提示：使用tensorrt 6一直莫名其妙編譯失敗，追蹤問題也沒有打印，所以改成tensorrt 5後成功了，後面有時間我再試試源碼編譯tensorrt。
環境
- cuda 10.1
- cudnn 7.6.3
- tensorrt 5.1.5
- tensorflow 1.14.0
步驟
將tensorrt 5解壓後的include、lib文件夾複製到/usr目錄下（合併）
解壓tensorflow，切換到v1.14.0版本，按如下修改代碼
之後執行
./configure
接下來彈出交互式選項
spicker@spicker-1:~/software/tensorflow$ ./configure
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.25.2 installed.
Please specify the location of python. [Default is /home/spicker/anaconda3/bin/python]:
Found possible Python library paths:
/home/spicker/anaconda3/lib/python3.6/site-packages
Please input the desired Python library path to use. Default is [/home/spicker/anaconda3/lib/python3.6/site-packages]
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
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.
Do you wish to build TensorFlow with TensorRT support? [y/N]: y
TensorRT support will be enabled for TensorFlow.
Found CUDA 10.1 in:
/usr/local/cuda/lib64
/usr/local/cuda/include
Found cuDNN 7.6.3 in:
/usr/local/cuda/lib64
/usr/local/cuda/include
Found TensorRT 5 in:
/usr/lib
/usr/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, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 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 /usr/bin/gcc]:
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=numa # Build with NUMA 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 Apache Ignite support.
--config=nokafka # Disable Apache Kafka support.
--config=nonccl # Disable NVIDIA NCCL support.
Configuration finished
GPU support
To make the TensorFlow package builder with GPU support:
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
Bazel build options
See the Bazel command-line reference for build options.
Building TensorFlow from source can use a lot of RAM. If your system is memory-constrained, limit Bazel’s RAM usage with: --local_ram_resources=2048.
The official TensorFlow packages are built with GCC 4 and use the older ABI. For GCC 5 and later, make your build compatible with the older ABI using: --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0". ABI compatibility ensures that custom ops built against the official TensorFlow package continue to work with the GCC 5 built package.
Build the package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
編譯成C api庫
bazel build --config opt --config=cuda //tensorflow/tools/lib_package:libtensorflow
ref: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/lib_package/README.md
如果要使用C++ 的tf-trt的方法，記得在tensorflow/BUILD中添加下面標亮的三行代碼：
"//tensorflow/contrib/tensorrt:trt_conversion",
"//tensorflow/contrib/tensorrt:trt_engine_op_op_lib",
"//tensorflow/contrib/tensorrt:trt_op_kernels",
ref：
https://blog.csdn.net/surtol/article/details/97638399#26GPU_237
https://yuxy.tk/2019/09/23/tensorflow_build_with_local_repository/