GitHub - Francisco-Booth/ComputeFence: Pre-flight validation and real-time monitoring for HuggingFace fine-tuning runs. · GitHub
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Francisco-Booth
ComputeFence
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ComputeFence
Pre-flight validation for GPU training runs on rented infrastructure.<br>Built specifically for RunPod, Vast.ai, Lambda, and similar providers.
Install
pip install computefence
Usage
computefence doctor<br>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<br>CPU with no error. Class weights caused loss collapse. My dataset had 28,432<br>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.
About<br>Pre-flight validation and real-time monitoring for HuggingFace fine-tuning runs.<br>Resources<br>Readme<br>MIT license<br>Activity<br>Stars<br>0 stars<br>Watchers<br>0 watching<br>Forks<br>1 fork<br>Report repository
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