A pytest plugin for the tests every model and training loop needs: inference contracts, invariance, deployment parity, checkpoint resume, gradient accumulation, dataset metrics with confidence intervals, and zero-code testing of any model.
Build custom objectives with tensor operations, reusable modules, quantile regression, composite losses, and gradient checks.
A tour of every documented core PyTorch loss, with equations, input conventions, examples, and practical advice.
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