GB
lemon/pytorch         2.2.2-py3.9.10-cuda12.2.0-ubuntu20.04          6ae13bdc8b55   2 days ago      6.29GB
lemon/pytorch         pytorch2.2.2-py3.9.10-cuda12.2.0-ubuntu20.04   6ae13bdc8b55   2 days ago      6.29GB
nginx                 latest                                         7383c266ef25   7 days ago      188MB
redis                 latest                                         7fc37b47acde   3 weeks ago     116MB
mysql                 latest                                         6f343283ab56   5 weeks ago     632MB
nvidia/cuda           12.2.0-base-ubuntu20.04                        2664f46b9de0   5 months ago    242MB
hello-world           latest                                         d2c94e258dcb   12 months ago   13.3kB
mysql                 8.0.17                                         b8fd9553f1f0   4 years ago     445MB
lemon@lemonPrecisionTower:~/my_file/yolo5_docker$

构建成功就可以看到刚才打包的镜像，后续我们基于刚才的镜像运行容器

三：Docker-Compose

准备工作

在Docker-compose文件中我们将Yolo v5的代码挂载进Dockerfile中创建的工作目录：/yoloTrain

在此之前需要你先调通程序并调试好代码，上面Dockerfile中安装的运行时环境包也可以根据你的运行环境自行更改。

运行容器


version: '3.8'
 
services:
  cuda121_yolo_5:
    image: lemon/pytorch-yolo5:test_1
    container_name: cuda121_yolo_5
    stdin_open: true
    tty: true
    shm_size: 200G
    volumes:
      - /etc/localtime:/etc/localtime:ro
      - /home/lemon/my_file/yolo_v5/:/yoloTrain/
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]


lemon@lemonPrecisionTower:~/my_file/yolo5_docker$ docker-compose -f docker-compose-yolo5-train.yml up
WARN[0000] /home/lemon/my_file/yolo5_docker/docker-compose-yolo5-train.yml: `version` is obsolete
[+] Running 1/0
 ✔ Container cuda121_yolo_5  Created                                                                                                                                                                         0.0s
Attaching to cuda121_yolo_5

稍后容器启动后进入容器中，尝试运行训练代码：


lemon@lemonPrecisionTower:~/my_file/yolo_v5/yolov5-5.0$ docker ps
CONTAINER ID   IMAGE                        COMMAND                   CREATED       STATUS         PORTS                                                  NAMES
f6a265b97218   lemon/pytorch-yolo5:test_1   "/bin/bash"               3 hours ago   Up 2 minutes                                                          cuda121_yolo_5
e5cacb5c9f33   mysql:8.0.17                 "docker-entrypoint.s…"   6 days ago    Up 12 hours    0.0.0.0:3306->3306/tcp, :::3306->3306/tcp, 33060/tcp   mysql_data-mysql_8_0_17-1
lemon@lemonPrecisionTower:~/my_file/yolo_v5/yolov5-5.0$ docker exec -it cuda121_yolo_5 /bin/bash
root@f6a265b97218:/yoloTrain# cd yolov5-5.0
root@f6a265b97218:/yoloTrain/yolov5-5.0# python train.py

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