# Tutorials Tutorials are paced learning sessions. They assume you have already read Quickstart so that installation and stage names are familiar. Start with the short bake-off path, then read the report, then open the detailed notebooks when you need stage-level logs and debugging patterns. Complete Tutorials after Quickstart. Use Guides when you later have a specific job such as bringing your own traces or building a custom classifier. Use the API Reference when you need exact names. ```{toctree} :maxdepth: 1 :caption: Getting started first_zoo_run read_the_report ``` ```{toctree} :maxdepth: 1 :caption: Detailed notebooks stage_data stage_classify stage_hardware_report debugging_pipeline ``` ## Getting started 1. **First zoo run** installs the extras used by the public multi-method benchmark, links the paper data the bundled configs expect, and launches ``configs/paper_all_methods_benchmark.yaml`` from the command line or from Python. 2. **Read the report** opens the summary PDF or HTML and the companion JSON, then explains joint accuracy, F5Q, PQ min, analytical hardware columns, and the recommendation box so you can defend a ranking in a lab meeting. ## Detailed notebooks These Jupyter notebooks live under ``doc/tutorials/`` and are rendered in the HTML manual without re-executing during the docs build. Run them locally from the repository root when you want live logs. | Notebook | Focus | |----------|-------| | {doc}`stage_data` | Stage 1 data load, demodulation, splits, caches, and data-stage log lines | | {doc}`stage_classify` | Feature kinds, Stage 2 classify banners, isolating one method | | {doc}`stage_hardware_report` | Analytical hardware estimation and summary report outputs | | {doc}`debugging_pipeline` | Log levels, validation, minimal reproductions, symptom → fix map | If a tutorial step fails because an optional dependency is missing, return to {doc}`../quickstart/installation` and install the matching extra before retrying.