ubuntu16.04 tensorflow-gpu版本安装好后，简单的检测代码

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是念 2022-08-11 17:39:27 博主文章分类：tensorflow ©著作权

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安装好cuda和 cudnn以后，我们用pip命令安装tensorflow-gpu版本(ubuntu 16.04)：

sudo pip install tensorflow-gpu

如果是安装的tensorflow 1.3版本，那就要注意了，需要安装cuda 8.0和cudnn 6.0（重要），否则会报错：

xyxt@xyxt-System-Product-Name:~/Downloads/shangyixing$ python
Python 2.7.12 (default, Nov 19 2016, 06:48:10) 
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/__init__.py", line 24, in <module>
    from tensorflow.python import *
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/__init__.py", line 49, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 52, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
ImportError: libcudnn.so.6: cannot open shared object file: No such file or directory


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

我们在检测tensorflow-gpu版本是否安装好，需要运行一个python代码进行测试，在python终端输入：

import tensorflow   
>>> import tensorflow as tf
>>> matrix1 = tf.constant([[3., 3.]])     
>>> matrix2 = tf.constant([[2.],[2.]])    
>>> product = tf.matmul(matrix1, matrix2) 
>>> sess = tf.Session()

输出的信息，如果有你的显卡信息，则说明你的tensorflow GPU 版本安装成功了

2017-10-10 17:44:46.428528: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-10 17:44:46.428544: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-10 17:44:46.428549: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-10 17:44:46.428553: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-10 17:44:46.428557: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-10-10 17:44:46.547766: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-10-10 17:44:46.548007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: 
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.683
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.43GiB
2017-10-10 17:44:46.548020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 
2017-10-10 17:44:46.548024: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y 
2017-10-10 17:44:46.548029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)