TensorFlow GPU版源码编译安装

前提：已安装CUDA，cuDNN，NCCL，bazel

 

1. configure

[user1@node1 tensorflow-1.12.0]$ ./configure

Please specify the location of python. [Default is /usr/bin/python]:              //回车

Found possible Python library paths:
  /usr/lib/python2.7/site-packages
  /usr/lib64/python2.7/site-packages
Please input the desired Python library path to use.  Default is [/usr/lib/python2.7/site-packages]                //回车

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]: y
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 9.0]: 10.0   //CUDA版本

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]:     //cuDNN版本

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 NCCL version you want to use. If NCCL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. [Default is 2.2]: 2.4   //NCCL版本

NCCL libraries found in /usr/lib64/libnccl.so
Assuming NCCL header path is /usr/lib64/../include
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.
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]: 6.1,7.0,7.5   //指定GPU硬件版本

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 /usr/bin/gcc]:

Do you wish to build TensorFlow with MPI support? [y/N]: n   //根据需求选择y或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]:     //回车

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
2. 编译&生成wheel包

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

3. 安装，需要用root

sudo pip install /tmp/tensorflow_pkg/tensorflow-1.12.0-cp27-cp27mu-linux_x86_64.whl

安装个人用户下：

pip install --prefix=~/tf_bin --ignore-installed /tmp/tensorflow_pkg/tensorflow-1.12.0-cp27-cp27mu-linux_x86_64.whl

export PATH=~/tf_bin/bin:$PATH

export PYTHONPATH=~/tf_bin/lib/python2.7/site-packages/