Metadata-Version: 2.4
Name: create-ml-stack
Version: 0.0.1
Summary: Better-T-Stack for ML — scaffold end-to-end runnable ML projects
Project-URL: Homepage, https://github.com/Prabhpuran/create-ml-stack
Project-URL: Documentation, https://github.com/Prabhpuran/create-ml-stack/tree/main/docs
Project-URL: Repository, https://github.com/Prabhpuran/create-ml-stack
Project-URL: Issues, https://github.com/Prabhpuran/create-ml-stack/issues
Author: create-ml-stack contributors
License: MIT
License-File: LICENSE
Keywords: cli,jax,ml,pytorch,scaffold,sklearn
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: click>=8.1
Requires-Dist: jinja2>=3.1
Requires-Dist: pyyaml>=6.0
Requires-Dist: questionary>=2.0
Requires-Dist: rich>=13.0
Requires-Dist: tomli-w>=1.0
Provides-Extra: dev
Requires-Dist: mypy>=1.11; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.6; extra == 'dev'
Requires-Dist: types-pyyaml>=6.0; extra == 'dev'
Description-Content-Type: text/markdown

# create-ml-stack

**Better-T-Stack for ML** — scaffold end-to-end runnable machine learning projects in seconds.

```bash
uvx create-ml-stack my-ml-app
# or: pipx run create-ml-stack my-ml-app
```

Choose a framework (PyTorch, JAX, scikit-learn, HF Transformers), tracking, data versioning, config, and serving — then get a project that trains, tracks, and serves on the first try.

## Quick start

```bash
uvx create-ml-stack my-ml-app
cd my-ml-app
uv sync --python 3.11   # or: conda env create -f environment.yml / pip install -r requirements.txt
uv run train --fast     # tiny seed data, <90s on CPU
uv run serve --dry-run  # load model without binding a port
uv run serve            # start local serving helper
```

## Features

- **Multi-framework**: PyTorch, JAX, scikit-learn, Hugging Face Transformers
- **Integrations**: W&B / MLflow, DVC, Hydra (+ Optuna), BentoML / Modal / HF Spaces
- **Package managers**: uv (recommended), conda, or pip
- **Layouts**: monorepo (`packages/`) or single-package (`src/`)
- **Hybrid compatibility matrix**: curated fast path + on-the-fly resolver fallback
- **Offline-first**: vendored tiny MNIST/digits shards for happy-path training

## Non-interactive flags

```bash
create-ml-stack my-app \
  --framework torch \
  --tracking wandb \
  --data dvc \
  --config hydra \
  --serving bentoml \
  --pm uv \
  --layout monorepo \
  --yes
```

## Development

```bash
uv sync --extra dev
uv run pytest -q
uv run ruff check .
uv build   # verify wheel before release
```

See [docs/publishing.md](docs/publishing.md) for PyPI release steps.

## License

MIT
