在CentOS 7上安装Tensorflow
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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,可以部署于一块或多块CPU、GPU卡的各种平台。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,系统的通用性使其可以广泛用于其他计算领域,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作系统,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
安装完成后可以通过NVIDIA命令工具nvidia-smi查看GPU情况:
nvidia-smiWed 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 |+-----------------------------------------------------------------------------+:
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.rpmsudo yum clean allsudo yum install cuda
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_PATHexport CUDA_HOME=/usr/local/cuda-8.0/
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/includecp lib64/* /usr/local/cuda/lib64
3.安装Python 环境
安装命令如下:
sudo yum install python-pip python-wheelsudo pip install --upgrade pip
4.安装Tensorflow
可以选择不同的环境安装Tensorflow,如:virtualenv,pip, Docker,Anaconda。如需要使TensorFlow 成为系统多用户可直接使用的服务,推荐通过原生pip命令安装。 安装命令如下:
pip install tensorflow #python2.7 CPU版本pip install tensorflow-gpu #python2.7 GPU版本
pip 命令安装失败时,可通过Tensorflow 网站选择下载whl 文件安装,不同版本whl文件地址。 下载到本地后通过pip 命令安装。
pip install tensorflow_gpu-1.1.0-cp27-none-linux_x86_64.whl
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:
pythonPython 2.7.5 (default, Jun 17 2014, 18:11:42)[GCC 4.8.2 20140120 (Red Hat 4.8.2-16)] on linux2Type "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 zero2017-06-28 16:42:54.100218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:name: Tesla M60major: 5 minor: 2 memoryClockRate (GHz) 1.1775pciBusID 0000:00:02.0Total memory: 7.93GiBFree memory: 7.86GiB2017-06-28 16:42:54.100243: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 02017-06-28 16:42:54.100251: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y2017-06-28 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!
2.MNIST例子
运行Tensorflow tutorial 中MNIST例子,下载代码到本地并执行。
训练输出示例如下:
python mnist_softmax.pySuccessfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gzSuccessfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gzSuccessfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gzSuccessfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz2017-06-28 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 zero2017-06-28 16:13:17.742503: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:name: Tesla M60major: 5 minor: 2 memoryClockRate (GHz) 1.1775pciBusID 0000:00:02.0Total memory: 7.93GiBFree memory: 7.86GiB2017-06-28 16:13:17.742529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 02017-06-28 16:13:17.742538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y2017-06-28 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
参考资料
- Tensorflow 官方网站
- 在CentOS 7上安装Tensorflow
- Tensorflow深度学习框架在CentOS 7上的安装
- Tensorflow在centos上安装记录
- tensorflow在centos安装
- 在linux(centos)上从源码安装tensorflow
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