Example Gallery¶

Examples are grouped into two tiers:

• Essentials: onboarding notebooks/scripts using the preferred execution path (Tracker.run, Tracker.trace, scenario)
• Advanced: larger workflows and integration templates that still use the same execution patterns, but add scale and system complexity

Essentials¶

Example	Quick Links	Key Learning Outcomes
00 Quickstart A 5-minute introduction to the core API.	📖 View on GitHub	• Create a tracked run • Observe cache hits on repeated runs • Query run history
01 BEAM CORE Demo A simple linear scenario workflow.	📖 View on GitHub	• Linear multi-step scenario • Scenario diffs and lineage • Path-bound input staging
02 Monte Carlo Sweeps Parallel scenario.map_runs(...) sweep with a wide lineage tree.	📖 View on GitHub	• Parallel parameter sweeps • Parent scenario with many child runs • Hybrid query workflow
Artifact Recovery Script One-artifact archive and workspace restore.	📖 View on GitHub	• Archive one artifact • Recover bytes from recovery_roots • Use materialize_artifact(...)

Advanced¶

Example	Quick Links	Key Learning Outcomes
03 Iterative Workflows Scenario workflows with feedback loops.	📖 View on GitHub	• Iterative scenario loops • Cache hydration choices across iterations • Provenance queries for extension runs
04 Demand Modeling End-to-end transportation simulation.	📖 View on GitHub	• Multi-step model pipelines • Scenario comparison and lineage tracing • Matrix-style downstream analysis
05 GTFS Support Raw transit feeds, selected-service identity, and typed GTFS views.	📖 View on GitHub	• Discover raw GTFS feed members • Compare weekday/weekend service slices • Track model lineage from selected GTFS tables

For non-preferred lifecycle/decorator APIs, see Advanced Usage, especially Manual Lifecycle and Decorators.

Installation¶

pip install consist

If you are running notebooks and need notebook tooling:

pip install "consist[examples]"

For notebook/module layout details, see examples/README.md.