多版本cuda环境管理
1、anaconda安装
1.1 下载 Anaconda 脚本
#wget下载 Anaconda 安装脚本 root@bc23574385ad:~# wget -P ./ https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2020.02-Linux-x86_64.sh #检查包的完整性 root@bc23574385ad:~# sha256sum Anaconda3-2020.02-Linux-x86_64.sh 2b9f088b2022edb474915d9f69a803d6449d5fdb4c303041f60ac4aefcc208bb Anaconda3-2020.02-Linux-x86_64.sh
1.2 安装Anaconda
#操作脚本启动安装过程 root@bc23574385ad:~# bash Anaconda3-2020.02-Linux-x86_64.sh #您应能看到以下输出： Welcome to Anaconda3 2020.02 In order to continue the installation process, please review the license agreement. Please, press ENTER to continue >>> #按ENTER继续。向下滑动阅读协议，使用ENTER按键。向下滑动阅读协议，使用ENTER按钮。一旦您阅读了协议，您将被问及是否接受协议条款： Do you accept the license terms? [yes|no] [no] >>> yes 输入yes接受协议，并提示您选择安装路径： Anaconda3 will now be installed into this location: /root/anaconda3 #这里是默认路径 - Press ENTER to confirm the location - Press CTRL-C to abort the installation - Or specify a different location below [/root/anaconda3] >>> /usr/local/anaconda3 ##新的安装路径可以在这里定制 #安装过程会花一些时间，一旦完成，脚本会问你是否想运行conda init。输入yes。输入yes。 installation finished. Do you wish the installer to initialize Anaconda3 by running conda init? [yes|no] [no] >>> yes #这将命令行工具conda添加到系统中PATH在环境变量中。
#想要激活 Anaconda，您可以关闭并重新打开您 shell 或者在当前 shell 在会话中输入以下命令，来重新加载PATH环境变量： root@bc23574385ad:~# source ~/.bashrc #查看安装版本 (base) root@bc23574385ad:~# conda -V conda 4.8.2
1.3 添加清华源
#添加清华源 (base) root@bc23574385ad:~# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ (base) root@bc23574385ad:~# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ (base) root@bc23574385ad:~# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ (base) root@bc23574385ad:~# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ (base) root@bc23574385ad:~# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r/ (base) root@bc23574385ad:~# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro/
2、完善python-3.7环境
| 名称 |
版本 |
| cuda |
10.2 |
| cudnn |
7.6 |
| pytorch |
1.6 |
| tensorflow |
2.3.0 |
2.1 创建python-3.7环境
#创建python-3.7环境 (base) root@bc23574385ad:~# conda create --name python-3.7 python=3.7 (base) root@bc23574385ad:~# conda activate python-3.7
#安装常用命令 (python-3.7) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple ipython pandas pillow matplotlib setproctitle networkx scikit-learn scipy tqdm GPUtil jupyterlab notebook h5py statsmodels
2.2 安装CUDA
#下载cuda包 (python-3.7) root@bc23574385ad:~# wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run #安装cuda (python-3.7) root@bc23574385ad:~# sh cuda_10.2.89_440.33.01_linux.run --silent --toolkit --samples --librarypath=/usr/local/cuda-10.2
#设置软链接 (python-3.7) root@bc23574385ad:~# ln -s /usr/local/cuda-10.2/bin/nvcc /usr/bin/nvcc-python-3.7 (python-3.7) root@bc23574385ad:~# which nvcc-python-3.7 /usr/bin/nvcc-python-3.7 #检查安装版本 (python-3.7) root@bc23574385ad:~# nvcc-python-3.7 -V nvcc-python-3: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Wed_Oct_23_19:24:38_PDT_2019 Cuda compilation tools, release 10.2, V10.2.89
#测试 CUDA Toolkit 验证安装是否成功 #Result = PASS则安装成功 (python-3.7) root@bc23574385ad:~# cd /usr/local/cuda-10.2/extras/demo_suite/ (python-3.7) root@bc23574385ad:/usr/local/cuda-10.2/extras/demo_suite# ./deviceQuery ... deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.6, CUDA Runtime Version = 10.2, NumDevs = 4, Device0 = NVIDIA GeForce RTX 2080 Ti, Device1 = NVIDIA GeForce RTX 2080 Ti, Device2 = NVIDIA GeForce RTX 2080 Ti, Device3 = NVIDIA GeForce RTX 2080 Ti Result = PASS
2.3 安装CUDNN
#下载cudnn包 (python-3.7) root@bc23574385ad:~# ll -d cudnn-10.2-linux-x64-v7.6.5.32.tgz -rw-r--r-- 1 root root 548210361 Mar 31 13:53 cudnn-10.2-linux-x64-v7.6.5.32.tgz #解压缩 (python-3.7) root@bc23574385ad:~# tar xf cudnn-10.2-linux-x64-v7.6.5.32.tgz (python-3.7) root@bc23574385ad:~# ll -d cuda drwxr-xr-x 4 root root 4096 Mar 31 14:03 cuda/
#把相应的文件，复制到指定目录即可
(python-3.7) root@bc23574385ad:~# cp cuda/include/cudnn* /usr/local/cuda-10.2/include/
(python-3.7) root@bc23574385ad:~# cp cuda/lib64/libcudnn* /usr/local/cuda-10.2/lib64/
#添加权限
(python-3.7) root@bc23574385ad:~# chmod a+r /usr/local/cuda-10.2/include/cudnn* /usr/local/cuda-10.2/lib64/libcudnn*
2.4 安装tensorflow
#安装依赖包
(python-3.7) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --user pytest-cov==2.0 pytest-filter-subpackage==0.1
#安装指定版本的tensorflow
(python-3.7) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow==2.3.0
#验证tensorflow是否安装成功
(python-3.7) root@bc23574385ad:~# pip show tensorflow
Name: tensorflow
Version: 2.3.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: packages@tensorflow.org
License: Apache 2.0
Location: /usr/local/anaconda3/envs/python-3.7/lib/python3.7/site-packages
Requires: absl-py, astunparse, gast, google-pasta, grpcio, h5py, keras-preprocessing, numpy, opt-einsum, protobuf, scipy, six, tensorboard, tensorflow-estimator, termcolor, wheel, wrapt
Required-by:
2.5 安装pytorch
#下载对应cuda版本编译的安装包
(python-3.7) root@bc23574385ad:~# wget -P ./ https://download.pytorch.org/whl/cu102/torch-1.6.0-cp37-cp37m-linux_x86_64.whl
#安装下载好的安装包
(python-3.7) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torch-1.6.0-cp37-cp37m-linux_x86_64.whl
#验证pytorch是否安装成功，这里可以写一个小脚本验证下，如没有报错就是安装成功了
(python-3.7) root@bc23574385ad:~# vim pytorch.py
(python-3.7) root@bc23574385ad:~# cat pytorch.py
from __future__ import print_function
import torch
x = torch.rand(5, 3)
print(x)
(python-3.7) root@bc23574385ad:~# python3.7 pytorch.py
tensor([[0.6295, 0.4860, 0.4348],
[0.2331, 0.1373, 0.6409],
[0.8252, 0.2289, 0.3068],
[0.2569, 0.0396, 0.2084],
[0.3917, 0.4409, 0.2219]])
2.6 多版本cuda环境管理
#进入环境生效脚本
(python-3.7) root@bc23574385ad:~# mkdir -p ~/anaconda3/envs/python-3.7/etc/conda/activate.d
(python-3.7) root@bc23574385ad:~# vim ~/anaconda3/envs/python-3.7/etc/conda/activate.d/activate.sh
(python-3.7) root@bc23574385ad:~# cat ~/anaconda3/envs/python-3.7/etc/conda/activate.d/activate.sh
#!/bin/sh
ORIGINAL_LD_LIBRARY_PATH=$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/extras/CUPTI/lib64:/lib/nccl/cuda-10.2:$LD_LIBRARY_PATH
(python-3.7) root@bc23574385ad:~# chmod +x ~/anaconda3/envs/python-3.7/etc/conda/activate.d/activate.sh
#退出环境生效脚本
(python-3.7) root@bc23574385ad:~# mkdir -p ~/anaconda3/envs/python-3.7/etc/conda/deactivate.d
(python-3.7) root@bc23574385ad:~# vim ~/anaconda3/envs/python-3.7/etc/conda/deactivate.d/deactivate.sh
(python-3.7) root@bc23574385ad:~# cat ~/anaconda3/envs/python-3.7/etc/conda/deactivate.d/deactivate.sh
#!/bin/sh
export LD_LIBRARY_PATH=$ORIGINAL_LD_LIBRARY_PATH
unset ORIGINAL_LD_LIBRARY_PATH
(python-3.7) root@bc23574385ad:~# chmod +x ~/anaconda3/envs/python-3.7/etc/conda/deactivate.d/deactivate.sh
3、完善python-3.8环境
| 名称 |
版本 |
| cuda |
11.0 |
| cudnn |
8.0 |
| pytorch |
1.7 |
| tensorflow |
2.4.0 |
3.1 创建python-3.8环境
#创建python-3.8环境
(python-3.7) root@bc23574385ad:~# conda create --name python-3.8 python=3.8
(python-3.7) root@bc23574385ad:~# conda activate python-3.8
#安装常用命令
(python-3.8) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple ipython pandas pillow matplotlib setproctitle networkx scikit-learn scipy tqdm GPUtil jupyterlab notebook h5py statsmodels
3.2 安装CUDA
#下载cuda包
(python-3.8) root@bc23574385ad:~# wget http://developer.download.nvidia.com/compute/cuda/11.0.2/local_installers/cuda_11.0.2_450.51.05_linux.run
#安装cuda
(python-3.8) root@bc23574385ad:~# sh cuda_11.0.2_450.51.05_linux.run --silent --toolkit --samples --librarypath=/usr/local/cuda-11.0
#设置软链接
(python-3.8) root@bc23574385ad:~# ln -s /usr/local/cuda-11.0/bin/nvcc /usr/bin/nvcc-python-3.8
(python-3.8) root@bc23574385ad:~# which nvcc-python-3.8
/usr/bin/nvcc-python-3.8
#检查安装的版本
(python-3.8) root@bc23574385ad:~# nvcc-python-3.8 -V
nvcc-python-3: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:38_PDT_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.TC445_37.28540450_0#
测试 CUDA Toolkit 以验证是否安装成功
#Result = PASS则安装成功
(python-3.8) root@bc23574385ad:~# cd /usr/local/cuda-11.0/extras/demo_suite/
(python-3.8) root@bc23574385ad:/usr/local/cuda-11.0/extras/demo_suite# ./deviceQuery
......
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.6, CUDA Runtime Version = 11.0, NumDevs = 4, Device0 = NVIDIA GeForce RTX 2080 Ti, Device1 = NVIDIA GeForce RTX 2080 Ti, Device2 = NVIDIA GeForce RTX 2080 Ti, Device3 = NVIDIA GeForce RTX 2080 Ti
Result = PASS
3.3 安装CUDNN
#下载cudnn包
(python-3.8) root@bc23574385ad:~# ll -d cudnn-11.0-linux-x64-v8.0.1.13.tgz
-rw-r--r-- 1 root root 1142456047 Mar 31 14:30 cudnn-11.0-linux-x64-v8.0.1.13.tgz
#解压缩
(python-3.8) root@bc23574385ad:~# tar xf cudnn-11.0-linux-x64-v8.0.1.13.tgz
(python-3.8) root@bc23574385ad:~# ll -d cuda
drwxr-xr-x 4 root root 4096 Mar 31 14:38 cuda/
#把相应的文件，复制到指定目录即可
(python-3.8) root@bc23574385ad:~# cp cuda/include/cudnn* /usr/local/cuda-11.0/include/
(python-3.8) root@bc23574385ad:~# cp cuda/lib64/libcudnn* /usr/local/cuda-11.0/lib64/
#添加权限
(python-3.8) root@bc23574385ad:~# chmod a+r /usr/local/cuda-11.0/include/cudnn* /usr/local/cuda-11.0/lib64/libcudnn*
3.4 安装tensorflow
#安装指定版本的tensorflow
(python-3.8) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow==2.4.0
#验证tensorflow是否安装成功
(python-3.8) root@bc23574385ad:~# pip show tensorflow
Name: tensorflow
Version: 2.4.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: packages@tensorflow.org
License: Apache 2.0
Location: /usr/local/anaconda3/envs/python-3.8/lib/python3.8/site-packages
Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras-preprocessing, numpy, opt-einsum, protobuf, six, tensorboard, tensorflow-estimator, termcolor, typing-extensions, wheel, wrapt
Required-by:
3.5 安装pytorch
#下载对应cuda版本编译的安装包
(python-3.8) root@bc23574385ad:~# wget -P ./ https://download.pytorch.org/whl/cu110/torch-1.7.1%2Bcu110-cp38-cp38-linux_x86_64.whl
#安装下载好的安装包
(python-3.8) root@bc23574385ad:~# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torch-1.7.1+cu110-cp38-cp38-linux_x86_64.whl
#验证pytorch是否安装成功，这里可以写一个小脚本验证下，如没有报错就是安装成功了
(python-3.8) root@bc23574385ad:~# vim pytorch.py
(python-3.8) root@bc23574385ad:~# cat pytorch.py
from __future__ import print_function
import torch
x = torch.rand(5, 3)
print(x)
(python-3.8) root@bc23574385ad:~# python3.8 pytorch.py
tensor([[0.4627, 0.5238, 0.0711],
[0.2442, 0.7200, 0.0021],
[0.3826, 0.1364, 0.1059],
[0.2161, 0.9110, 0.2768],
[0.1932, 0.7716, 0.2172]])
3.6 多版本cuda环境管理
#进入环境生效脚本
(python-3.8) root@bc23574385ad:~# mkdir -p ~/anaconda3/envs/python-3.8/etc/conda/activate.d
(python-3.8) root@bc23574385ad:~# vim ~/anaconda3/envs/python-3.8/etc/conda/activate.d/activate.sh
(python-3.8) root@bc23574385ad:~# cat ~/anaconda3/envs/python-3.8/etc/conda/activate.d/activate.sh
#!/bin/sh
ORIGINAL_LD_LIBRARY_PATH=$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0/extras/CUPTI/lib64:/lib/nccl/cuda-11.0:$LD_LIBRARY_PATH
(python-3.8) root@bc23574385ad:~# chmod +x ~/anaconda3/envs/python-3.8/etc/conda/activate.d/activate.sh
#退出环境生效脚本
(python-3.8) root@bc23574385ad:~# mkdir -p ~/anaconda3/envs/python-3.8/etc/conda/deactivate.d
(python-3.8) root@bc23574385ad:~# vim ~/anaconda3/envs/python-3.8/etc/conda/deactivate.d/deactivate.sh
(python-3.8) root@bc23574385ad:~# cat ~/anaconda3/envs/python-3.8/etc/conda/deactivate.d/deactivate.sh
#!/bin/sh
export LD_LIBRARY_PATH=$ORIGINAL_LD_LIBRARY_PATH
unset ORIGINAL_LD_LIBRARY_PATH
(python-3.8) root@bc23574385ad:~# chmod +x ~/anaconda3/envs/python-3.8/etc/conda/deactivate.d/deactivate.sh