mlplant-cli 0.1.2

mlplant is an MLOps framework that converts exploratory Jupyter notebooks into production-ready FastAPI projects using in-cell annotations.

Installation

pip install mlplant-cli

How to use

1. Annotate your notebook

Use Python decorators directly on functions inside your notebook cells:

import mlplant

@mlplant.config
def _():
    LEARNING_RATE = 0.01
    N_ESTIMATORS = 100

@mlplant.features
def _():
    X["new_feature"] = X["a"] / X["b"]

@mlplant.predict
def predict(data: dict):
    return model.predict([list(data.values())])[0]

Cells with the same decorator are automatically merged into a single output file. Visual noise (matplotlib, seaborn, print, display) is stripped automatically.

2. Build the project

# as a direct command (after installation)
mlplant build my_notebook.ipynb --output ./api --port 8080 --title "My API" --docker

# or via python -m (works anywhere)
python -m mlplant build my_notebook.ipynb --output ./api --docker

3. Inspect detected steps

mlplant inspect my_notebook.ipynb

# or
python -m mlplant inspect my_notebook.ipynb

Supported decorators

Build flags

mlplant build <notebook> [OPTIONS]

  --output / -o   Output directory             (default: ./output)
  --port          Server port                  (default: 8000)
  --title         Swagger UI title             (default: "mlplant API")
  --workers       Uvicorn workers              (default: 1)
  --docker        Generate optimized Dockerfile
  --mlflow        Inject MLflow tracking into training