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ubuntu系统配置深度学习环境

 3 years ago
source link: https://blog.whuzfb.cn/blog/2020/10/30/ubuntu_config_deep_learning/
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0. 查看最新教程

点击此链接跳转到Nvidia Driver 450、CUDA 11.0、cuDNN 8.0.4版本配置教程

1. 深度学习硬件环境参数

服务器的硬件配置如下：

  • CPU型号：Intel Core i7-8700
  • 内存大小：64GB
  • GPU型号：GeForce GTX 1080 Ti

系统和各基础软件的版本都在下表中列出：

  • 操作系统版本：Ubuntu 18.04，64位
  • 英伟达显卡驱动：nvidia-driver-410
  • CUDA版本：10.0.130
  • cuDNN版本：7.6.0.64

2. 深度学习系统环境配置

2.1 安装nvidia驱动

打开终端，依次输入以下命令：

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-driver-410

然后重启计算机即可完成驱动安装，在终端输入nvidia-smi命令得到如下示例输出则表明安装成功

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.104      Driver Version: 410.104      CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| 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 108...  Off  | 00000000:01:00.0  On |                  N/A |
|  0%   29C    P8    18W / 250W |    249MiB / 11175MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1259      G   /usr/lib/xorg/Xorg                            12MiB |
|    0      1376      G   /usr/bin/gnome-shell                          59MiB |
|    0      3384      G   /usr/lib/xorg/Xorg                            71MiB |
|    0      3581      G   /usr/bin/gnome-shell                          92MiB |
+-----------------------------------------------------------------------------+

2.2 安装CUDA

访问CUDA的下载网址，选择适合自己操作系统版本的文件下载，然后打开终端依次输入以下命令：

sudo dpkg -i cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-10-0-local-10.0.130-410.48/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

此时CUDA以及安装完毕，在终端输入cd /usr/local/cuda-10.0/bin && ./nvcc -V命令得到如下示例输出则表明安装成功

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130

但是，为了方便下面深度学习软件的使用，还要把相关路径加入PATH。打开文件~/.profile ，在文档末尾添加以下内容：

# set PATH for cuda 10.0 installation
if [ -d "/usr/local/cuda-10.0/bin/" ]; then
    export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
    export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
fi

重启计算机即可使环境变量生效

2.3 安装cuDNN

打开cuDNN的下载网址，然后点击Download cuDNN v7.6.0 (May 20, 2019), for CUDA 10.0并根据自己的操作系统选择合适的版本。其中，cuDNN Runtime Library和cuDNN Developer Library是必须要下载的，cuDNN Code Samples and User Guide为可选项目。然后依次安装前面下载的3个文件：

sudo dpkg -i libcudnn7_7.6.0.64-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.6.0.64-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-doc_7.6.0.64-1+cuda10.0_amd64.deb

3. python3软件环境配置

创建基于python3的虚拟环境，然后安装深度学习以及本项目需要用到的库：

# 安装python3开发库
sudo apt-get install python3-pip
# 创建名称为myvenv的虚拟环境
python3 -m venv myvenv
# 激活myvenv虚拟环境
source myvenv/bin/activate
# pip安装深度学习相关第三方库
pip install -r numpy==1.16.3 matplotlib==3.1.0 tensorflow-gpu==1.13.1 Keras==2.2.4

晨曦 / 2020-10-30 / 101 views
Published under(CC) BY-NC-SA 4.0.


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