| General QuickStart Guide for ASPIRE 2A: https://help.nscc.sg/wp-content/uploads/2024/05/ASPIRE2A-General-Quickstart-Guide.pdf |
| General QuickStart Guide for ASPIRE 2A+: https://help.nscc.sg/wp-content/uploads/ASPIRE2A_Plus_General_Quick_Start_Guide.pdf |
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| GPU Server Access and Job Submission Guide |
| This guide explains how to connect to the H100 GPU server through VS Code Remote SSH and how to submit GPU jobs using qsub. |
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| 1. SSH Access Path |
| The connection path is: |
| Local PC → Mac mini → GPU server → H100 GPU server |
| Manual SSH steps: |
| ssh -X ecteam@eyecodegpus.ddns.net |
| ssh -X minh@gpuserver3 |
| ssh smrvmdo@aspire2p.nus.edu.sg |
| Do not store passwords inside this README or inside SSH config files. |
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| 2. VS Code Remote SSH Setup |
| Open your SSH config file: |
| code ~/.ssh/config |
| Add the following configuration: |
| Host mac-mini |
| HostName eyecodegpus.ddns.net |
| User ecteam |
| ForwardX11 yes |
| ServerAliveInterval 60 |
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| Host gpuserver3 |
| HostName gpuserver3 |
| User minh |
| ProxyJump mac-mini |
| ForwardX11 yes |
| ServerAliveInterval 60 |
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| Host h100 |
| HostName aspire2p.nus.edu.sg |
| User smrvmdo |
| ProxyJump gpuserver3 |
| ServerAliveInterval 60 |
| Then connect in VS Code: |
| Remote-SSH: Connect to Host... |
| Choose: |
| h100 |
| VS Code will connect through: |
| mac-mini → gpuserver3 → h100 |
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| 3. Test SSH Connection from Terminal |
| Before using VS Code, test the connection from terminal: |
| ssh -J ecteam@eyecodegpus.ddns.net,minh@gpuserver3 smrvmdo@aspire2p.nus.edu.sg |
| If this works, VS Code Remote SSH should also work. |
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| 4. Request Interactive GPU Session |
| To request an interactive session with 4 GPUs: |
| qsub -I -q normal -P 11714283 -l select=1:ncpus=56:ngpus=4 -l walltime=12:00:00 |
| For 2 GPUs: |
| qsub -I -q normal -P 11714283 -l select=1:ncpus=28:ngpus=2 -l walltime=12:00:00 |
| After the interactive job starts, you can run commands directly on the allocated compute node. |
| Example: |
| cd /path/to/project |
| conda activate your_env |
| python train.py |
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| 5. Submit a Batch Job |
| Create a job script, for example: |
| nano run_job.pbs |
| Example run_job.pbs: |
| #!/bin/bash |
| #PBS -N my_gpu_job |
| #PBS -q normal |
| #PBS -P 11714283 |
| #PBS -l select=1:ncpus=56:ngpus=4 |
| #PBS -l walltime=12:00:00 |
| #PBS -j oe |
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| cd $PBS_O_WORKDIR |
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| source ~/.bashrc |
| conda activate your_env |
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| python train.py |
| Submit the job: |
| qsub run_job.pbs |
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| 6. Useful PBS Commands |
| Check your running jobs: |
| qstat -u $USER |
| Check all jobs: |
| qstat |
| Delete/cancel a job: |
| qdel <job_id> |
| Example: |
| qdel 123456 |
| Check job output after it finishes: |
| ls |
| cat my_gpu_job.o* |
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| 7. Example Training Job |
| Example project structure: |
| project/ |
| ├── train.py |
| ├── run_job.pbs |
| └── README.md |
| Example run_job.pbs: |
| #!/bin/bash |
| #PBS -N train_yolo |
| #PBS -q normal |
| #PBS -P 11714283 |
| #PBS -l select=1:ncpus=56:ngpus=4 |
| #PBS -l walltime=12:00:00 |
| #PBS -j oe |
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| cd $PBS_O_WORKDIR |
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| source ~/.bashrc |
| conda activate yolo_env |
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| python train.py --batch 64 --epochs 100 |
| Submit: |
| qsub run_job.pbs |
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| 8. Notes |
| Use qsub -I only when you need an interactive terminal session. |
| Use qsub run_job.pbs when you want to submit a training script and let it run in the queue. |
| Do not run heavy GPU training directly on the login node. |
| Do not put passwords in ~/.ssh/config. |
| If VS Code SSH fails, first test the SSH command in terminal. |
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