WIN10+RTX2070 tensorflow-gpu环境搭建

2019独角兽企业重金招聘Python工程师标准>>>

参考

tensorflow-windows-wheel 我选择了：1.12.0\py37\GPU\cuda100cudnn73sse2

前置环境

• Visual Studio 2017
• Anaconda3

这两个我都安装的最新版。
Visual Studio 2017不装会导致安装CUDA时"Visual Studio Integration"组件安装失败，我在此处卡了几天，查了好多资料都无效。

GPU相关软件下载

• Nvidai驱动
• CUDA10.0： cuda-tookit下载， 选择CUDA Toolkit 10.0
• cuDNN：cuDNN下载， 选择Download cuDNN v7.3.1 (Sept 28, 2018), for CUDA 10.0
• tensorflow安装包：tensorflow-windows-wheel

安装

• 显卡驱动安装
• 安装CUDA

安装好之后的环境变量默认包含：
CUDA_PATH：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0
CUDA_PATH_V10_0：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0
PATH:
	C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
	C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\libnvvp
	C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\extras\CUPTI\libx64(这个需要自己添加)

• 安装cuDNN

解压cuDNN后得到bin、include、lib三个文件夹
将bin下的cudnn64_7.dll拷贝到：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
将include下的cudnn.h拷贝到：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include
将lib\x64下的cudnn.lib拷贝到：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64

tensorflow-gpu环境配置

• 打开
• 创建环境

  	conda create -n tensorflow_gpu_1.12_py37 python=3.7
  

• 激活环境

  	conda activate tensorflow_gpu_1.12_py37
  

• 安装tensorflow-gpu

  	pip install E:\Downloads\tensorflow_gpu-1.12.0-cp37-cp37m-win_amd64.whl
  

• 测试

  	进入python
  	>>> import tensorflow as tf
  	>>> hello = tf.constant('Hello, tensorflow!')
  	>>> sess = tf.Session()
  

  输出：

  	2019-03-02 09:37:52.875467: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
  	2019-03-02 09:37:53.200917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:name: GeForce RTX 2070 major: 7 minor: 5 memoryClockRate(GHz): 1.62
  	pciBusID: 0000:01:00.0
  	totalMemory: 8.00GiB freeMemory: 6.59GiB
  	2019-03-02 09:37:53.207587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
  	2019-03-02 09:40:52.845289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
  	2019-03-02 09:40:52.848702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
  	2019-03-02 09:40:52.850105: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
  	2019-03-02 09:40:52.854219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow 
  	device (/job:localhost/replica:0/task:0/device:GPU:0 with 6331 MB memory) -> physical GPU (device: 0, 
  	name: GeForce RTX 2070, pci bus id: 0000:01:00.0, compute capability: 7.5)
  

  接着执行

  	print(sess.run(hello))
  

  输出：

  	b'Hello, tensorflow!'
  

  关闭session

  	sess.close()
  

至此结束，第一次感觉开发环境搭建不容易。

转载于:https://my.oschina.net/pasenger/blog/3016987