技術標籤：tensorflow

1.首先安裝bazel

如果linux系統上沒有bazel，可參考https://blog.csdn.net/qq_41204464/article/details/95333396進行安裝。建議採用“用二進位制安裝程式安裝”

2.下載tensorflow原始檔

從https://github.com/tensorflow/tensorflow/tags中選擇需要編譯的tensorflow版本

3.配置tensroflow庫

進入tensorflow原始碼根目錄，執行 ./configure 進行配置，根據需求進行配置，以下是我的配置。從下面看出，所有配置我都選擇no，因為我都不需要，連cuda我也不要，因為我要編譯的tensorflow是要用在cpu上面的。

You have bazel 0.15.0 installed.
Please specify the location of python. [Default is /media/huaxin/tcl2/nlp/anaconda3/bin/python]:

Found possible Python library paths:
/media/huaxin/tcl2/nlp/anaconda3/lib/python3.6/site-packages
Please input the desired Python library path to use. Default is [/media/huaxin/tcl2/nlp/anaconda3/lib/python3.6/site-packages]

Do you wish to build TensorFlow with Apache Ignite support? [Y/n]: n
No Apache Ignite support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n

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]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

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]:

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 tools/bazel.rc 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.
Configuration finished

4.編譯tensroflow庫

由於我需要將mkl庫加到tensorflow中，所以使用一下命令進行編譯

bazel build --config=mkl -c opt --copt=-march=native //tensorflow/tools/pip_package:build_pip_package

5.生成whl檔案

通過一下命令，生成whl檔案，[path_to_save_wheel]為生成的whl檔案的儲存路徑

bazel-bin/tensorflow/tools/pip_package/build_pip_package ~/path_to_save_wheel

6.使用pip命令進行安裝即可

7.出現的坑

在第4步中，出現下述報錯，可參考https://www.freesion.com/article/5917370408/和https://blog.csdn.net/weixin_39634443/article/details/111109185進行解決