mlplant-cli 0.2.0

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

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Installation

pip install mlplant-cli

How to use

1. Annotate your notebook

Place an annotation at the top of each cell to mark it as a pipeline step. Four syntax styles are supported — pick whichever fits your workflow:

Function call (recommended)

Valid before any code, reads like a registration call, no errors or warnings:

import mlplant

mlplant.config()
LEARNING_RATE = 0.01
N_ESTIMATORS = 100

mlplant.features()
X["new_feature"] = X["a"] / X["b"]

Decorator

Clean decorator syntax, but only valid immediately before a def or class:

@mlplant.train
def _():
    model.fit(X_train, y_train)

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

Expression

Minimal form — attribute access with no call. Valid anywhere, but Pylance may show an "expression value is unused" hint in some configurations:

mlplant.load_data
df = pd.read_csv("data/train.csv")

Comment

Never causes any error or warning, useful when you want zero runtime footprint:

# @mlplant.preprocessing
df = df.dropna()
df["age"] = df["age"].clip(0, 120)

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All styles also accept PascalCase aliases (Config, LoadData, Train, etc.):

mlplant.LoadData()        # same as mlplant.load_data()
# @Config                 # same as # @mlplant.config
@mlplant.Train            # same as @mlplant.train (before a function)

Cells with the same annotation 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

By default, build now also runs train_pipeline.py in the generated project, so the API starts with ready-to-serve artifacts in artifacts/.

Data files can stay at the project root (for example data/train.csv). During auto-training, mlplant keeps reading data from the notebook/project root, but all generated pipeline outputs are written under the selected output folder.

With --mlflow, if a remote tracking server is unavailable, mlplant automatically falls back to local SQLite tracking (no server required), stored in output/artifacts/mlflow.db.

To generate files only (without training), use:

mlplant build my_notebook.ipynb --output ./api --no-train

3. Inspect detected steps

mlplant inspect my_notebook.ipynb

# or
python -m mlplant inspect my_notebook.ipynb

Supported annotations

Annotation	PascalCase alias	Generated file	Purpose
mlplant.config()	mlplant.Config()	src/config.py	Hyperparameters and constants
mlplant.load_data()	mlplant.LoadData()	src/data_loader.py	Data ingestion
mlplant.preprocessing()	mlplant.Preprocessing()	src/preprocessing.py	Cleaning and null handling
mlplant.features()	mlplant.Features()	src/features.py	Feature engineering
mlplant.train()	mlplant.Train()	src/trainer.py	Model training
mlplant.evaluate()	mlplant.Evaluate()	src/evaluate.py	Metrics and evaluation
mlplant.artifacts()	mlplant.Artifacts()	src/artifacts.py	Model serialization
mlplant.predict()	mlplant.Predict()	src/predict.py	Inference (exposed at /predict)

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
  --ui            Generate React frontend scaffold for inference
  --train         Run generated train pipeline after build (default)
  --no-train      Skip training during build

Output layout after build

Generated code is placed in the output folder (./output by default):

• output/main.py
• output/src/*
• output/train_pipeline.py
• output/requirements.txt
• output/predict_request_example.json (payload blueprint for /predict)
• output/Dockerfile (when --docker is enabled)
• output/ui/* (when --ui is enabled)

Pipeline runtime artifacts are also kept in the output folder:

• output/artifacts/models/*.joblib
• output/artifacts/metadata.json
• output/artifacts/label_encoder.joblib (when available)
• output/artifacts/mlflow.db (local MLflow SQLite tracking)
• output/artifacts/catboost_info/ (CatBoost auxiliary outputs)

When --ui is enabled:

cd output/ui
npm install
npm run dev

The frontend dynamically reads your API OpenAPI schema (/openapi.json) and builds the predict form from the generated InputData contract.