falcon-ml 0.7.0

AutoML library for fast experementations.

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.
• Extendability: Falcon's modular design, along with its extension registration procedure, allows seamless integration with virtually any framework.
• Portability: A standout feature of Falcon is its deep native support for ONNX models. This lets you export complex pipelines into a single ONNX graph, irrespective of the underlying frameworks. As a result, your model can be conveniently deployed on any platform or with almost any programming language, all without dependence on the training environment.

Future Developments 🔮

Falcon ML is under active development. We've already implemented a robust and production-ready core functionality, but there's much more to come. We plan to introduce many new features by the end of the year, so stay tuned!

⭐ If you liked the project, please support us with a star!

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

Installing some of the dependencies on Apple Silicon Macs might not work, the workaround is to create an X86 environment using Conda

conda create -n falcon_env
conda activate falcon_env
conda config --env --set subdir osx-64
conda install python=3.9
pip3 install falcon-ml

Documentation 📚

You can find a more detailed guide as well as an API reference in our official docs.

Authors & Contributors ✨

Oleg Kostromin

Iryna Kondrashchenko

Marco Pasini