-
Notifications
You must be signed in to change notification settings - Fork 21
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Replicating experiments with CUDA version > 8.0 #2
Comments
Hi Sotiris, Apologies for my slow response! I haven't touched this codebase in 4 years. If I were you I would try removing the dated tensorflow in the virtualenv and just use the working tensorflow version on your system, though there may be API changes that causes the tensorflow code to break. Hope that helps Cheers, |
Hi, One of the useful solution is to use docker or singularity container to run this code. docker://tensorflow/tensorflow:1.14.0-gpu-py3 is available to execute the device mapping code. hope it's helpful. Cheers, |
Thanks @xshaun that's a great suggestion! |
Hello Chris!
I am trying to replicate your first case study regarding OpenCL heterogeneous mapping and I am trying to use CUDA. When I import tensorflow from the Python 3 CLI on my machine, everything works as expected, loading my version of the CUDA library (
libcudart.so.11.2.72
), but when I import tensorflow from inside of one of your Jupyter notebooks (which are launched from inside the virtualenv you create usingmake run
) I get anImportError
that the filelibcudart.so.8.0
could not be found.To my understanding, the tensorflow version used in your virtualenv needs CUDA version 8.0 explicitly ( ? ). Is there any workaround for this to work with my CUDA version? Tried a workaround with symlinks, but it seems that there are deeper dependencies and it still does not work.
Thanks a lot in advance!
The text was updated successfully, but these errors were encountered: