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Due to recent developments in deep learning and related topics, tensorflow and its components have been widely requested by the user community. Due to the nature of the package however, we have decided it is best for the user to manage their own installation to ensure that they can quickly modify or upgrade tensorflow to their own needs without waiting for Research Computing to handle version changes. This page therefore serves to provide basic instructions on how to install tensorflow without elevated privileges, into a local directory that is owned by the user.
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if you are in an interactive session (on a GPU node), you can now start running your code. If you plan to submit a batch job, place all of the above commands (with the choice of python module) into your submission script. From here, you should be all set! Make sure you fully understand the Using O2 GPU resources page before submitting to the partition, as there are a limited amount of resources available, and pend time is highly variable depending on current demand.
Installation with (ana)conda
If you are familiar with the conda package manager (or the Anaconda environment system), it is also possible to install Tensorflow this way (and depending on your project requirements, this may be the best way to handle your Tensorflow installation). If you have your own ana/conda installation, skip to after the module load command.
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Depending on when you installed your copy of Tensorflow, you may see something like this when you run your code:
Loaded runtime CuDNN library: 7.0.4 but source was compiled with: 7.2.1. CuDNN ... Segmentation Fault
This is because the version of Tensorflow you installed used a newer version of CuDNN than the one that was found on the cluster. To fix this, you'll need to download a newer version of CuDNN and place it somewhere in a directory you own. You can submit a ticket with us if you'd like assistance with this issue.
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