Machine Learning Lifecycle Framework
Project description
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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