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.
Two BRCF research pods also have AMD GPU servers available: the Hopefog and Livestong PODs. Their use is restricted to the groups who own those pods. See Livestrong and Hopefog pod AMD servers for specific information.
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.sh and 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.
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.
If GPUs are available and accessible, the output generated will indicate they are being used.
The AMD-GPU-specific version of TensorFlow, Tensorflow-rocm 2.9.1 is installed on all AMD GPU servers. This version works with ROCm 5.1.3+. If you need to install your own version with pip, specify this version:
pip install tensorflow-rocm==2.9.1 |
You may also need to adjust your LD_LIBRARY_PATH as follows:
export LD_LIBRARY_PATH="/opt/rocm-5.1.3/hip/lib:$LD_LIBRARY_PATH" |
ROCm is AMD's equivalent to the CUDA framework. ROCm is open source, while CUDA is proprietary.
We have multiple versions of the ROCm framework installed in the /opt directory, designated by a version number extension (e.g. /opt/rocm-5.1.3, /opt/rocm-5.2.3). The default version is the one pointed to by the /opt/rocm symbolic link, which is generally the latest version.
To specify a specific ROCm version, set the ROCM_HOME environment variable; for example:
export ROCM_HOME=/opt/rocm-5.1.3 |
You may also need to adjust your LD_LIBRARY_PATH as follows:
export LD_LIBRARY_PATH="/opt/rocm-5.1.3/hip/lib:$LD_LIBRARY_PATH" |
Since there's no batch system on BRCF POD compute servers, it is important for users to monitor their resource usage and that of other users in order to share resources appropriately.
Best starting places: