▶️ AGILAB (Home dashboard)

AGILAB.py is the main Streamlit entry point. It provides a single navigation surface for the built-in pages (EDIT / EXECUTE / EXPERIMENT / EXPLORE) and for optional app-specific dashboards (“apps-pages”) that can be enabled per project.

Navigation model

• Built-in pages are always available and drive reproducible workflows:

  • EDIT: inspect and modify the project sources and settings.

  • EXECUTE: install workers, generate distributions, and run pipelines.

  • EXPERIMENT: iterate on analysis/code in lab_steps.toml and run snippets against exported datasets.

  • EXPLORE: browse and launch installed Streamlit bundles (apps-pages).

• Apps-pages are standalone Streamlit bundles discovered under ${AGILAB_PAGES_ABS}. They run in isolated virtual environments and are enabled by listing their module names under [pages].view_module in app_settings.toml.

Learning lifecycle (when applicable)

When a project includes learning components:

• Training updates model parameters using new experience.

• Inference runs a fixed checkpoint to produce allocations/decisions.

• Continuous learning and federated learning require explicit pipeline steps (checkpointing, dataset joins, aggregation); AGILAB provides the run orchestration and artifact conventions, but does not enable these modes implicitly.

See also

• AGILab Architecture for the end-to-end pipeline view.

• ▶️ EXECUTE and ▶️ EXPERIMENT for the default workflow.

• Learning Workflows for training vs inference, continuous learning, and federated learning patterns.