michelangelo-examples 0.0.1

Pip-installable, ready-to-run example models and pipelines for Michelangelo

michelangelo-examples

Pip-installable, ready-to-run example models and pipelines for Michelangelo — a lightweight on-ramp for trying Michelangelo without a full monorepo checkout, the heavyweight example extra, or a running sandbox.

pip install michelangelo-examples[<example-name>]

Why this repo

The full example pipelines in michelangelo/python/examples/ are the canonical, full-fidelity reference — they exercise real Cadence/Temporal orchestration, Spark, and a live sandbox, and they aren't going anywhere. But trying one today means checking out the whole monorepo and installing one heavyweight extra that bundles every example's dependencies (torch, transformers, xgboost, pyspark, ray, mlflow, peft, and more) at once, regardless of which example you actually want.

michelangelo-examples is a second, lighter tier: each example is its own pip extra with only the dependencies it needs, plus a plain-Python run_local.py entrypoint that trains/evaluates locally — no Cadence, no Spark, no sandbox. It's the "try it in five minutes" path underneath the full pipeline reference, not a replacement for it.

Examples

(Coming soon — v1 will port a small initial set, starting with california-housing, movielens, and bert-cola. See the project spec for the full list this repo is drawing from.)

Each example will live under examples/<example-name>/ with its own README.md, model.py (or an import from michelangelo-models where applicable), and run_local.py.

Installing an example

pip install michelangelo-examples[california-housing]
python -m michelangelo_examples.california_housing.run_local

Only that example's dependencies are installed.

Relationship to other Michelangelo repos

• michelangelo — the core platform; python/examples/ there remains the full pipeline reference (Cadence/Temporal/Spark/sandbox).
• michelangelo-models — pluggable model architecture definitions. Some examples here may import a seed architecture from there instead of defining their own model.

Development

This repo uses uv for dependency management.

uv build
uv run pytest

License

Apache License 2.0