CentOS 7.6安装Tensor flow 1.13 GPU版本（RTX2070）

Tensorflow官网推荐使用的操作系统是Ubuntu 16.04或者18.04，我个人比较喜欢CentOS，所以就选择在CentOS 7上安装。

1、CentOS 7.6的安装（当前最新的64位版本）

      安装的时候注意要安装开发环境：gcc和linux kernel源代码的.h文件在后面安装GPU的驱动要用

2、确定与Tensorflow对应的cuda、cuDNN版本

    网址：https://tensorflow.google.cn/install/source， 这个是官网，信息比较权威（百度出来的内容很多都不准确，而且比较老。毕竟RTX2070是比较新的显卡）

    从官网的信息可知在LINUX平台上，有如下的对应关系：

版本	Python 版本	编译器	编译工具	cuDNN	CUDA
tensorflow_gpu-1.13.0	2.7、3.3-3.6	GCC 4.8	Bazel 0.19.2	7.4	10.0

 

3、下载并安装CUDA 10和cuDNN

     CUDA: https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=CentOS&target_version=7&target_type=runfilelocal

     cuDNN: https://developer.nvidia.com/rdp/cudnn-archive  Download cuDNN v7.4.2 (Dec 14, 2018), for CUDA 10.0

     说明：1）、不需要另外安装RTX2070的驱动，已经包含在CUDA里面了。

                2）、安装驱动的时候需要把X window关闭：一般就ps -eaf|grep X，找出来直接kill

4、Tensorflow_gpu安装

     根据对应关系，只能用python 3.6。 我习惯用anaconda，在这里犯了不少错误，试了好几个版本，最后确定3.6的最新版本是Anaconda3-5.2.0-Linux-x86_64.sh     tensorfow最新的1.13 gpu版本下载：https://files.pythonhosted.org/packages/7b/b1/0ad4ae02e17ddd62109cd54c291e311c4b5fd09b4d0678d3d6ce4159b0f0/tensorflow_gpu-1.13.1-cp36-cp36m-manylinux1_x86_64.whl

5、安装完成后，运行hello

[root@centos7 Downloads]# python 
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow
/usr/local/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
>>> 
>>> 
>>> hello = tensorflow.constant("Hello")
>>> session = tensorflow.Session()
2019-04-17 22:14:30.674969: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-17 22:14:31.813537: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-04-17 22:14:31.814127: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55a818cf86c0 executing computations on platform CUDA. Devices:
2019-04-17 22:14:31.814155: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2070, Compute Capability 7.5
2019-04-17 22:14:31.816052: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3192000000 Hz
2019-04-17 22:14:31.816643: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55a818d64010 executing computations on platform Host. Devices:
2019-04-17 22:14:31.816661: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
2019-04-17 22:14:31.816846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2070 major: 7 minor: 5 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
totalMemory: 7.76GiB freeMemory: 7.65GiB
2019-04-17 22:14:31.816877: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-04-17 22:14:31.817521: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-17 22:14:31.817535: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-04-17 22:14:31.817543: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-04-17 22:14:31.817675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7438 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070, pci bus id: 0000:01:00.0, compute capability: 7.5)
>>> print(hello)
Tensor("Const:0", shape=(), dtype=string)
>>> session.run(hello)
b'Hello'