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Austin's own Advanced Micro Devices (AMD) has most generously donated a number of GPU-enabled servers to UT.
While it is still true that AMD GPUs do not support as many 3rd party applications as NVIDIA, they do support many popular Machine Learning (ML) applications such as TensorFlow, PyTorch, and AlphaFold, and Molecular Dynamics (MD) applications such as GROMACS, all of which are installed and ready for use.
Our recently announced AMD GPU pod is available for both research and instructional use, for any UT-Austin affiliated PIs. To request an allocation, ask your PI to contact us at rctf-support@utexas.edu, and provide the UT EIDs of those who should be granted access.
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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.4 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.
On AMD GPU servers, AlphaFold is implemented by a run_alphafold.py Python script inside a Docker image, See the run_alphafold_rocm.sh and run_multimer_rocm.sh scripts under /stor/scratch/AlphaFold for a complete list of options to that script.
Pytorch and TensorFlow
Two Python scripts are located in /stor/scratch/GPU_info that can be used to ensure you have access to the server's GPUs from TensorFlow or PyTorch. Run them from the command line using time to compare the run times.
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If GPUs are available and accessible, the output generated will indicate they are being used.
All pod compute servers have 3 main Python environments, which are all managed separately (see About Python and JupyterHub server for more information about these environments):
- command-line Python 3.8 (python3, python3.8, pip3, pip3.
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- command-line Python 3.9 (python3,9, pip3.9)
- web-based JupyterHub which uses the Python 3.9 kernel
We are working hard to get AMD-GPU-enabled versions of TensorFlow and PyTorch working in all three environments. Current status is as follows:
| POD | AMD-GPU-enabled PyTorch | AMD-GPU-enabled TensorFlow |
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| Hopefog |
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| Livestrong |
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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) as well as your own Jupyter Notebook environment if needed.
Installing a custom TensorFlow version
The AMD-GPU-specific version of TensorFlow, Tensorflow-rocm 2.9.1 is installed on all most AMD GPU servers. This version works with ROCm 5.1.3+. If you need to install your own version with pip, specify this versionthe version explicitly, e.g.:
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pip install tensorflow-rocm==2.9.1 |
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- ROCm Video series
- https://community.amd.com/t5/instinct-accelerators-blog/rocm-open-software-ecosystem-for-accelerated-compute/ba-p/418720
- Especially the Introduction to AMD GPU Hardware: Link
- Provides hardware background and terminology used throughout other guides
- Also
- AMD ROCm resources Learning Center: https://developer.amd.com/resources/rocm-resources/rocm-learning-center/
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