Falcon is a lightweight python library that allows to train production-ready machine learning models in a single line of code.
- 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.
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!
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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)
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
You can find a more detailed guide as well as an API reference in our official docs.
Oleg Kostromin |
Iryna Kondrashchenko |
Marco Pasini |