Saving and Loading

Alibi Detect includes support for saving and loading detectors to disk. To save a detector, simply call the save_detector method and provide a path to a directory (a new one will be created if it doesn’t exist):

from alibi_detect.od import OutlierVAE
from alibi_detect.saving import save_detector

od = OutlierVAE(...) 

filepath = './my_detector/'
save_detector(od, filepath)

To load a previously saved detector, use the load_detector method and provide it with the path to the detector’s directory:

from alibi_detect.saving import load_detector

filepath = './my_detector/'
od = load_detector(filepath)


When loading a saved detector, a warning will be issued if the runtime alibi-detect version is different from the version used to save the detector. It is highly recommended to use the same alibi-detect, Python and dependency versions as were used to save the detector to avoid potential bugs and incompatibilities.


Detectors can be saved using two formats:

  • Config format: For drift detectors, by default save_detector serializes the detector via a config file named config.toml, stored in filepath. The TOML format is human-readable, which makes the config files useful for record keeping, and allows a detector to be edited before it is reloaded. For more details, see Detector Configuration Files.

  • Legacy format: Outlier and adversarial detectors are saved to dill files stored within filepath. Drift detectors can also be saved in this legacy format by running save_detector with legacy=True. Loading is performed in the same way, by simply running load_detector(filepath).

Supported detectors

The following tables list the current state of save/load support for each detector. Adding full support for the remaining detectors is in the Roadmap.


Legacy save/load

Config save/load

Isolation Forest

Mahalanobis Distance





Likelihood Ratios


Spectral Residual



Legacy save/load

Config save/load

Adversarial AE

Model distillation


Saving/loading of detectors using PyTorch models and/or a PyTorch backend is currently not supported.