這一系列基本上是屬於我自己進行到了那個步驟就做到那個步驟的

由於新裝了GPU (GTX750ti)和CUDA9.0、CUDNN7.1版本的軟件，所以希望TensorFlow能在GPU上運行，也算上補上之前的承諾

說了下初衷，由於現在新的CUDA版本對TensorFlow的支持不好，只能采取編譯源碼的方式進行

所以大概分為以下幾個步驟

1.安裝依賴庫（這部分我已經做過了，不進行介紹，可以看前邊的依賴庫，基本一致）

2.安裝Git（有的就跳過這一步）

3.安裝TensorFlow的build工具bazel

4.編譯TensorFlow源碼

5.安裝並配置環境變量

1.安裝依賴庫

2.安裝Git

使用

sudo
 
 apt-get install git

3. 安裝TensorFlow的build工具bazel

這一步比較麻煩，是因為apt-get中沒有bazel這個工具

因此需要到GitHub上先下載，再進行安裝 下載地址是https://github.com/bazelbuild/bazel/releases

選擇正確版本下載，並使用sudo命令安裝.sh文件即可

4.編譯TensorFlow源碼

這一步特別麻煩，有很多選項需要選擇，我的選擇如下：

 1 jourluohua@jour:~/tools/tensorflow$ ./configure 
 2 WARNING: Running Bazel server needs to be killed, because the startup options are different.
 
 3 You have bazel 0.14.1 installed.
 4 Please specify the location of python. [Default is /usr/bin/python]: 
 5 
 6 
 7 Found possible Python library paths:
 8   /usr/local/lib/python2.7/dist-packages
 9   /usr/lib/python2.7/dist-packages
10 Please input the desired Python library path to use.  Default is [/usr/local/lib/python2.7
 
/dist-packages]
11 
12 Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: Y
13 jemalloc as malloc support will be enabled for TensorFlow.
14 
15 Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
16 No Google Cloud Platform support will be enabled for TensorFlow.
17 
18 Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
19 No Hadoop File System support will be enabled for TensorFlow.
20 
21 Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
22 No Amazon S3 File System support will be enabled for TensorFlow.
23 
24 Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
25 No Apache Kafka Platform support will be enabled for TensorFlow.
26 
27 Do you wish to build TensorFlow with XLA JIT support? [y/N]: y
28 XLA JIT support will be enabled for TensorFlow.
29 
30 Do you wish to build TensorFlow with GDR support? [y/N]: y
31 GDR support will be enabled for TensorFlow.
32 
33 Do you wish to build TensorFlow with VERBS support? [y/N]: y
34 VERBS support will be enabled for TensorFlow.
35 
36 Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N
37 No OpenCL SYCL support will be enabled for TensorFlow.
38 
39 Do you wish to build TensorFlow with CUDA support? [y/N]: y
40 CUDA support will be enabled for TensorFlow.
41 
42 Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 8
43 
44 
45 Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 
46 
47 
48 Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 
49 
50 
51 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
52 
53 
54 Do you wish to build TensorFlow with TensorRT support? [y/N]: N
55 No TensorRT support will be enabled for TensorFlow.
56 
57 Please specify the NCCL version you want to use. [Leave empty to default to NCCL 1.3]: 
58 
59 
60 Please specify a list of comma-separated Cuda compute capabilities you want to build with.
61 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
62 Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.0]
63 
64 
65 Do you want to use clang as CUDA compiler? [y/N]: N
66 nvcc will be used as CUDA compiler.
67 
68 Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: 
69 
70 
71 Do you wish to build TensorFlow with MPI support? [y/N]: N
72 No MPI support will be enabled for TensorFlow.
73 
74 Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: 
75 
76 
77 Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: N
78 Not configuring the WORKSPACE for Android builds.
79 
80 Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
81     --config=mkl             # Build with MKL support.
82     --config=monolithic      # Config for mostly static monolithic build.
83 Configuration finished

然後使用bazel進行編譯(本步驟非常容易出問題，而且特別耗時)

bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

5.安裝並配置環境變量

使用pip進行安裝

$ sudo pip install /tmp/tensorflow_pkg/tensorflow

# with no spaces after tensorflow hit tab before hitting enter to fill in blanks

最後就是測試

import tensorflow as tf
sess = tf.InteractiveSession()
sess.close()

如果每一步都不報錯的，TensorFlow就編譯並安裝成功了

TensorFlow入門——安裝（帶GPU）