Euler GPU Track

Complete this track before regular GPU use on Euler. GPU resources are available only through shareholder accounts such as es_fuge; the public share has no GPUs.

Prerequisites#

• Passed Euler CPU Track
• Project code has a real GPU implementation
• Supervisor confirmation that GPU use is appropriate

Competencies#

You can:

• justify why a workload needs a GPU;
• request one RTX 4090 by default or one RTX 3090 as a general fallback through es_fuge;
• recognize when the two RTX PRO 6000 GPUs are appropriate and verify CUDA 13 compatibility before requesting pro_6000;
• select CPU, system memory, GPU count, and time intentionally;
• perform the smallest useful GPU smoke test;
• inspect GPU utilization and memory with nvidia-smi;
• recognize CPU/data-loader and I/O bottlenecks;
• checkpoint work before the wall-time limit;
• use euler-tunnel for short interactive debugging and sbatch for training;
• establish a one-GPU baseline before requesting multiple GPUs;
• cap GPU arrays so they cannot consume the full shared allowance accidentally.

Required Learning#

1. Read IDEAL Lab Euler share policy.
2. Read Slurm GPU jobs.
3. Read Euler tunnel.
4. Complete GPU resource review.

Evidence#

• A reviewed one-GPU Slurm script using es_fuge.
• A reasoned RTX 4090 default, RTX 3090 fallback, and explanation of why RTX PRO 6000 is or is not required.
• A monitoring plan for GPU utilization, GPU memory, CPU, and system memory.
• A checkpoint/recovery plan for training jobs.
• A multi-GPU scaling criterion, even when only one GPU is currently requested.

A live GPU job is not required for the initial review exercise. If the project requires a smoke test, keep it short and obtain supervisor approval.

Understand Before Accepting AI Output#

• The program executes GPU operations rather than only detecting CUDA.
• I can explain every requested CPU, memory, GPU, and time value.
• I know why adding another GPU may not improve a CPU-bound pipeline.
• I will move long or unattended work from a tunnel to sbatch.

Pass Criteria#

The student corrects unsafe GPU scenarios and explains a measurement-driven plan. Multi-GPU access is not justified solely by dataset or model size.