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

Four problems plagued the training job, costing six hours of GPU compute while running on CPU. The first issue was CUDA silently switching to CPU, causing iteration times to lengthen to 24 seconds instead of the usual 0.4 seconds. PyTorch produced no logs to alert the user. The second issue was that every fresh pod re-downloaded base model weights to an ephemeral disk, which was wiped upon pod shutdown, causing the same download and cost for every run.

The third issue was an accelerate config mismatch, where the num_processes was set to 2, but the pod only had one GPU. Training launched, appeared to run, and produced incorrect results without any error thrown. The fourth issue was a dirty dataset containing 28,432 duplicate rows and 312 conflicting labels. The loss collapsed to 0.693 on step one, the exact cross-entropy value for random guessing on a binary classification problem.

None of these issues printed an exception, and they were all visible before running python train.py if the user knew what to check. Many ML engineers reported losing checkpoints to ephemeral disk and having silent garbage output with no log errors. To address these issues, ComputeFence was built. It is a pre-flight diagnostic tool that checks GPU and CUDA visibility, HuggingFace cache path persistence, Accelerate GPU count versus the actual hardware, disk headroom for checkpoints, and checkpoint output directory persistence.

ComputeFence does not check the training script correctness or learning rate safety, as those failures only occur during the run. Add ComputeFence to your pod startup script to catch configuration mistakes before the GPU starts billing. The tool is free and MIT licensed, working on any bare metal GPU provider.