Accessing Lab's Workstation
Last updated on July 7, 2025
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Last updated on July 7, 2025
CPU: 56 cores, 112 threads, W9 Xeon Processors
GPU: Dual RTX 5000 ada (x2), 32 GB each
RAM: 128 GB
login: username@ada.qtinslab.com
Home directories: 946 GB, NVMe M.2, / 'root'
Large data storage I: 3.6 TB, HDD-SATA, /mnt/storage_4tb
Large data storage II: 1.8 TB, HDD-SATA, /mnt/storage_2tb
Scratch workspace: 954 GB, NVMe M.2, /mnt/nvme_1tb_folder (Please use this dir to run tasks on GPUs)
Job submission: sbatch run.shell
Job queue: squeue
run.shell may read
#!/bin/bash
#SBATCH --job-name=test_two_gpus
#SBATCH --output=test_two_gpus_%j.out
#SBATCH --error=test_two_gpus_%j.err
#SBATCH --partition=main
#SBATCH --gres=gpu:2 (max 2)
#SBATCH --cpus-per-task=8 (max 112)
#SBATCH --mem=32G (max 128G)
#SBATCH --time=01:00:00 (max INFINITY)
#----* Feel free to add other SBATCH *----
echo "Running on host: $(hostname)"
echo "GPUs allocated:"
nvidia-smi
# -- * OPTIONAL * --
# Activate your virtual environment
# source /opt/anaconda3/lib/python3.13/site-packages/conda/shell/etc/profile.d/conda.sh
# conda activate torch_env_3.12.4
python small_net.py
Documentations: Slurm Workload Manager - Quick Start User Guide