24 seconds per iteration instead of 0.4. I paid for six hours of GPU compute and trained on CPU the entire time.

Description and use

ComputeFence detects pre‑training configuration errors, preventing silent GPU fallback and costly misconfigurations.

Problem

Training jobs on GPU providers silently fall back to CPU or misconfigure resources, causing wasted compute time and unexpected billing.

Outcome

ComputeFence identified and prevented silent GPU fallback, cache path misconfiguration, and Accelerate mismatches, reducing wasted GPU billing and improving training reliability.

Workflow

• Install ComputeFence via pip or uvx
• Run computefence doctor before launching training
• Review warnings and blockers
• Fix reported issues (e.g., set HF_HOME, adjust Accelerate config, free disk space)
• Proceed with training (e.g., python train.py)

Evidence

ComputeFence v0.2.5 — Pre-flight diagnostic

Source

Three operators have run it before paid training jobs on RunPod.

Source