cannot locate TensorRT when installing TensorFlow

To install tensorflow on my ubuntu 16.04 laptop git cloned tensorflow and run ./configure
During the setup when asked to specify location of TensorRT, I entered /usr/lib/x86_64-linux-gnu
but it does not find libraries.

Please specify the location where TensorRT is installed. [Default is /usr/lib/x86_64-linux-gnu]:

Invalid path to TensorRT. None of the following files can be found:
/usr/lib/x86_64-linux-gnu
/usr/lib/x86_64-linux-gnu/lib
/usr/lib/x86_64-linux-gnu/lib64
/usr/lib/x86_64-linux-gnu/libnvinfer.so.4

I did installed TensorRT and libnvinfer.so.4 file can be found in

/usr/lib/x86_64-linux-gnu$ ls libnvinfer*
libnvinfer.a          libnvinfer_plugin.so.4      libnvinfer.so.4
libnvinfer_plugin.a   libnvinfer_plugin.so.4.1.0  libnvinfer.so.4.1.0
libnvinfer_plugin.so  libnvinfer.so

And when check the package
$ pip show tensorrt
Name: tensorrt
Version: 4.0.0.3
Summary: Python API for TensorRT, a high-performance deep learning inference optimizer and runtime for deep learning applications.
Home-page: https://developer.nvidia.com/tensorrt
Author: NVIDIA Corporation
Author-email: kismats@nvidia.com
License: NVIDIA Software License
Location: /usr/lib/python2.7/dist-packages
Requires: argparse, enum34, protobuf, pycuda, Flask, future, pillow, numpy

Just wonder why tensorflow configure cannot detect tensorRT.
Any help would be appreciated.

We created a new “Deep Learning Training and Inference” section in Devtalk to improve the experience for deep learning and accelerated computing, and HPC users:
https://devtalk.nvidia.com/default/board/301/deep-learning-training-and-inference-/

We are moving active deep learning threads to the new section.

URLs for topics will not change with the re-categorization. So your bookmarks and links will continue to work as earlier.

-Siddharth

Was Kyubot’s question every resolved?

I have the same issue, and I cannot find the new thread via the link above https://devtalk.nvidia.com/default/board/301/deep-learning-training-and-inference-/
.

Thanks for the help.

Did you check whether the cuda+cudnn version you selected for tensorflow is the same with the tensorrt installation that came with python tensorrt package?