使用bazel安装tensorflow
为了提高CPU运行速度，使用SSE/AVX/FMA指令集，需要从source安装tensorflow，其中最简便的就是利用bazel安装tensorflow，但是安装过程也是充满了血泪。。。
1. 安装bazel
conda install bazel
2. 下载tensorflow代码包
在https://github.com/tensorflow/tensorflow中下载tf包 并上传到服务器中并解压 当然，服务器网速好的话也可以使用指令 $git clone https://github.com/tensorflow/tensorflow
3. 进入tensorflow-master文件夹
cd tensorflow #cd to the top-level directory created
4. 设置configure（请打起精神！）
这里比较重要，虽然都有提示，但还是很容易踩坑
./configure
configure 的时候要选择一些东西是否支持，这里建议都选N，不然后面会报错，如果支持显卡，就在cuda的时候选择y,然后按照提示填写自己的cuda cudnn的版本
整个flow如下(#字为我的注释)：
(python2bazel) hthong@node150:~/software/tensorflow-master$ ./configure
WARNING: detected http_proxy set in env, setting no_proxy for localhost.
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
INFO: Invocation ID: a54e5cb9-a34d-4848-ae0c-b17d9973d404
You have bazel 0.20.0- (@non-git) installed.
Please specify the location of python. [Default is /home/hthong/anaconda3/envs/python2bazel/bin/python]:
Found possible Python library paths:
/home/hthong/anaconda3/envs/python2bazel/lib/python2.7/site-packages
Please input the desired Python library path to use. Default is [/home/hthong/anaconda3/envs/python2bazel/lib/python2.7/site-packages]
Do you wish to build TensorFlow with XLA JIT support? [Y/n]: Y #这里可以设置为Y，可以提高tensorflow的运行效率
XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 10.0]:
Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]:
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.
Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]:
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. #去这个官网查自己GPU型号对应的计算能力系数，一般默认值都与官网一致
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1,6.1,6.1,6.1,6.1,6.1,6.1,6.1]:
Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler. [Default is /home/hthong/anaconda3/envs/python2bazel/bin/gcc]: /usr/bin/gcc
#gcc要求是多版本的（64、32）的gcc，一般使用系统目录下的,
#对于gcc 5或更高版本的说明：TensorFlow 网站上提供的二进制pip软件包是使用gcc4编译的，高版本gcc涉及一些setting，请自己翻官网
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=monolithic # Config for mostly static monolithic build.
--config=gdr # Build with GDR support.
--config=verbs # Build with libverbs support.
--config=ngraph # Build with Intel nGraph support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
Preconfigured Bazel build configs to DISABLE default on features:
--config=noaws # Disable AWS S3 filesystem support.
--config=nogcp # Disable GCP support.
--config=nohdfs # Disable HDFS support.
--config=noignite # Disable Apacha Ignite support.
--config=nokafka # Disable Apache Kafka support.
--config=nonccl # Disable NVIDIA NCCL support.
Configuration finished
5.编译
编译之前，需安装：numpy，enum，keras，mock 根据是否使用GPU选择以下命令执行：
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package # CUP-only
bazel build --config=opt --config=cuda --verbose_failures //tensorflow/tools/pip_package:build_pip_package # GPU support
这里我遇到了一个坑：
ERROR: missing input file '@pasta//:LICENSE'
ERROR: /home/shiki/tensorflow/tensorflow/tools/pip_package/BUILD:235:1: //tensorflow/tools/pip_package:build_pip_package: missing input file '@pasta//:LICENSE'
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
ERROR: /home/shiki/tensorflow/tensorflow/tools/pip_package/BUILD:235:1 1 input file(s) do not exist
根据网友的智慧：https://github.com/tensorflow/tensorflow/issues/24722 删掉了（注释掉也行）tensorflow/tools/pip_package/BUILD文件中的第L172行代码（”@pasta//:LICENSE”），完美解决
然后又遇到了一个坑：
from tensorflow.python.ops import variables
File "/home/hthong/.cache/bazel/_bazel_hthong/36e03d3843aed90a32db1b13ecd82b39/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/python/keras/api/create_tensorflow.python_api_2_keras_python_api_gen_compat_v2.runfiles/org_tensorflow/tensorflow/python/ops/variables.py", line 118, in <module>
"* `ONLY_FIRST_TOWER`: Deprecated alias for `ONLY_FIRST_REPLICA`.\n ")
AttributeError: attribute '__doc__' of 'type' objects is not writable
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 3.041s, Critical Path: 0.72s
INFO: 0 processes.
FAILED: Build did NOT complete successfully
还是网友智慧：https://github.com/tensorflow/tensorflow/issues/12491
卸载enum，安装enum34
6.生成whl
过了很久之后，第五步完成，生成了一个uild_pip_package脚本。
成功示例：
Target //tensorflow/tools/pip_package:build_pip_package up-to-date:
bazel-bin/tensorflow/tools/pip_package/build_pip_package
INFO: Elapsed time: 270.072s, Critical Path: 269.03s
INFO: 60 processes: 60 local.
INFO: Build completed successfully, 61 total actions
(python2bazel) hthong@node150:~/software/tensorflow-master$
然后我们就可以根据这个脚本生成whl文件了
The bazel build command builds a script named build_pip_package. Running this script as follows will build a .whl file within the /home/hthong/software/tensorflow_pkg directory
bazel-bin/tensorflow/tools/pip_package/build_pip_package /home/hthong/software/tensorflow_pkg
显示如下：
(python2bazel) hthong@node150:~/software/tensorflow-master$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /home/hthong/software/tensorflow_pkg
Mon Jan 7 17:52:44 UTC 2019 : === Preparing sources in dir: /tmp/tmp.GFfdUdpEig
~/software/tensorflow-master ~/software/tensorflow-master
~/software/tensorflow-master
Mon Jan 7 17:53:00 UTC 2019 : === Building wheel
warning: no files found matching '*.pyd' under directory '*'
warning: no files found matching '*.pd' under directory '*'
warning: no files found matching '*.dll' under directory '*'
warning: no files found matching '*.lib' under directory '*'
warning: no files found matching '*.h' under directory 'tensorflow/include/tensorflow'
warning: no files found matching '*' under directory 'tensorflow/include/Eigen'
warning: no files found matching '*.h' under directory 'tensorflow/include/google'
warning: no files found matching '*' under directory 'tensorflow/include/third_party'
warning: no files found matching '*' under directory 'tensorflow/include/unsupported'
Mon Jan 7 17:53:32 UTC 2019 : === Output wheel file is in: /home/hthong/software/tensorflow_pkg
(python2bazel) hthong@node150:~/software/tensorflow-master$ ls
ACKNOWLEDGMENTS CONTRIBUTING.md SECURITY.md bazel-tensorflow-master third_party
ADOPTERS.md ISSUES.md WORKSPACE bazel-testlogs tools
AUTHORS ISSUE_TEMPLATE.md arm_compiler.BUILD configure
BUILD LICENSE bazel-bin configure.py
CODEOWNERS README.md bazel-genfiles models.BUILD
CODE_OF_CONDUCT.md RELEASE.md bazel-out tensorflow
(python2bazel) hthong@node150:~/software/tensorflow-master$ cd ..
(python2bazel) hthong@node150:~/software$ ls
Anaconda3-2018.12-Linux-x86_64.sh srun.sh tensorflow-master tensorflow-master.zip tensorflow_pkg
(python2bazel) hthong@node150:~/software$ cd tensorflow_pkg
(python2bazel) hthong@node150:~/software/tensorflow_pkg$ ls
tensorflow-1.12.0-cp27-cp27mu-linux_x86_64.whl
(python2bazel) hthong@node150:~/software/tensorflow_pkg$
7.安装
终于可以安装了：
pip install /home/hthong/software/tensorflow_pkg/your_tensorflow_whlname.whl
提示成功之后就可以验证tensorflow是否安装成功了，记住退出当前tensorflow文件夹后再进行验证，不然会fail
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
你可以看到,没有warning了！！！tensorflow速度是不是变快了！！
