Two BRCF research pods have NVIDIA GPU servers; however their use is restricted to the groups who own those pods.
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The AlphaFold protein structure solving software is available on all AMD GPU servers. The /stor/scratch/AlphaFold directory has the large required database, under the data.3 sub-directory. There is also an AMD example script /stor/scratch/AlphaFold/alphafold_example_amd.shand an alphafold_example_nvidia.sh script if the POD also has NVIDIA GPUs, (e.g. the Hopefog pod). Interestingly, our timing tests indicate that AlphaFold performance is quite similar on all the AMD and NVIDIA GPU servers.
TensorFlow and PyTorch examples
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If GPUs are available and accessible, the output generated will indicate they are being used.
Note that our that our system-wide CUDA-enabled TensorFlow and PyTorch versions are only available in the default Python 3 command-line environment (e.g. python3 or python3.8 on the command line). They are not yet available in the global JupyterHub environment that uses the Python 3.9 kernel. If you need a different combination of Python and TensorFlow/PyTorch versions, you'll need to construct an appropriate custom Conda environment (e.g. miniconda3 or anaconda).
Resources
CUDA
Both hfogcomp04 and wilkcomp03 have both CUDA 11.8 and CUDA 12.x installed, under version-specific subdirectories of /usr/local.
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