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.

To install the latest release version use

pip install torchdrift

To get the latest and greatest install from git with

pip install git+https://github.com/torchdrift/torchdrift/

Our documentation is at TorchDrift.org.

Check out our worked example with an ImageNet-type classifier.

If you have a model (without head) as the feature extractor and a training dataloader you can fit the reference distribution as

drift_detector = torchdrift.detectors.KernelMMDDriftDetector()
torchdrift.utils.fit(train_dataloader, feature_extractor, drift_detector)

and then check drifts with

features = feature_extractor(inputs)
score = drift_detector(features)
p_val = drift_detector.compute_p_value(features)

if p_val < 0.01:
    raise RuntimeError("Drifted Inputs")

Also check out our deployment example for integration of TorchDrift into inference with a model.

TorchDrift is a joint project of Orobix Srl, Bergamo, Italy and MathInf GmbH, Garching b. München, Germany.

The TorchDrift Team: Thomas Viehmann, Luca Antiga, Daniele Cortinovis, Lisa Lozza

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/