linux 查看驱动 cuda cudnn python tensorflow版本
1 win查看tensorflow版本pythonimport tensorflow as tftf.__version__查询tensorflow安装路径为:tf.__path__2 导入文件路径
0
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
cudnn-9.0-linux-x64-v7.tgz
Anaconda3-5.2.0-Linux-x86_64
pycharm-professional-2018.3.5.tar.gz
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.bsys.dev.centos.org) (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 + b
print('结果是:%d\n 值为:%d' % (sess.run(c), sess.run(c)))
5 查看 CUDA 版本:
cat /usr/local/cuda/version.txt
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.]]
9 查看python版本
- (1)win+R输入cmd,打开命令窗口
- (2)输入指令:python --version
10 查看某第三包信息、版本号
- (1)win+R输入cmd,打开命令窗口
- (2)输入指令:pip show xxx(xxx,需查看的包名)
- 或者pip list
11 linux查看gcc/cmake/当前版本
cmake/gcc/g++ --version 查看
12 查看 linux 版本
cat /etc/redhat-release
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