ebonite 0.3.4

Machine Learning Lifecycle Framework

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

Before starting with Ebonite prepare your model. This could be a model from your favorite library (list of supported libraries is presented above) or a custom Python function working with typical machine learning data structures.

import numpy as np
def clf(data):
  return (np.sum(a, axis=-1) > 1).astype(np.int32)

Moreover, your custom function could wrap a model from some library. This gives you flexibility to use not only pure ML models but rule-based ones (e.g., as a service stub at project start) and hybrid (ML with pre/postprocessing) ones which are often applied to solve real world problems.

When a model is prepared you should create a Ebonite client.

from ebonite import Ebonite
ebnt = Ebonite.local()

Then create a task and push your model object with some sample data. Sample data is required for Ebonite to determine structure of inputs and outputs for your model.

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

You are awesome! Now your model is safely persisted in a repository.

Later on in other Python process you can load your model from this repo and do some wonderful stuff with it, e.g., create a Docker image named my_service with an HTTP service wrapping your model.

from ebonite import Ebonite
ebnt = Ebonite.local()
task = ebnt.get_or_create_task('my_project', 'my_task')
model = client.get_model('my_clf', task)
client.build_service('my_service', model)

Check out examples (in examples directory) and documentation to learn more.

Documentation

… is available here

Supported libraries and repositories

• Models

  • your arbitrary Python function

  • scikit-learn

  • TensorFlow < 2

  • XGBoost

  • LightGBM

  • PyTorch

  • CatBoost

• Model input / output data

  • NumPy

  • pandas

  • images

• Model repositories

  • in-memory

  • local filesystem

  • SQLAlchemy

  • Amazon S3

• Serving

  • Flask

Contributing

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

Current release candidate

• Fixed bug with deleting models from tasks

• Support working with model meta without requiring installation of all model dependencies

• Added region argument for s3 repository

• Support for delete_model in Ebonite client

• Support for force flag in delete_model which deletes model even if artifacts could not be deleted

0.3.3 (2020-01-10)

• Eliminated tensorflow warnings. Added more tests for providers/loaders. Fixed bugs in multi-model provider/builder.

• Improved documentation

• Eliminate useless “which docker” check which fails on Windows hosts

• Perform redirect from / to Swagger API docs in Flask server

• Support for predict_proba method in ML model

• Do not fix first dimension size for numpy arrays and torch tensors

• Support for Pytorch JIT (TorchScript) models

• Bump tensorflow from 1.14.0 to 1.15.0

• Added more tests

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.