系統：
Ubuntu 16.04LTS
配置：
GeForce GTX 1060 （6078MiB）
已安裝好的顯卡驅動：
NVIDIA-SMI 418.56 Driver Version: 418.56
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.56 Driver Version: 418.56 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 00000000:01:00.0 Off | N/A |
| 33% 36C P5 22W / 250W | 297MiB / 11178MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1321 G /usr/lib/xorg/Xorg 226MiB |
| 0 2059 G compiz 62MiB |
| 0 3418 G /usr/lib/firefox/firefox 2MiB |
| 0 3824 G /usr/lib/firefox/firefox 2MiB |
+-----------------------------------------------------------------------------+
CUDA 10.0
下載runfile文件：
Archived Releases：CUDA Toolkit 10.0
https://developer.nvidia.com/cuda-10.0-download-archive（需要登錄）
安裝：
1.Run sudo ./cuda_10.0.130_410.48_linux.run
2.Follow the command-line prompts
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: n
Install the CUDA 10.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-10.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 10.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/toson ]:
把cuda10.1卸載掉：
:/usr/local/cuda-10.1/bin$ sudo ./cuda-uninstaller
cudnn 7.5
下載7.5版本：https://developer.nvidia.com/rdp/cudnn-archive
解壓到cuda目錄：
$ sudo tar -zxvf cudnn-10.0-linux-x64-v7.5.0.56.tgz -C /usr/local/
其他軟件
1. PyTorch
PyTorch的C++調用庫可以在官網直接下載，解壓即可，無需安裝。
需要重新下載基於cuda10.0的：https://pytorch.org/get-started/locally/
我下載的是(cxx11 ABI)：
https://download.pytorch.org/libtorch/cu100/libtorch-cxx11-abi-shared-with-deps-1.2.0.zip
下載後解壓，並在CMakeLists.txt中包含引用：
set(Torch_DIR /home/toson/download_libs/libtorch/share/cmake/Torch)
find_package(Torch REQUIRED)
2. TensorFlow重新編譯
我下載的是1.12版本：https://github.com/tosonw/tensorflow/archive/v1.12.0.tar.gz
解壓後命令行進入目錄：
$ mkdir build
$ cd build
$ ../configure
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.15.2 installed.
Please specify the location of python. [Default is /home/toson/anaconda3/bin/python]:
Found possible Python library paths:
/home/toson/anaconda3/lib/python3.6/site-packages
Please input the desired Python library path to use. Default is [/home/toson/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
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
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 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]: 1.3
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]:
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]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/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
編譯：
# 注："-D_GLIBCXX_USE_CXX11_ABI=0"是由於protobuf是基於GCC4等等一系列原因。
$ bazel build --config=opt --config=cuda //tensorflow:libtensorflow_cc.so
#等待有點久，最後提示以下內容就算成功了：
Target //tensorflow:libtensorflow_cc.so up-to-date:
bazel-bin/tensorflow/libtensorflow_cc.so
INFO: Elapsed time: 1206.366s, Critical Path: 162.98s
INFO: 4956 processes: 4956 local.
INFO: Build completed successfully, 5069 total actions
編譯成功後，在 /bazel-bin/tensorflow 目錄下會出現 libtensorflow_cc.so 文件
C版本： bazel build :libtensorflow.so
C++版本： bazel build :libtensorflow_cc.so
需要的頭文件，要在源碼裏拷貝出來使用：
bazel-genfiles/...，eigen/...，include/...，tf/...
需要的文件都拷貝出來後，可使用bazel clean命令，把編譯的那些瑣碎文件清除掉，那些太佔空間。
3. TensorRT
我原來的TensorRT是基於cuda10.1的
需要重新下載基於cuda10.0的TensorRT：
下載（需要登錄）：https://developer.nvidia.com/tensorrt
TensorRT 6.0.1.5 GA for Ubuntu 16.04 and CUDA 10.0 tar package
然後解壓到自己的目錄，再在CMakeLists.txt中包含就可以了。
注：tensorRT模型需要基於其版本重新生成模型文件。
如果要在python裏使用tensorRT，需要安裝：
$ cd TensorRT-6.0.1.5/python
$ pip install tensorrt-6.0.1.5-cp36-none-linux_x86_64.whl
