RCDS now has ten generally available GPU Nodes. To use the nodes, you’ll need to submit jobs using ‘sbatch’ on fortyfour.ibest.uidaho.edu. To request one or more GPU’s, use an sbatch command like:
sbatch -p gpu-long --gres=gpu:1 ascript.slurmThe GPU nodes in the ‘gpu-long’ partition have a job time limit of one week. Here are the node specs:
| Node(s) | GPU | GPU RAM | Sys RAM | Processor |
|---|---|---|---|---|
| n105 | 2x Nvidia GTX 1080Ti | 11GB | 128 GB | Xeon E5-2620 v4 (16 cores) |
| n106 | 2x Nvidia T4 | 16GB | 64 GB | 2x Xeon E5-2680 v2 (40 cores) |
| n110-113 | 2x Nvidia GTX 1080Ti | 11GB | 128 GB | 2x Xeon E5-2623 v4 (8 cores each) |
| n114 | 2x Nvidia RTX 2080Ti | 12GB | 128 GB | Ryzen 1920X (12 cores) |
| n118 | 1x Nvidia Titan RTX | 24GB | 128 GB | Ryzen 2920X (12 cores) |
| n120-121 | 2x Nvidia T4 | 16GB | 192 GB | 2x Xeon Silver 4216 (32 cores each) |
There is also a single GPU node in the gpu_short partition. This lesser spec’d node (8 cores, 1x Nvidia 1080 Ti, 64G RAM) is intended for short jobs, and debugging scripts. The time limit on the gpu-short partition is 24 hours.
The GPU nodes all have Tensorflow installed in the Python modules.
The Institute for Modeling Collaboration and Innovation (IMCI) purchased five GPU nodes for use by researchers associated with CMCI (and with special permission).
To use these nodes, you must be a member of the ‘cmci’ group, and you have to specifically request the nodes by submitting your job to the cmci partition.
sbatch -p cmci-gpu --gres=gpu:1 job_script.slurmIn order to choose the type of GPU you’d like to use, add it to the gres specification
sbatch -p cmci-gpu --gres=gpu:v100:2 job_script.slurmApplications must be compiled specifically to use these GPU nodes, currently compiled software includes:
