Francisco-Booth/ComputeFence
Pre-flight validation and real-time monitoring for HuggingFace fine-tuning runs. - Francisco-Booth/ComputeFence
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README
ComputeFence Pre-flight validation for GPU training runs on rented infrastructure. Built specifically for RunPod, Vast.ai, Lambda, and similar providers. Install pip install computefence Usage computefence doctor computefence doctor --dataset train.csv What it checks
CUDA and GPU availability HuggingFace cache volume path (catches the RunPod /root vs /workspace conflict) Accelerate GPU count vs config Dataset duplicates and missing values
What it does not yet check
Training script correctness Model architecture compatibility Learning rate or hyperparameter safety Runtime monitoring during the job
Why this exists I burned ~£1,000 on GPU training runs that failed silently. CUDA fell back to CPU with no error. Class weights caused loss collapse. My dataset had 28,432 duplicate rows and 312 conflicting labels I only found during the rebuild. Nothing existed that caught these before the job started. So I built it.