在CentOS 7上安装Tensorflow

简介

Tensorflow 是Google基于DistBelief进行研发的第二代人工智能学习系统,是采用数据流图（data flow graphs)，用于数值计算的开源软件库。架构灵活，能够部署于一块或多块CPU、GPU卡的各类平台。最初用由Google大脑小组开发出来，用于机器学习和深度神经网络方面的研究，系统的通用性使其能够普遍用于其余计算领域，是目前使用最广的深度学习框架之一。

本教程主要介绍TensorFlow 在Centos 7上的安装和使用，包括安装过程，基本使用和简单示例运行。

GPU驱动安装

根据GPU型号从相应网站下载驱动，例如使用NVIDIA Tesla M60，从NVIDIA网站选择对应的型号和操做系统，CUDA Toolkit版本，下载驱动文件，如NVIDIA-Linux-x86_64-375.66.run，运行驱动文件，根据提示安装：

sh  NVIDIA-Linux-x86_64-375.66.run
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安装完成后能够经过NVIDIA命令工具nvidia-smi查看GPU状况:

nvidia-smi
Wed Jun 28 11:39:28 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.39                 Driver Version: 375.39                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla M60           Off  | 0000:00:02.0     Off |                  Off |
| N/A   36C    P0    38W / 150W |      0MiB /  8123MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+：
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TensorFlow 安装过程

1.安装CUDA

从NVIDIA网站选择最新的驱动版本,选择Linux，x86_64,CentOS 7,下载rpm(local)驱动文件，并安装

sudo rpm -i cuda-repo-rhel7-8-0-local-ga2-8.0.61-1.x86_64.rpm
sudo yum clean all
sudo yum install cuda
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设置环境变量

export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda-8.0/
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2.安装cuDNN

从NVIDIA网站下载cuDNN 安装包，根据GPU及CUDA版本选择对应cuDNN版本，下载cuDNN v5.1 for CUDA8.0,解压拷贝到CUDA安装目录

cp include/* /usr/local/cuda/include
cp lib64/* /usr/local/cuda/lib64
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3.安装Python 环境

安装命令以下：

sudo yum install python-pip python-wheel
sudo pip install --upgrade pip
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4.安装Tensorflow

能够选择不一样的环境安装Tensorflow，如：virtualenv,pip, Docker,Anaconda。如须要使TensorFlow 成为系统多用户可直接使用的服务，推荐经过原生pip命令安装。 安装命令以下：

pip install tensorflow #python2.7 CPU版本
pip install tensorflow-gpu #python2.7 GPU版本
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pip 命令安装失败时，可经过Tensorflow 网站选择下载whl 文件安装，不一样版本whl文件地址。 下载到本地后经过pip 命令安装。

pip install tensorflow_gpu-1.1.0-cp27-none-linux_x86_64.whl
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TensorFlow 使用示例

1.TensorFlow模块使用

运行Python SHELL，import TensorFlow模块，进行简单的功能验证：

python
Python 2.7.5 (default, Jun 17 2014, 18:11:42)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-16)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2017-06-28 16:42:53.518877: 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. 2017-06-28 16:42:53.518906: 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.
2017-06-28 16:42:53.518914: 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. 2017-06-28 16:42:53.518921: 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.
2017-06-28 16:42:53.518929: 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. 2017-06-28 16:42:54.099744: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2017-06-28 16:42:54.100218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: name: Tesla M60 major: 5 minor: 2 memoryClockRate (GHz) 1.1775 pciBusID 0000:00:02.0 Total memory: 7.93GiB Free memory: 7.86GiB 2017-06-28 16:42:54.100243: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 2017-06-28 16:42:54.100251: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 2017-06-28 16:42:54.100266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla M60, pci bus id: 0000:00:02.0) >>> print(sess.run(hello)) Hello, TensorFlow! 复制代码

2.MNIST例子

运行Tensorflow tutorial 中MNIST例子，下载代码到本地并执行。
训练输出示例以下：

python mnist_softmax.py
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz
2017-06-28 16:13:17.165681: 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. 2017-06-28 16:13:17.165727: 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.
2017-06-28 16:13:17.165736: 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. 2017-06-28 16:13:17.165744: 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.
2017-06-28 16:13:17.165751: 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. 2017-06-28 16:13:17.742066: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2017-06-28 16:13:17.742503: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: name: Tesla M60 major: 5 minor: 2 memoryClockRate (GHz) 1.1775 pciBusID 0000:00:02.0 Total memory: 7.93GiB Free memory: 7.86GiB 2017-06-28 16:13:17.742529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 2017-06-28 16:13:17.742538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 2017-06-28 16:13:17.742553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla M60, pci bus id: 0000:00:02.0) 0.9176 复制代码

参考资料

• Tensorflow 官方网站