explore-examples

name: explore-examples description: 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. metadata: short-description: Run bundled apeiron examples

Explore Examples

Run one of apeiron's bundled examples end to end.

Inputs

• Optional config path: if the user provides one, run that bundled config.
• If no config path is provided, discover the available configs and let the user choose.

Procedure

1. Discover Configs

Build the menu dynamically from the repo:

find examples -name "*.toml" -type f | sort

For each config, read enough TOML fields to summarize it:

• data.name
• model.name
• drift_detection.detector_name
• continual_learning.update_mode

Present a numbered menu and ask the user which config to run. Recommend MNIST for a first run when no preference is given.

2. Check Pretrained Weights

• MNIST is the expected low-friction path when examples/mnist/mnist.pth exists.
• For non-MNIST configs, read any configured pretrained_path.
• If the referenced file is missing, tell the user plainly that the run may train from scratch or fail to load weights, then ask whether to continue or switch configs.

3. Choose Logging Backend

Before running, ask which metrics backend to use and pass it as an override instead of editing the config:

• none: no account or network, best for local smoke runs.
• wandb: requires an authenticated Weights & Biases session.
• mlflow: uses MLflow tracking.

Default to none when the user asks for a quick local run.

4. Summarize And Run

Summarize the selected config: dataset, model, detector, updater, device, and batch size.

Run from the project root:

poetry run python -m src.main --config <config_path> --set logging.backend=<choice>

This is a real training and monitoring run. Stream output and do not silently background it.

5. Report Results

Summarize from the run output:

• whether drift was detected
• number of drift events when available
• final accuracy or final reported metric
• output CSV path from visualization.input

The package emits a CSV for inspection; it does not ship a built-in dashboard renderer.

Useful Commands

Quick local first run:

poetry run python -m src.main --config examples/mnist/mnist.toml --set logging.backend=none

Useful overrides:

--set drift_detection.detector_name=PageHinkleyDetector
--set continual_learning.update_mode=ewc_online
--set device=cpu

If Poetry is not set up, complete the repo's development install before running examples.