Machine learning prediction serving
Project description
# ServeIt
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[![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:
<a href="https://www.buymeacoffee.com/6Ii7vzL" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: auto !important;width: auto !important;" ></a>
[![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:
<a href="https://www.buymeacoffee.com/6Ii7vzL" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: auto !important;width: auto !important;" ></a>
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