TensorFlow setup resources

This page list useful resources about TensorFlow setup.

  • See this post for my installation of GPU TensorFlow on Ubuntu Server 16.04  from source with Cuda 8

I browsed and referenced some of the posts – great appreciation for the authors for their sharing,  and I list them below.


Step 0: Install cuda from the standard repositories. (See How can I install CUDA on Ubuntu 16.04?)

Step 1: Register an nvidia developer account and download cudnn here (about 80 MB)

Step 2: Check where your cuda installation is. For the installation from the repository it is /usr/lib/... and /usr/include. Otherwise, it will be /urs/local/cuda/. You can check it with which nvcc or ldconfig -p | grep cuda

Step 3: Copy the files:

$ cd folder/extracted/contents
$ sudo cp -P include/cudnn.h /usr/include
$ sudo cp -P lib64/libcudnn* /usr/lib/x86_64-linux-gnu/
$ sudo chmod a+r /usr/lib/x86_64-linux-gnu/libcudnn*

Adding -P retains the symbolic links, i.e. sudo cp -P lib64/libcudnn* /usr/lib/x86_64-linux-gnu/, and avoids the message: /sbin/ldconfig.real: /usr/lib/x86_64-linux-gnu/libcudnn.so.5 is not a symbolic link – Max Gordon Jun 26 ’16 at 8:56

For Tensorflow to find everything, I had to copy include/cudnn.h and the libraries in lib64/ to /usr/local/cuda-8.0/include and /usr/local/cuda-8.0/lib64 (using CUDA 8.0, Ubuntu 14.04, Tensorflow 0.12.0rc0) – maybe this is helpful for somebody. – David Stutz Dec 9 ’16 at 12:16


If you don’t want to install TensorFlow, you can use TensorFlow over the web.

  • SageMath

You can use Tensorflow over the web at https://cloud.sagemath.com/ Simply click on the link, create an account (or log in with your GitHub), and create a TensorFlow project.

  • Jupyter

You can also use Jupyter notebook to write TensorFlow programs.

======TensorFlow setup on RedHat supercomputer clusters

Install TensorFlow with GPU support on a RedHat (supercluster) (pdf)

Install TensorFlow on CentOS7 (pdf)

Installing TensorFlow on CentOS (pdf)