https://github.com/rtlee9/serveit

Simple API serving for Python ML models
https://github.com/rtlee9/serveit

keras machine-learning machine-learning-api pytorch restful-api scikit-learn tensorflow

Last synced: 4 months ago
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Simple API serving for Python ML models

• Host: GitHub
• URL: https://github.com/rtlee9/serveit
• Owner: rtlee9
• License: mit
• Created: 2018-02-20T02:51:53.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-11-22T02:15:10.000Z (over 2 years ago)
• Last Synced: 2025-02-28T00:53:47.059Z (5 months ago)
• Topics: keras, machine-learning, machine-learning-api, pytorch, restful-api, scikit-learn, tensorflow
• Language: Python
• Homepage: https://serveit.ryanlee.site/
• Size: 332 KB
• Stars: 32
• Watchers: 4
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ServeIt

[![Build Status](https://travis-ci.org/rtlee9/serveit.svg?branch=master)](https://travis-ci.org/rtlee9/serveit)

[![Codacy Grade Badge](https://api.codacy.com/project/badge/Grade/2af32a3840d5441e815f3956659b091f)](https://www.codacy.com/app/ryantlee9/serveit)

[![Codacy Coverage Badge](https://api.codacy.com/project/badge/Coverage/2af32a3840d5441e815f3956659b091f)](https://www.codacy.com/app/ryantlee9/serveit)

[![PyPI version](https://badge.fury.io/py/ServeIt.svg)](https://badge.fury.io/py/ServeIt)

ServeIt lets you serve model predictions and supplementary information from a RESTful API using your favorite Python ML library in as little as one line of code:

```python

from serveit.server import ModelServer

from sklearn.linear_model import LogisticRegression

from sklearn.datasets import load_iris

# fit logistic regression on Iris data

clf = LogisticRegression()

data = load_iris()

clf.fit(data.data, data.target)

# initialize server with a model and start serving predictions

ModelServer(clf, clf.predict).serve()

```

Your new API is now accepting `POST` requests at `localhost:5000/predictions`! Please see the [examples](examples) directory for detailed examples across domains (e.g., regression, image classification), including live examples.

#### Features

Current ServeIt features include:

1. Model inference serving via RESTful API endpoint

1. Extensible library for inference-time data loading, preprocessing, input validation, and postprocessing

1. Supplementary information endpoint creation

1. Automatic JSON serialization of responses

1. Configurable request and response logging (work in progress)

#### Supported libraries

The following libraries are currently supported:

* Scikit-Learn

* Keras

* PyTorch

## Installation: Python 2.7 and Python 3.6

Installation is easy with pip: `pip install serveit`

## Building

You can build locally with: `python setup.py`

## License

[MIT](LICENSE.md)

Please consider buying me a coffee if you like my work: