linux 查看驱动 cuda cudnn python tensorflow版本

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linux安装tensorflow-gpu，思路就是从底层到上层一层层安装

driver 驱动版本 cuda cudnn版本和变量 anaconda python tf环境

已安装本版信息：

NVIDIA-SMI 390.132 Driver Version: 390.132
cuda V9.0.176

Anaconda3-5.2.0-Linux-x86_64
pycharm-professional-2018.3.
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
tensorflow-gpu 1.9.0

1 查看Linux内核版本命令（两种方法）：

cat /proc/version

[root@S-CentOS home]# cat /proc/version
Linux version 2.6.32-431.el6.x86_64 (mockbuild@c6b8.s) (gcc version 4.4.7 20120313 (Red Hat 4.4.7-4) (GCC) ) #1 SMP Fri Nov 22 03:15:09 UTC 2013

uname -a

[root@S-CentOS home]# uname -a

Linux S-CentOS 2.6.32-431.el6.x86_64 #1 SMP Fri Nov 22 03:15:09 UTC 2013 x86_64 x86_64 x86_64 GNU/Linux

2 查看Linux系统版本的命令（3种方法）：

lsb_release -a，即可列出所有版本信息：

[root@S-CentOS ~]# lsb_release -a
LSB Version: :base-4.0-amd64:base-4.0-noarch:core-4.0-amd64:core-4.0-noarch:graphics-4.0-amd64:graphics-4.0-noarch:printing-4.0-amd64:printing-4.0-noarch
Distributor ID: CentOS
Description: CentOS release 6.5 (Final)
Release: 6.5
Codename: Final

这个命令适用于所有的Linux发行版，包括RedHat、SUSE、Debian…等发行版。

cat /etc/redhat-release，这种方法只适合Redhat系的Linux：

[root@S-CentOS home]# cat /etc/redhat-release
CentOS release 6.5 (Final)

cat /etc/issue，此命令也适用于所有的Linux发行版。

[root@S-CentOS home]# cat /etc/issue
CentOS release 6.5 (Final)
Kernel r on an m

3 win查看tensorflow版本

python

import tensorflow as tf

tf.__version__

查询tensorflow安装路径为:

tf.__path__

4 测试gpu运行

import tensorflow as tf
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:a = tf.constant(1)b = tf.constant(3)c = a + bprint('结果是：%dn 值为：%d' % (sess.run(c), sess.run(c)))

5 查看 CUDA 版本：

cat /usr/local/

查看cuda信息

nvcc -V,

6 查看 CUDNN 版本：

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

7查看GPU型号

lspci | grep -i nvidia

8 查看NVIDIA驱动版本

sudo dpkg --list | grep nvidia-*

7 查看驱动 cuda cudnn

nvida-smi

nvcc -V

8 查看所使用的tensorflow是GPU还是CPU版本

法1

import os
from tensorflow.python.client import device_lib
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "99"if __name__ == "__main__":print(device_lib.list_local_devices())

法2

# Creates a graph. 
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') 
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') 
c = tf.matmul(a, b) # Creates a session with log_device_placement set to True. 
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # Runs the op. print(sess.run(c))

显示如下：

Device mapping:

/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K40c, pci bus

id: 0000:05:00.0

b: /job:localhost/replica:0/task:0/device:GPU:0

a: /job:localhost/replica:0/task:0/device:GPU:0

MatMul: /job:localhost/replica:0/task:0/device:GPU:0 [[ 22. 28.] [ 49. 64.]]