FALCON: A Lightweight AutoML Library

Falcon is a lightweight python library that allows to train production-ready machine learning models in a single line of code.

Why Falcon ? 🔍

• Simplicity: With Falcon, training a comprehensive Machine Learning pipeline is as easy as writing a single line of code.
• Flexibility: Falcon offers a range of pre-set configurations, enabling swift interchangeability of internal components with just a minor parameter change.
• Portability: A standout feature of Falcon is its deep native support for FNNX/ONNX models. This lets you export complex pipelines into a single production-ready file, irrespective of the underlying frameworks. As a result, your model can be conveniently deployed without any dependency on the training environment.

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Quick Start 🚀

You can try falcon out simply by pointing it to the location of your dataset.

from falcon import AutoML

AutoML(task = 'tabular_classification', train_data = '/path/to/titanic.csv')

Alternatively, you can use one of the available demo datasets.

from falcon import AutoML
from falcon.datasets import load_churn_dataset, load_insurance_dataset 
# churn -> classification; insurance -> regression

df = load_churn_dataset()

AutoML(task = 'tabular_classification', train_data = df)

Installation 💾

Stable release from PyPi

pip install falcon-ml

Latest version from GitHub

pip install git+https://github.com/OKUA1/falcon

Optional extras add the FNNX runtime, the gradient boosting candidates and hyperparameter search.

pip install "falcon-ml[runtime]"
pip install "falcon-ml[gbdt]"
pip install "falcon-ml[hpo]"

Documentation 📚

The user guide covers configuration, evaluation, export and inference.