ml-test-driven-development

You can validate PyTorch model integration by running a synthetic batch through the forward pass to check tensor shapes at module boundaries and ensure all parameters receive non-zero gradients to catch detached tensors or broken loss hookups.

The overfit-one-batch test verifies that a model can memorize a single batch on a small configuration. It confirms that the loss function, label alignment, and optimizer steps are wired correctly before committing to a full training run.

To test custom loss functions and training loops, run focused checkpoints like a gradient smoke test on a dummy batch. This validates that gradients flow correctly and the loss hookups are properly configured within short CPU runtimes.

Zero gradients often indicate detached tensors, frozen layers, or broken loss hookups. Running a gradient smoke test on a dummy batch detects these wiring bugs early by verifying all model parameters receive non-zero gradients before long training runs.

Yes, these test-driven development checks validate model integration within short CPU runtimes. By running a shape gauntlet, gradient smoke test, and overfit-one-batch sequence, you detect wiring and integration bugs before starting costly full training runs.