自己的桌上型電腦裝了影馳1050ti大將顯示卡，平時也不怎麼玩遊戲，乾脆把TensorFlow-GPU版給裝上試試。

本來想裝docker版本，http://blog.csdn.net/ixuhangyi/article/details/52291716，但是據說docker對GPU的支援有限，且對本地開發環境的支援也不夠友好，因此直接用pip安裝的方式

最重要的就是滿足下面的先決條件：注意版本都要對應，cuDNN v6.0親測不行，必須要v5.1，此處安裝過程請參考本文開頭參考文章中win10的安裝

Requirements to run TensorFlow with GPU support

If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system:

• CUDA® Toolkit 8.0. For details, see NVIDIA's documentation Ensure that you append the relevant Cuda pathnames to the %PATH% environment variable as described in the NVIDIA documentation.
• The NVIDIA drivers associated with CUDA Toolkit 8.0.
• cuDNN v5.1. For details, see NVIDIA's documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH%
  environment variable.
• GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards.

If you have an earlier version of the preceding packages, please upgrade to the specified versions.

遇到的坑：之前安裝了visual c++2017的庫，但是TensorFlow需要visual c++2015，解除安裝掉（x86 x64的visual c++2017都要解除安裝），重新安裝2015，都弄完了最好重啟下電腦

準備好前提條件後：開啟命令列：建立一個Python3.5的環境tensor_flow ，因為TensorFlow只支援Python3.5

conda create -n tensor_flow python=3.5

啟用環境：activate tensor_flow

pip安裝TensorFlow：pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-win_amd64.whl 

安裝完成，進入Python互動命令列，執行測試樣例，執行成功