一些在Docker中比较难以安装的库(整理)

本文记录了一些在Docker中比较难以安装、频繁报错的库，如cuda、cudnn。

前言

在配置镜像时, 强烈建议将源更改为国内镜像站, 因为国外有些镜像站链接速度很慢, 更新也很慢, 很多库无法正确安装

我所使用的镜像站为sources.list:

deb-src http://archive.ubuntu.com/ubuntu xenial main restricted #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial main restricted
deb-src http://mirrors.aliyun.com/ubuntu/ xenial main restricted multiverse universe #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial-updates main restricted
deb-src http://mirrors.aliyun.com/ubuntu/ xenial-updates main restricted multiverse universe #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial universe
deb http://mirrors.aliyun.com/ubuntu/ xenial-updates universe
deb http://mirrors.aliyun.com/ubuntu/ xenial multiverse
deb http://mirrors.aliyun.com/ubuntu/ xenial-updates multiverse
deb http://mirrors.aliyun.com/ubuntu/ xenial-backports main restricted universe multiverse
deb-src http://mirrors.aliyun.com/ubuntu/ xenial-backports main restricted universe multiverse #Added by software-properties
deb http://archive.canonical.com/ubuntu xenial partner
deb-src http://archive.canonical.com/ubuntu xenial partner
deb http://mirrors.aliyun.com/ubuntu/ xenial-security main restricted
deb-src http://mirrors.aliyun.com/ubuntu/ xenial-security main restricted multiverse universe #Added by software-properties
deb http://mirrors.aliyun.com/ubuntu/ xenial-security universe
deb http://mirrors.aliyun.com/ubuntu/ xenial-security multiverse

在Dockerfile或者在容器内使用命令

RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak
COPY sources.list /etc/apt/sources.list

将源替换.

CUDA 9.0 开发者版

dockerfile如下:

FROM nvidia/cuda:9.0-runtime-ubuntu16.04
LABEL maintainer "Keavnn <https://stepneverstop.github.io>"

RUN apt-get update && apt-get install -y --allow-unauthenticated --no-install-recommends \
        cuda-libraries-dev-$CUDA_PKG_VERSION \
        cuda-nvml-dev-$CUDA_PKG_VERSION \
        cuda-minimal-build-$CUDA_PKG_VERSION \
        cuda-command-line-tools-$CUDA_PKG_VERSION \
        cuda-core-9-0=9.0.176.3-1 \
        cuda-cublas-dev-9-0=9.0.176.4-1 \
        libnccl-dev=$NCCL_VERSION-1+cuda9.0 && \
    rm -rf /var/lib/apt/lists/*

ENV LIBRARY_PATH /usr/local/cuda/lib64/stubs

—allow-unauthenticated 这句命令很重要, 不使用的话很有可能安装失败

cudnn 7.0.5

• https://developer.nvidia.com/rdp/cudnn-archive 下载cuDNN Libraries for Linux,不要下载 Power 8
• 把下载好的包上传到FTP服务器, 或者传输到容器内, 或者直接在容器中下载好
• cd到包位置
• cp cudnn-9.0-linux-x64-v7.solitairetheme8 cudnn-9.0-linux-x64-v7.tgz
• tar -xvf cudnn-9.0-linux-x64-v7.tgz
• cp include/* /usr/local/cuda-9.0/include
• cp lib64/* /usr/local/cuda-9.0/lib64
• chmod a+r /usr/local/cuda-9.0/include/cudnn.h /usr/local/cuda-9.0/lib64/libcudnn* 这一步如果cuda是base版本,则没有include文件夹,需要手动创建后再执行
• export PATH=/usr/local/cuda-9.0/bin:$PATH
• cd到/usr/local/cuda-9.0/lib64
• nano ~/.bashrc,关联环境变量
• 在最后一行加入export LD_LIBRARY_PATH=/home/cuda/lib64:$LD_LIBRARY_PATH
• source ~/.bashrc
• ldconfig -v
• 使用cat /usr/local/cuda-9.0/include/cudnn.h | grep CUDNN_MAJOR -A 2 查看cudnn版本
  

jemalloc

选择安装jemalloc,这个工具可以加速编译,碎片整理,具体请自行谷歌

• apt-get install autoconf
• apt-get install automake
• apt-get install libtool
• git clone https://github.com/jemalloc/jemalloc.git
• cd jemalloc
• git checkout 4.5.0安装4.5.0版本的jemalloc,5.x版本的有坑,深坑
• ./autogen.sh
• make
• make install_bin install_include install_lib,之所以不使用make install是因为会报错,如下:

Python3.6

记得sudo

• apt-get install software-properties-common
• add-apt-repository ppa:jonathonf/python-3.6, 按ENTER
• apt-get update && apt-get install python3.6 -y
• 修改系统默认的python版本为3.6
• cd /usr/bin, 保险起见,建议分两步
• rm python
• ln -s python3.6m python
• 如需更新,pip3 install --upgrade pip, 8.1.1->19.0.3
• python -V