mlflow-autogluon 0.1.0

MLflow community flavor and autologging for AutoGluon predictors

mlflow-autogluon

MLflow community model flavor and autologging for AutoGluon predictors.

Documentation: phylatech.github.io/mlflow-autogluon

AutoGluon has no built-in MLflow flavor, and the MLflow maintainers have asked for this integration to live as a community flavor (see mlflow/mlflow#13214 and autogluon/autogluon#1404). This package provides that integration:

• Model flavor: save_model, log_model, and load_model for TabularPredictor, TimeSeriesPredictor, and MultiModalPredictor, with full round-trip fidelity.
• PyFunc support: logged models load with mlflow.pyfunc.load_model and serve with mlflow models serve, including predict_proba via inference params and long-format DataFrame forecasting for timeseries models.
• Autologging: one call to mlflow_autogluon.autolog() records params, leaderboard metrics, artifacts, and the fitted predictor for every fit call, across all installed predictor types.

Installation

pip install mlflow-autogluon[tabular]

Autologging quickstart

import mlflow_autogluon
from autogluon.tabular import TabularPredictor

mlflow_autogluon.autolog()

predictor = TabularPredictor(label="target").fit(train_data, presets="medium_quality")

That single autolog() call gives you, per fit:

What	Examples
Params	label, problem_type, eval_metric, presets, time_limit, hyperparameters, train_rows
Metrics	best_model_score_val, fit_time_seconds, per-model score_val_* / fit_time_*
Tags	estimator_name, autogluon_version, best_model, problem_type
Artifacts	leaderboard.csv, optional fit_summary.json, the fitted model

Because this is a community flavor, mlflow.autolog() does not enable it automatically; call mlflow_autogluon.autolog() explicitly. Options:

mlflow_autogluon.autolog(
    log_models=True,             # log the fitted predictor as an MLflow model
    log_leaderboard=True,        # leaderboard CSV artifact + per-model metrics
    log_fit_summary=False,       # fit_summary() as a JSON artifact
    registered_model_name=None,  # also register logged models under this name
    extra_tags=None,             # extra tags applied to autologged runs
    disable=False,               # turn the integration off
)

Manual logging

import mlflow
import mlflow_autogluon

with mlflow.start_run():
    model_info = mlflow_autogluon.log_model(
        ag_model=predictor,
        artifact_path="model",
        input_example=train_data.drop(columns=["target"]).head(),
    )

# Load back the native predictor
predictor = mlflow_autogluon.load_model(model_info.model_uri)

# Or load as a generic pyfunc for serving
pyfunc_model = mlflow.pyfunc.load_model(model_info.model_uri)
predictions = pyfunc_model.predict(test_data)
probabilities = pyfunc_model.predict(test_data, params={"predict_method": "predict_proba"})

Serving works out of the box:

mlflow models serve -m "models:/<name>/<version>" -p 5001

Compatibility

• Python >= 3.9 (CI: 3.10, 3.11, 3.12)
• MLflow >= 2.15 (CI: latest 3.x and the 2.x line)
• AutoGluon >= 1.1: TabularPredictor ([tabular] extra), TimeSeriesPredictor ([timeseries]), and MultiModalPredictor ([multimodal])

See examples/autolog_quickstart.py for a complete runnable walkthrough on a real dataset.

Roadmap

• Model signature inference during autologging
• ClearML integration (tracked separately)

Development

The repo ships an environment.yml for a conda/mamba based dev environment:

mamba env create -f environment.yml
mamba run -n mlflow-autogluon pip install -e .
mamba run -n mlflow-autogluon pytest

License

Apache License 2.0. See LICENSE.