Help the user pick a drift detector and tune its settings for an apeiron run. Use when the user asks which detector to use, how to configure drift detection, what ADWIN/KSWIN/PageHinkley/threshold values to set, or wants a ready-to-use [drift_detection] config block. Asks a few questions about the monitored metric and drift shape, recommends a detector, writes a filled-in TOML block, and validates that it loads. Does NOT run a full experiment — for that use explore-examples or custom-experiment.
Help the user pick a drift detector and tune its settings for an apeiron run. Use when the user asks which detector to use, how to configure drift detection, what ADWIN/KSWIN/PageHinkley/threshold values to set, or wants a ready-to-use [drift_detection] config block. Asks a few questions about the monitored metric and drift shape, recommends a detector, writes a filled-in TOML block, and validates that it loads. Does not run a full experiment; for that use explore-examples or custom-experiment.
Run an apeiron experiment on the user's OWN dataset and model end-to-end. Use when the user wants to bring their own data + architecture (beyond the shipped MNIST/CIFAR examples), scaffold a custom model harness, write a config for it, smoke-test it, and run the full experiment. Self-contained: it creates the harness, data utilities, and TOML, registers them in the example factory, and runs. For trying the bundled examples instead, use explore-examples; for adding apeiron to a separate project's training loop, use integrate-apeiron.
Run a bundled apeiron example experiment to explore the framework's capabilities. Use when the user wants to try the software, run a default/demo experiment, see drift detection and continual learning in action, or pick from the shipped MNIST/CIFAR configs. Presents a menu of available example configs, runs the chosen one, and reports where the metrics CSV landed. For running the user's OWN data/model/config, use the custom-experiment skill instead.
Install the apeiron continual-learning package as a dependency into an existing Python project so the user can `import apeiron`. Use when the user wants to add apeiron to their own project or training framework, set it up as a path/git dependency, or get `from apeiron import ...` working in another codebase. Handles Poetry presence, Python 3.13 verification, and automatic GPU-vs-CPU PyTorch selection. SKIP for developing inside THIS repo itself — that is just `poetry install`.
Add apeiron's continual-learning / drift-detection capabilities to a user's EXISTING training framework. Use when the user already has their own training loop (vanilla PyTorch, Lightning, HF Trainer, etc.) and wants to bolt on drift detection and/or CL adaptation rather than adopt apeiron's runner. Inspects the user's repo, recommends the lightest viable integration path, writes the adapter glue into their repo, and smoke-tests it. Assumes apeiron is importable (`import apeiron`) — if not, run install-apeiron first. For a self-contained apeiron run on custom data, use custom-experiment instead.
Scaffold and run an apeiron experiment for the user's own dataset and model. Use when the user wants to bring custom data or architecture beyond shipped examples, create a model harness, write a config, register it in the example factory, smoke-test it, and run the full experiment. For bundled demos, use explore-examples. For integrating apeiron into an existing external training loop, use integrate-apeiron.
Run a bundled apeiron example experiment to explore the framework. Use when the user wants to try apeiron, run a default or demo experiment, see drift detection and continual learning behavior, or choose from shipped MNIST/CIFAR configs. Presents available example configs, runs the chosen one, and reports the metrics output. For the user's own data and model, use custom-experiment instead.