linux下安装TensorFlow(centos)

一、python安装

　　centos自带python2.7.5，这一步可以省略掉。

二、python-pip

　　pip--python index package,累世linux的yum，安装管理python软件包用的。

yum install python-pip python-devel

 

三、安装tensorflow

　　安装基于linux和python2.7的tensorflow 0.9

pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl

　　其他操作系统版本可以参照下表：

　　

# Ubuntu/Linux 64-bit, CPU only, Python 2.7 export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl # Ubuntu/Linux 64-bit, GPU enabled, Python 2.7 # Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below. export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl # Mac OS X, CPU only, Python 2.7: export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/tensorflow-0.9.0-py2-none-any.whl # Ubuntu/Linux 64-bit, CPU only, Python 3.4 export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl # Ubuntu/Linux 64-bit, GPU enabled, Python 3.4 # Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below. export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl # Ubuntu/Linux 64-bit, CPU only, Python 3.5 export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl # Ubuntu/Linux 64-bit, GPU enabled, Python 3.5 # Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below. export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl # Mac OS X, CPU only, Python 3.4 or 3.5: export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/tensorflow-0.9.0-py3-none-any.whl

四、测试安装是否成功

　　编写python脚本 test.py

import tensorflow as tf hello = tf.constant('Hello,TensorFlow!') sess = tf.Session() print(sess.run(hello)) a = tf.constant(10) b = tf.constant(32) print(sess.run(a + b))

运行 python test.py结果如下：

大功告成。