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Machine Learning Lifecycle Framework

Project description

ebonite.jpg

Ebonite is a machine learning lifecycle framework. It allows you to persist your models and reproduce them (as services or in general).

Installation

pip install ebonite

Quickstart

First, create a Ebonite client.

from ebonite import Ebonite
ebnt = Ebonite.local()

Second, create a task and push your model object with some sample data.

task = ebnt.get_or_create_task('my_project', 'my_task')
model = task.create_and_push_model(clf, test_x, 'my_sklearn_clf')

You are awesome! Now you can load you model from this repo and do other wonderful stuff with it, for example create a docker image.

Check out examples and documentation to learn more.

Documentation

… is available here

Supported libraries and repositories

  • Machine Learning
    • scikit-learn
    • TensorFlow < 2
    • XGBoost
    • LightGBM
    • PyTorch
    • CatBoost
  • Data
    • NumPy
    • pandas
    • images
  • Repositories
    • SQLAlchemy
    • Amazon S3
  • Serving
    • Flask

Contributing

Read this Changelog =========

Current release candidate

0.3.2 (2019-12-04)

  • Multi-model interface bug fixes

0.3.1 (2019-12-04)

  • Minor bug fixes

0.3.0 (2019-11-27)

  • Added support for LightGBM models
  • Added support for XGBoost models
  • Added support for PyTorch models
  • Added support for CatBoost models
  • Added uwsgi server for flask containers

0.2.1 (2019-11-19)

  • Minor bug fixes

0.2.0 (2019-11-14)

  • First release on PyPI.

Project details


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