centos7 configuration (nvidia+cuda+cudnn+anaconda+tensorflow) gpu development environment

1. Installation preparation

1. Check the nvidia graphics card, mine is a T4 graphics card

lspci | grep -i nvidia

insert image description here
2. View the linux system version

uname -m && cat /etc/redhat-release

insert image description here
3. Installation dependencies

yum install gcc kernel-devel kernel-headers

2. Install the nvidia driver

1. Disable nouveau

lsmod | grep nouveau

If there is output, then nouveau is enabled and needs to be turned off, follow the steps below. Disable method in centos7:

#打开如下文件
sudo vim /usr/lib/modprobe.d/dist-blacklist.conf
#写入以下内容
blacklist nouveau
options nouveau modeset=0
#保存并退出
:wq
#重启
sudo reboot
#最后输入上面的命令验证
lsmod | grep nouveau

No output, nouveau disabled

2. Install the driver
Step 1: Open the NVIDIA driver download link http://www.nvidia.com/Download/Find.aspx
Step 2: Choose the driver that suits you, including product series, operating system, language, etc.
I will install it here is version 11.2
insert image description here

rpm -i nvidia-driver-local-repo-rhel7-460.106.00-1.0-1.x86_64.rpm
yum clean all
yum install cuda-drivers
reboot

3. Check whether the driver is installed successfully

nvidia-smi

3. Install cuda

1. Enter the address in the browser: https://developer.nvidia.com/cuda-toolkit-archive,
insert image description here
click Download Latest CUDA Toolkit, jump to this page, and select according to the system version

wget https://developer.download.nvidia.com/compute/cuda/11.2.0/local_installers/cuda_11.2.0_460.27.04_linux.run
chmod +x cuda_11.2.0_460.27.04_linux.run
./cuda_11.2.0_460.27.04_linux.run

Press Enter to cancel Diver
insert image description here

2. Configure environment variables

vim ~/.bashrc 
写入：
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2/lib64
export PATH=$PATH:/usr/local/cuda-11.2/bin
export CUDA_HOME=$CUDA_HOME:/usr/local/cuda-11.2

source vim ~/.bashrc

3. Verify that cuda takes effect:

nvcc -V

insert image description here

Fourth, install cudnn

1. Download address: https://developer.nvidia.com/rdp/cudnn-download
2. Unzip cudnn

tar -xvf cudnn-linux-x86_64-8.7.0.84_cuda11-archive.tar.xz
cd cudnn-linux-x86_64-8.7.0.84_cuda11-archive
sudo cp include/cudnn*.h /usr/local/cuda-11.2/include/ 
sudo cp lib/libcudnn* /usr/local/cuda-11.2/lib64/ 
sudo chmod a+r /usr/local/cuda-11.2/include/cudnn.h 
sudo chmod a+r /usr/local/cuda-11.2/lib64/libcudnn*

2. Check the installation of cudnn

cat /usr/local/cuda-11.2/include/cudnn_version.h | grep CUDNN_MAJOR -a

insert image description here

Five, install anaconda

1. Download the anaconda installation package at https://repo.anaconda.com/archive/.
Select python3.88 version
insert image description here
2, install anaconda

sh Anaconda3-2021.05-Linux-x86_64.sh

3. Configure environment variables

vim ~/.bashrc 
# 配置anaconda
export PATH=/root/anaconda3/bin:$PATH

source ~/.bashrc

4. Check conda

conda -V

6. Configure pip source

1. Create a .pip folder in the root directory

mkdir ~/.pip

2. Use vim to open the pip.conf configuration file

vim ~/.pip/pip.conf

3. pip source configuration file

[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple

7. Install tensorflow

1. Install tensorflow

pip install tensorflow==2.5.0

2. Enter python to enter the development
Method 1:

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

insert image description here
Method Two:

import tensorflow as tf
a = tf.test.is_built_with_cuda()  # 判断CUDA是否可以用
b = tf.test.is_gpu_available(
    cuda_only=False,
    min_cuda_compute_capability=None
)                                  # 判断GPU是否可以用
print(a)
print(b)

insert image description here