一文多图搞定Ubuntu安装Anaconda+GPU Driver+CUDA+CUDNN+TensorFlow-gpu+Pytorch

1 Anaconda

1.1 下载 Anaconda

通过国内镜像源下载，请点击清华开源软件镜像站，这里我选择 Ananconda3, python 版本为 3.6，即 Anaconda3-5.0.1-Linux-x86_64.sh，如下图所示。

1.2 安装 Anaconda

先切换到 anaconda 安装包所在的路径下，接着执行以下命令

chiyukunpeng@chiyukunpeng:~$ cd anaconda3
chiyukunpeng@chiyukunpeng:~/anaconda3$ sh Anaconda3-5.0.1-Linux-x86_64.sh

接下来一路按回车以及输入 yes 即可。重启终端，输入以下命令验证安装是否成功

chiyukunpeng@chiyukunpeng:~$ source ~/.bashrc
chiyukunpeng@chiyukunpeng:~$ python  # 出现以下 Anaconda 的 python 环境即可
Python 3.6.3 |Anaconda, Inc.| (default, Oct 13 2017, 12:02:49) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()  # 输入退出此环境

1.3 conda换源

Anaconda 是深度学习环境搭建的必备工具，但服务器在国外，下载速度很慢，换为国内源十分重要。
输入以下命令，打开 condarc 文件

chiyukunpeng@chiyukunpeng:~$ sudo gedit ~/.condarc

然后将以下内容复制粘贴更换掉此文件内所有内容，保存退出即可。

auto_activate_base: false
channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/menpo/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
show_channel_urls: true

2 GPU 驱动

2.1 查看 GPU

在终端输入以下命令，查看 GPU 型号及推荐驱动

chiyukunpeng@chiyukunpeng:~$ ubuntu-drivers devices
== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
modalias : pci:v000010DEd00001C20sv000017AAsd000039F5bc03sc00i00
vendor   : NVIDIA Corporation
model    : GP106M [GeForce GTX 1060 Mobile]  # 我的GPU型号
driver   : nvidia-driver-390 - third-party free
driver   : nvidia-driver-435 - distro non-free
driver   : nvidia-driver-410 - third-party free
driver   : nvidia-driver-415 - third-party free
driver   : nvidia-driver-440 - third-party free recommended  # 推荐的驱动型号
driver   : xserver-xorg-video-nouveau - distro free builtin

2.2 安装驱动

在终端输入以下命令

chiyukunpeng@chiyukunpeng:~$ sudo apt-get install nvidia-driver-440

安装成功后，打开桌面左下角的应用程序抽屉，打开软件和更新->附加驱动，选择刚刚下载的驱动，如下图所示
重启电脑，打开终端，输入以下命令验证是否安装成功

chiyukunpeng@chiyukunpeng:~$ nvidia-smi
Sun Apr 19 21:39:38 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82       Driver Version: 440.82       CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1060    Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   45C    P5    10W /  N/A |    245MiB /  6078MiB |      3%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1569      G   /usr/lib/xorg/Xorg                           109MiB |
|    0      1743      G   /usr/bin/gnome-shell                         118MiB |
|    0     11018      G   /usr/lib/firefox/firefox                       1MiB |
|    0     11711      G   ...uest-channel-token=10670826761480323022    12MiB |
+-----------------------------------------------------------------------------+

3 CUDA

3.1 gcc 降级

因为本文将要安装 cuda9.0，所以要把 gcc 降到 6.0 以下，在终端输入以下命令

chiyukunpeng@chiyukunpeng:~$ sudo apt-get install gcc-4.8 # 安装
chiyukunpeng@chiyukunpeng:~$ sudo apt-get install g++-4.8

进入 /usr/bin 路径，输入以下命令修改 gcc 默认链接

chiyukunpeng@chiyukunpeng:~$ cd /usr/bin
chiyukunpeng@chiyukunpeng:/usr/bin$ sudo mv gcc gcc.bak #备份
chiyukunpeng@chiyukunpeng:/usr/bin$ sudo ln -s gcc-4.8 gcc #重新链接
chiyukunpeng@chiyukunpeng:/usr/bin$ sudo mv g++ g++.bak
chiyukunpeng@chiyukunpeng:/usr/bin$ sudo ln -s g++-4.8 g++

最后验证是否降级成功，输入以下命令

chiyukunpeng@chiyukunpeng:/usr/bin$ gcc --version
gcc (Ubuntu 4.8.5-4ubuntu8) 4.8.5
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

chiyukunpeng@chiyukunpeng:/usr/bin$ g++ --version
g++ (Ubuntu 4.8.5-4ubuntu8) 4.8.5
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

3.2 下载 CUDA

本文选择 CUDA 9.0，高版本 TensorFlow-gpu 和 Pytorch 可能还没有支持
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3.3 安装 CUDA

打开终端，进入 CUDA 安装包路径，执行以下命令

chiyukunpeng@chiyukunpeng:~$ cd software 
chiyukunpeng@chiyukunpeng:~/software$ ls
baidunetdisk  cuda_9.0.176_384.81_linux.run  FoxitReader              pycharm  WangYiCloudMusic
Chrome        cudnn-9.0-linux-x64-v7         NVIDIA_CUDA-9.0_Samples  snap     WPS
chiyukunpeng@chiyukunpeng:~/software$ sudo sh cuda_9.0.176_384.81_linux.run

然后会弹出一系列问题，只有下面这个问题选择 no (因为此前GPU驱动已经安装了），其余都选择 yes, 如下

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 352.39? ((y)es/(n)o/(q)uit): n

3.4 配置 CUDA 环境变量

回到主目录路径，打开 .bashrc 文件，命令如下

chiyukunpeng@chiyukunpeng:~/software$ cd
chiyukunpeng@chiyukunpeng:~$ sudo gedit ~/.bashrc

在文件末尾添加以下内容，保存退出即可

export PATH="/usr/local/cuda-9.0/bin:$PATH" 
export LD_LIBRARY_PATH="/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH"

在终端输入以下命令

chiyukunpeng@chiyukunpeng:~$ source ~/.bashrc

验证CUDA是否安装成功

chiyukunpeng@chiyukunpeng:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

4 CUDNN

4.1 下载 CUDNN

CUDNN 版本必须与 CUDA 版本匹配，本文选择 CUDNN7.05，官网下载要先注册的哦
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4.2 安装 CUDNN

先解压压缩包，然后在终端进入解压文件夹路径，命令如下

chiyukunpeng@chiyukunpeng:~$ cd software
chiyukunpeng@chiyukunpeng:~/software$ ls
baidunetdisk                   cudnn-9.0-linux-x64-v7   pycharm           WPS
Chrome                         FoxitReader              snap
cuda_9.0.176_384.81_linux.run  NVIDIA_CUDA-9.0_Samples  WangYiCloudMusic
chiyukunpeng@chiyukunpeng:~/software$ cd cudnn-9.0-linux-x64-v7
chiyukunpeng@chiyukunpeng:~/software/cudnn-9.0-linux-x64-v7$ cd cuda
chiyukunpeng@chiyukunpeng:~/software/cudnn-9.0-linux-x64-v7/cuda$ sudo cp lib64/* /usr/local/cuda/lib64/ # 复制相应文件
chiyukunpeng@chiyukunpeng:~/software/cudnn-9.0-linux-x64-v7/cuda$ sudo cp include/* /usr/local/cuda/include/
chiyukunpeng@chiyukunpeng:~/software/cudnn-9.0-linux-x64-v7/cuda$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* # 所有用户可读

CUDNN 安装成功。

5 TensorFlow-gpu

5.1 安装 tensorflow-gpu

安装前，请点击TensorFlow官网查看版本匹配信息，
本文第三大福利来啦，版本匹配信息如下图所示。

本文选择下载1.12.0版本，在终端输入如下命令

chiyukunpeng@chiyukunpeng:~$ pip install tensorflow-gpu==1.12.0

5.2 测试

在主目录下，输入如下命令

chiyukunpeng@chiyukunpeng:~$ python
Python 3.6.3 |Anaconda, Inc.| (default, Oct 13 2017, 12:02:49) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
1.12.0
>>> exit()

恭喜你，tensorflow-gpu 安装成功！！！

6 PyTorch

6.1 前言

前五节，我们完成了深度学习框架 TensorFlow-gpu 的安装，为何还要再安装另一个深度学习框架 Pyorch 呢？
因为目前越来越多的学术党开源的项目用的都是这个，不会不行阿，所以必须重新创建一个新环境，安装一下这个框架。

6.2 安装 PyTorch

打开终端，创建一个新的 anaconda 虚拟环境，本文命名为 pytorch，命令如下

chiyukunpeng@chiyukunpeng:~$ conda create -n pytorch python=3.6
Fetching package metadata .................
Solving package specifications: .

Package plan for installation in environment /home/chiyukunpeng/anaconda3/envs/pytorch:

The following NEW packages will be INSTALLED:  # 这里体会到 conda 换源的好处了吧

    _libgcc_mutex:    0.1-conda_forge              https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    _openmp_mutex:    4.5-0_gnu                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    bzip2:            1.0.8-h516909a_2             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    ca-certificates:  2020.4.5.1-hecc5488_0        https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    certifi:          2020.4.5.1-py36hc560c46_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    expat:            2.2.9-he1b5a44_2             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    gdbm:             1.18-h0a1914f_1              https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    ld_impl_linux-64: 2.34-h53a641e_0              https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libffi:           3.2.1-he1b5a44_1007          https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libgcc-ng:        9.2.0-h24d8f2e_2             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libgomp:          9.2.0-h24d8f2e_2             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libstdcxx-ng:     9.2.0-hdf63c60_2             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    ncurses:          6.1-hf484d3e_1002            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    openssl:          1.1.1f-h516909a_0            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    pip:              20.0.2-py_2                  https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    pypy3.6:          7.3.1-h3e02ecb_1             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    python:           3.6.10-h8356626_1010_cpython https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    python_abi:       3.6-1_pypy36_pp73            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    readline:         8.0-hf8c457e_0               https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    setuptools:       46.1.3-py36hc560c46_0        https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    sqlite:           3.30.1-hcee41ef_0            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    tk:               8.6.10-hed695b0_0            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    wheel:            0.34.2-py_1                  https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    xz:               5.2.5-h516909a_0             https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    zlib:             1.2.11-h516909a_1006         https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge

Proceed ([y]/n)? y

_libgcc_mutex- 100% |######################################################| Time: 0:00:00   1.84 MB/s
ca-certificate 100% |######################################################| Time: 0:00:00 643.45 kB/s
ld_impl_linux- 100% |######################################################| Time: 0:00:00   1.91 MB/s
libstdcxx-ng-9 100% |######################################################| Time: 0:01:05  71.46 kB/s
libgomp-9.2.0- 100% |######################################################| Time: 0:00:03 262.33 kB/s
_openmp_mutex- 100% |######################################################| Time: 0:00:00   2.80 MB/s
libgcc-ng-9.2. 100% |######################################################| Time: 0:00:09 906.65 kB/s
bzip2-1.0.8-h5 100% |######################################################| Time: 0:00:00   1.99 MB/s
expat-2.2.9-he 100% |######################################################| Time: 0:00:00   2.47 MB/s
libffi-3.2.1-h 100% |######################################################| Time: 0:00:00   3.32 MB/s
ncurses-6.1-hf 100% |######################################################| Time: 0:00:01   1.29 MB/s
openssl-1.1.1f 100% |######################################################| Time: 0:00:08 250.19 kB/s
xz-5.2.5-h5169 100% |######################################################| Time: 0:00:00   3.50 MB/s
zlib-1.2.11-h5 100% |######################################################| Time: 0:00:00   3.97 MB/s
readline-8.0-h 100% |######################################################| Time: 0:00:00   2.99 MB/s
tk-8.6.10-hed6 100% |######################################################| Time: 0:00:02   1.28 MB/s
gdbm-1.18-h0a1 100% |######################################################| Time: 0:00:00   1.66 MB/s
sqlite-3.30.1- 100% |######################################################| Time: 0:00:01   1.59 MB/s
pypy3.6-7.3.1- 100% |######################################################| Time: 0:00:26   1.23 MB/s
python-3.6.10- 100% |######################################################| Time: 0:00:39 900.41 kB/s
python_abi-3.6 100% |######################################################| Time: 0:00:00   8.60 MB/s
certifi-2020.4 100% |######################################################| Time: 0:00:00   1.36 MB/s
setuptools-46. 100% |######################################################| Time: 0:00:01 558.58 kB/s
wheel-0.34.2-p 100% |######################################################| Time: 0:00:00   5.53 MB/s
pip-20.0.2-py_ 100% |######################################################| Time: 0:00:00   1.64 MB/s
#
# To activate this environment, use:
# > source activate pytorch
#
# To deactivate an active environment, use:
# > source deactivate
#

虚拟环境创建成功，激活此环境，命令如下

chiyukunpeng@chiyukunpeng:~$ source activate pytorch
(pytorch) chiyukunpeng@chiyukunpeng:~$

安装 pytorch 之前，请点击PyTorch官网查看匹配版本及下载命令，本文选择1.0.0
本文第四大福利来啦，版本匹配信息如下图
这里请注意，每个命令后的 -c pytorch 是从官网下载，大家不要加上这个，安装命令如下

(pytorch) chiyukunpeng@chiyukunpeng:~$ conda install pytorch==1.0.0 torchvision==0.2.1 cuda90
Fetching package metadata .................
Solving package specifications: .

Package plan for installation in environment /home/chiyukunpeng/anaconda3/envs/pytorch:

The following NEW packages will be INSTALLED:  # conda 换源就是爽

    cffi:           1.14.0-py36hd463f26_0                https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    cuda90:         1.0-h6433d27_0                       https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch    
    freetype:       2.10.1-he06d7ca_0                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    intel-openmp:   2020.0-166                           https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main        
    jpeg:           9c-h14c3975_1001                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libblas:        3.8.0-14_openblas                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libcblas:       3.8.0-14_openblas                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libgfortran-ng: 7.3.0-hdf63c60_5                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    liblapack:      3.8.0-14_openblas                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libopenblas:    0.3.7-h5ec1e0e_6                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libpng:         1.6.37-hed695b0_1                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libtiff:        4.1.0-hc7e4089_6                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    libwebp-base:   1.1.0-h516909a_3                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    lz4-c:          1.9.2-he1b5a44_0                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    mkl:            2020.0-166                           https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main        
    ninja:          1.10.0-hc9558a2_0                    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    numpy:          1.18.1-py36he0f5f23_1                https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    olefile:        0.46-py_0                            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    pillow:         7.1.1-py36hfc7c323_0                 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    pycparser:      2.20-py_0                            https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    pytorch:        1.0.0-py3.6_cuda9.0.176_cudnn7.4.1_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch    
    six:            1.14.0-py_1                          https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    torchvision:    0.2.1-py_2                           https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch    
    zstd:           1.4.4-h6597ccf_3                     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge

Proceed ([y]/n)? y

cuda90-1.0-h64 100% |######################################################| Time: 0:00:00   2.21 MB/s
intel-openmp-2 100% |######################################################| Time: 0:00:00   1.98 MB/s
libgfortran-ng 100% |######################################################| Time: 0:00:00  13.21 MB/s
mkl-2020.0-166 100% |######################################################| Time: 0:05:42 618.83 kB/s
jpeg-9c-h14c39 100% |######################################################| Time: 0:00:00   2.53 MB/s
libopenblas-0. 100% |######################################################| Time: 0:00:06   1.33 MB/s
libwebp-base-1 100% |######################################################| Time: 0:00:00 959.85 kB/s
lz4-c-1.9.2-he 100% |######################################################| Time: 0:00:00 623.38 kB/s
ninja-1.10.0-h 100% |######################################################| Time: 0:00:01   1.86 MB/s
libblas-3.8.0- 100% |######################################################| Time: 0:00:00   8.32 MB/s
libpng-1.6.37- 100% |######################################################| Time: 0:00:00   3.14 MB/s
zstd-1.4.4-h65 100% |######################################################| Time: 0:00:00   1.48 MB/s
freetype-2.10. 100% |######################################################| Time: 0:00:00   2.17 MB/s
libcblas-3.8.0 100% |######################################################| Time: 0:00:00   8.41 MB/s
liblapack-3.8. 100% |######################################################| Time: 0:00:00   7.49 MB/s
libtiff-4.1.0- 100% |######################################################| Time: 0:00:00   2.28 MB/s
olefile-0.46-p 100% |######################################################| Time: 0:00:00  15.45 MB/s
pycparser-2.20 100% |######################################################| Time: 0:00:00   1.38 MB/s
six-1.14.0-py_ 100% |######################################################| Time: 0:00:00  11.34 MB/s
cffi-1.14.0-py 100% |######################################################| Time: 0:00:00   4.79 MB/s
numpy-1.18.1-p 100% |######################################################| Time: 0:00:03   1.73 MB/s
pillow-7.1.1-p 100% |######################################################| Time: 0:00:00   1.02 MB/s
pytorch-1.0.0- 100% |######################################################| Time: 0:09:25 925.37 kB/s
torchvision-0. 100% |######################################################| Time: 0:00:00  49.84 MB/s

6.3 测试

测试命令如下

(pytorch) chiyukunpeng@chiyukunpeng:~$ python
Python 3.6.10 | packaged by conda-forge | (default, Apr  6 2020, 14:52:36) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>> exit()

恭喜你，第二个深度学习框架安装成功啦！！！
最后，别忘了关闭这个虚拟环境哦

(pytorch) chiyukunpeng@chiyukunpeng:~$ source deactivate pytorch

陌生人，点个赞再走呗！