TorchDrift: drift detection for PyTorch¶
TorchDrift is a data and concept drift library for PyTorch. It lets you monitor your PyTorch models to see if they operate within spec.
We focus on practical application and strive to seamlessly integrate with PyTorch.
- Practical drift detection
- Comparing drift detectors
- Intuition for the Maximum Mean Discrepancy two-sample test
We were inspired by
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift, NeurIPS 2019 https://github.com/steverab/failing-loudly
Hendrycks & Dietterich: Benchmarking Neural Network Robustness to Common Corruptions and Perturbations ICLR 2019 https://github.com/hendrycks/robustness/
Van Looveren et al.: Alibi Detect https://github.com/SeldonIO/alibi-detect/