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open-edge-platformopen-edge-platform/training_extensions/skills/getitune/geti-library-dev/SKILL.md

geti-library-dev

Skill1.4k starsChanged 17 days agoApache-2.0View on GitHub

Develop and validate changes in `libraries/getitune/` for the `getitune` Python package. Use when changing library source or tests as well as packaging, recipes and model manifests. This includes Python APIs and CLI behavior across training and export. Covers environment setup; accelerator extras; multi-backend architecture; model and recipe additions; focused checks.

1 other project uses a version of this.

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Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
Sep 2026Oct 2026Nov 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

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The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 2 are distinctive, and 7 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Create or refresh the environment with just venv --device cpu for routine work.

    line 14
  2. Very specific:

    Run just lint before wider test runs.

    line 16
  3. Distinctive:

    Run the smallest relevant checks first and widen only if the changed behavior crosses package or task boundaries.

    line 43
  4. Very specific:

    Use just lint for formatting, lint, and type issues.

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  5. Very specific:

    Use just test-unit -- tests/unit/...

    line 48
  6. Very specific:

    Use the backend-scoped recipes for model code: just test-unit-lightning -- <pytest args>, just test-unit-ultralytics -- <pytest args>, or …

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  7. Very specific:

    Use just test-integration -- <pytest args> only when the change affects end-to-end training, export, or integration behavior.

    line 51
  8. Distinctive:

    Update docs or examples when public library behavior changes.

    line 56
  9. Very specific:

    Prefer project just targets over ad hoc dependency-install commands so the pinned uv workflow stays consistent with CI.

    line 57

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Geti Library Development

For the full architecture reference (package layout, multi-backend design, how to add models, recipes, and manifests) read libraries/getitune/AGENTS.md.

Quick Start

  • Work from libraries/getitune/.
  • Create or refresh the environment with just venv --device cpu for routine work.
  • Switch to just venv --device cuda or just venv --device xpu only when the task needs accelerator-specific behavior.
  • Run just lint before wider test runs.

Architecture Essentials

  • Source lives under src/getitune/. Public API entry points must stay stable because application/backend/ consumes them.
  • Multi-backend design: compute backends live under src/getitune/backend/ — lightning/ (PyTorch Lightning training), openvino/ (inference-only), and optional ultralytics/. src/getitune/models/ re-exports model classes from each backend into one namespace.
  • Device abstraction: DeviceType (src/getitune/types/device.py) and DeviceConfig (src/getitune/config/device.py) abstract accelerator choice. Guard device-specific (CUDA vs XPU) code with capability checks in src/getitune/utils/device.py — never at import time.
  • Recipes: YAML configs under src/getitune/recipe/<task>/<model>.yaml bind a task + model class_path + training config. Recipes are self-discovering via src/getitune/utils/recipes.py (list_models) — no registry to update.
  • Adding a model: implement the model class under src/getitune/backend/lightning/models/<task>/, inheriting the task base class (ultimately LightningModel); export it from the task __init__.py; add a recipe YAML. See libraries/getitune/AGENTS.md for the full walkthrough.

Workflow

  1. Confirm the change belongs in libraries/getitune/. If the task is mainly FastAPI or React work, switch to the matching backend or UI skill.
  2. Inspect the nearest module and tests before editing. Keep changes inside the existing package boundaries under src/getitune/.
  3. Make the smallest change that resolves the task. Avoid lockfile churn unless dependencies changed intentionally.
  4. Run the smallest relevant checks first and widen only if the changed behavior crosses package or task boundaries.

Verification

  • Use just lint for formatting, lint, and type issues.
  • Use just test-unit -- tests/unit/... or just test-unit -- -k <expr> for normal Python behavior changes.
  • Use the backend-scoped recipes for model code: just test-unit-lightning -- <pytest args>, just test-unit-ultralytics -- <pytest args>, or just test-unit-openvino -- <pytest args>.
  • Use just test-integration -- <pytest args> only when the change affects end-to-end training, export, or integration behavior.

Coordination Notes

  • application/backend consumes ../../libraries/getitune as a local editable dependency. If the change affects shared runtime behavior, validate the backend too.
  • Update docs or examples when public library behavior changes.
  • Prefer project just targets over ad hoc dependency-install commands so the pinned uv workflow stays consistent with CI.

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