Modes overview

Modes are the steps in a sleepKIT task: acquire data, generate features, train, evaluate, export, and run a demo. Select a mode with --mode and provide the task configuration with --config.

Choose a mode

Mode	Purpose	Guide
download	Fetch the datasets selected in your configuration.	Download datasets
feature	Generate features from your datasets.	Feature generation
train	Train the task model with the configured features and settings.	Train a model
evaluate	Evaluate the model on the configured test data.	Evaluate a model
export	Convert the trained model for deployment.	Export a model
demo	Run a task-level demonstration with the configured backend.	Run a demo

Run a step

Train a staging model

sleepkit --mode train --task stage --config configuration.json

Use the same configuration for related steps so that dataset, feature and model settings stay consistent. Complete data download and feature generation before training; evaluate the trained model before exporting it.

Demo support depends on the task and backend. See the demo guide for setup and limitations; running a demo does not establish model accuracy.

Python workflow

The Python interface exposes the same task operations for scripts and notebooks. See Quickstart for a configuration-driven example and Configuration for parameter definitions.