GradGuard

GradGuard is an AI-focused web app (with optional desktop agent) that monitors deep learning training runs and flags “quiet failures” like dead gradients, data leakage, label noise spikes, exploding/vanishing activations, and overfitting that looks like progress. It plugs into PyTorch/Lightning and popular experiment trackers, then runs lightweight diagnostics on logs, metrics, and sampled tensors to explain what’s going wrong in plain language and suggest concrete fixes (LR schedules, normalization checks, batch composition, augmentation sanity tests). The honest reality: most teams burn expensive GPU time due to basic issues that aren’t obvious until days later. GradGuard aims to shorten that feedback loop. It won’t magically improve your model; it will reduce wasted compute and help engineers debug faster. Pricing can be per-seat plus usage-based for large orgs, with a free tier for small projects.