Machine learning prediction serving
Project description
# ServeIt
[](https://travis-ci.org/rtlee9/serveit)
[](https://www.codacy.com/app/ryantlee9/serveit)
[](https://www.codacy.com/app/ryantlee9/serveit)
[](https://badge.fury.io/py/ServeIt)
ServeIt lets you easily serve model predictions and supplementary information from a RESTful API. Current 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)
## Installation: Python 2.7 and Python 3.6
Installation is easy with pip: `pip install serveit`
## Usage:
Deploy your model `clf` to an API endpoint with as little as one line of code:
```python
from serveit.server import ModelServer
# initialize server with a model and a method to use for predictions
# then 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 additional usage.
### Supported libraries
* Scikit-Learn
* Keras
### Coming soon:
* TensorFlow
* PyTorch
## Building
You can build locally with: `python setup.py`
## License
[MIT](LICENSE.md)
[](https://travis-ci.org/rtlee9/serveit)
[](https://www.codacy.com/app/ryantlee9/serveit)
[](https://www.codacy.com/app/ryantlee9/serveit)
[](https://badge.fury.io/py/ServeIt)
ServeIt lets you easily serve model predictions and supplementary information from a RESTful API. Current 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)
## Installation: Python 2.7 and Python 3.6
Installation is easy with pip: `pip install serveit`
## Usage:
Deploy your model `clf` to an API endpoint with as little as one line of code:
```python
from serveit.server import ModelServer
# initialize server with a model and a method to use for predictions
# then 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 additional usage.
### Supported libraries
* Scikit-Learn
* Keras
### Coming soon:
* TensorFlow
* PyTorch
## Building
You can build locally with: `python setup.py`
## License
[MIT](LICENSE.md)
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