Tensorflow GPU import error - tensorflow

Question

I have CUDA 8.0, and I can download cuDNN. Currently, I have cuDNN version 7.0.5 for Linux.
I do not have administrator privileges.
When I tried to install TensorFlow version 1.4 for GPU, I got this error:
ImportError: libcudnn.so.6: cannot open shared object file: No such file or directory
I figured this was due to the absence of cuDNN on my machine. I downloaded version 7.0.5, at the advice of the sysadmin, which is of course not the version the error message wanted me to get (it wanted version 6).
So I thought, I'll try Tensorflow version 1.5 for GPU. I got this error:
ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory
What should I do? Is there a way to download older versions of cuDNN? Or a way to download cublas 9.0 somewhere?

Answer 1

Yes if you register at nvidia you can also download older versions of cuDNN. It‘s a little hidden though. Make sure you download the right version which is compatible to your cuda version. Also don‘t forget to set CUDA_HOME environment variable for tensorflow to find your GPU.

Answer 2

This is what ended up working for me:
Steps to install tensorflow-gpu on a remote machine via a local machine.
1) SSH into the remote machine via something like: ssh -X username#remote
2) Use pip install tensorflow-gpu for a first-time install of tensorflow-gpu. This will give you the most current version. If you want an older version, you can specify it with pip install tensorflow-gpu==1.4.0 (for example)
3) If you get an error, you likely either need to install CUDA or cuDNN.
To check your CUDA version:
* cd /usr/local/cuda
* vim version.txt
To download cuDNN:
Go to https://developer.nvidia.com/cudnn
Sign up for a free developer account (you will be prompted to do this via the ‘Download’ button)
Once you’ve created an account and logged in, click the box next to “I agree to the terms of the cuDNN software licence agreement”. A list of possible cuDNN versions for download will appear.
The error message from the terminal will tell you which version of cuDNN you need. For example, “libcudnn.so.6” in the error message means it’s looking for cuDNN version 6.
Click Download cuDNN v6.0 (April 27, 2017), for CUDA 8.0 (note your CUDA version must be aligned with your cuDNN version - you may not be able to use cuDNN version 6.0 with CUDA version 9.0, for example).
Click cuDNN v6.0 Library for Linux (if you have a Linux machine, and you are not trying to install cuDNN for an entire system). A download of a zipped folder will be started.
Unzip the folder and save it on your Desktop. Call the folder ‘cuda’.
Secure copy the folder and all its contents to the cluster. For example:
scp -r /Users/username/Desktop/cuda username#remote:~/path/to/a/folder/you/use
SSH into the remote server via ssh -X username#remote
Copy (or move) the cuda folder via something like: cp -r cuda /path/to/where/you/want/cuda
cd /path/to/where/you/want/cuda
echo "export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:path/to/where/you/want/cuda/lib64"
>>$HOME/.bashrc
Restart your terminal window
SSH into the remote again and try importing tensor flow in Python. If it succeeds, great! If not, preload the library path before starting python and that should work. You can do this with:
LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/path/to/where/you/want/cuda/lib64 python