【Docker】Docker:小白之路九(从0搭建自己的开发环境ubuntu16.04版:未完待续...)

Docker:小白之路九(从0搭建自己的开发环境ubuntu16.04版)

1. 拉取对应的镜像

cuda后面跟着版本号，对应的用深度学习再加上cudnn卷积加速库，在最后再指定一下对应的系统镜像

docker pull nvidia/cuda:10.2-cudnn7-denvel-ubuntu16.04

2. 制作数据卷

root@felaim-PC:~# docker run -v /home:/usr/Downloads --name dataVol nvidia/cuda:10.2-cudnn7-devel-ubuntu16.04 /bin/bash

3. 制作自己的容器

创建一个容器,挂载对应的数据卷

docker run -it --gpus all --name felaim_sever_ubuntu16.04 --volumes-from dataVol nvidia/cuda:10.2-cudnn7-devel-ubuntu16.04 /bin/bash

后面开始配置对应的环境吧!

4. 安装对应三方库

1. 更新源和环境

apt-get update apt-get upgrade

2. 安装wget

apt-get install wget curl git

3. 确定g++和gcc版本

最好是用5.4.0版本的,4.8.5对有些软件安装会有问题例如OpenCV4.2的GPU版本

gcc --version g++ --version

4. 安装cmake

cd cmake-3.14.0 ./bootstrap make make install cmake --version

5. 安装mlocate

apt-get install mlocate

6. 安装protobuf

tar -zxvf protobuf-3.7.1.tar.gz cd protobuf-3.7.1 ./autogen.sh: 37: ./autogen.sh: autoreconf: not found 安装依赖项 apt-get install autoconf automake libtool ./autogen.sh ./configure make -j8 make install ldconfig protoc --version

7. 安装eigen

下载对应安装包

mkdir build cd build cmake .. make install

8.安装bazel

apt-get install unzip ./bazel-0.24.1-installer-linux-x86_64.sh --user export PATH="$PATH:$HOME/bin"

9. vim

apt-get install vim

10. TensorRT

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>

11. tensorflow1.14

./configure中path的设置 /usr/local/cuda,/usr/local/cuda/bin,/usr/local/cuda/lib64,/usr/local/cuda/include,/home/software/TensorRT-6.0.1.8,/usr/lib/x86_64-linux-gnu,/usr/include,/usr/lib64 bazel build --config=opt --config=cuda //tensorflow:libtensorflow_cc.so cd tensorflow/contrib/makefile sh build_all_linux.sh

/usr/bin/env: 'python': No such file or directory apt-get install python

ERROR: /root/.cache/bazel/_bazel_root/79617ab99e45fd3cf89b3b67f497350d/external/nccl_archive/BUILD.bazel:67:1: fatbinary external/nccl_archive/device_dlink_hdrs.fatbin failed (Exit 1) fatbinary fatal : Unknown option '-bin2c-path' Target //tensorflow:libtensorflow_cc.so failed to build Use --verbose_failures to see the command lines of failed build steps. tensorflow-r1.14/third_party/nccl/build_defs.bzl.tpl 删除"--bin2c-path=%s" % bin2c.dirname,这行

12. 安装OpenCV 4.2版本

安装OpenCV4.2版本,安装对应依赖项

sudo apt-get install build-essential [required] sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev [optional] sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

git clone https://github.com/opencv/opencv.git git clone https://github.com/opencv/opencv_contrib.git cd opencv_contrib git checkout 4.2.0 cd ../opencv git checkout 4.2.0 mkdir build cd build cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_OPENGL=ON -D WITH_CUDA=ON -D ENABLE_FAST_MATH=1 -D CUDA_FAST_MATH=1 -D CUDA_NVCC_FLAGS="-D_FORCE_INLINES" -D WITH_CUBLAS=1 -D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules/ -D CUDA_ARCH_BIN=5.3,6.0,6.1,7.0,7.5 -DCUDA_ARCH_PTX=7.5 -DOPENCV_ENABLE_NONFREE=ON -D BUILD_EXAMPLES=ON -D BUILD_opencv_world=ON .. make -j4 make install

参考地址
1.https://gitlab.com/nvidia/container-images/cuda/-/tree/ubuntu16.04