BaseSIM_APEIRON

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.

Install the apeiron continual-learning package into an existing Python project so `import apeiron` works. Use when the user wants to add apeiron to another project or training framework, set it up as a path or git dependency, or fix imports in an external codebase. Handles package-manager detection, Python compatibility checks, and CPU vs CUDA PyTorch selection. Do not use for developing inside the apeiron repo itself; that path is just the repo's normal development install.

Add apeiron drift-detection or continual-learning behavior to an existing training framework. Use when the user already has a training loop, such as vanilla PyTorch, Lightning, Hugging Face Trainer, or Accelerate, and wants to integrate apeiron rather than adopt apeiron's runner. Inspects the target repo, recommends the lightest viable integration path, writes adapter glue, and smoke-tests it. If `import apeiron` fails, use install-apeiron first.