tensorflow with gpu 环境配置
1、准备工作
1.1 确保GPU驱动已经安装
lspci | grep -i nvidia 通过此命令可以查看GPU信息，测试机已经安装GPU驱动
nvidia-smi 可以查看英伟达显卡信息
1.2 确保gcc安装
可以通过gcc -v 查看，如果没有安装需要安装
1.3 确保安装open-ssh
如果没有安装可以通过 yum install openssh-server 安装
1.4 确保安装kernel
sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
2、安装CUDA工具包
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit.
备注：测试机选用的是CUDA 8.0，不要使用9.x 有坑
3、安装GPU加速器cuDNN
安装完CUDA就可以安装 cuDNN .
备注：这个地方要选用与CUDA版本匹配的加速器；测试机选用的是：Download cuDNN v6.0 (April 27, 2017), for CUDA 8.0，安装步骤如下图所示：
4、安装或更新pip（如果有需要的话，如果已经有了，可以选择性跳过）
TensorFlow itself can be installed using the pip package manager. First, make sure that your system has pip installed and updated:
$ sudo apt-get install python-pip python-dev
$ pip install --upgrade pip
5、安装TensorFlow
Run the following command to install the TensorFlow Python package using pip:
$ pip install --upgrade tensorflow-gpu
6、测试安装是否成功
To test the installation, open an interactive Python shell and import the TensorFlow module:
(shan) root@VM-241-228-ubuntu:/data/shan# python
Python 3.5.3 (default, Jun 23 2017, 16:12:41)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> sess = tf.Session()
2017-07-12 19:24:14.030098: 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
2017-07-12 19:24:14.030833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: Tesla M40 24GB
major: 5 minor: 2 memoryClockRate (GHz) 1.112
pciBusID 0000:00:06.0
Total memory: 22.40GiB
Free memory: 22.29GiB
2017-07-12 19:24:14.030855: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0
2017-07-12 19:24:14.030867: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y
2017-07-12 19:24:14.030882: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla M40 24GB, pci bus id: 0000:00:06.0)
>>> hello_world = tf.constant("Hello, TensorFlow!")
>>> print (sess.run(hello_world))
b'Hello, TensorFlow!'
>>> print (sess.run(tf.constant(123)*tf.constant(456)))
56088
>>>
参考链接
