Recently installing TensorFlow-GPU on a service has been experiencing the following error:
ImporError: libcublas.so.10.0: Cannot open shared object file: No such file or directory
iled to load the native TensorFlow Runtime.
I>Error: libcublas.so. Different TensorFlow-GPU versions correspond to different CUDA and CUDNN versions. How do I view CUDA and CUDNN versions?

DA 8.0→ CUDNN V6.0/CUDA 8.0→ CUDNN V6.0/CUDA 9.0→ CUDNN V7.0.5
T>rFlow 1.6/1.5 CUDA 9.0 and 1.3/1.3 CUDA 8.0.
TensorFlow 1.6/1.5 CUDA 9.0
As a result, specify TensorFlow – GPU version to reinstall (Note: you don’t need to install TF before uninstalling it, you can PIP directly because it will automatically detect the installed TF and uninstall it).

pip install  tensorflow-gpu==1.5

Note: it is best to use mirror image such as Tsinghua mirror, the speed difference is not a little bit.

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple some-package

You can test it once it’s installed

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Displaying large section of relevant information indicates successful installation!