Metadata-Version: 1.1
Name: ServeIt
Version: 0.0.1a1
Summary: Machine learning prediction serving
Home-page: https://github.com/rtlee9/serveit
Author: Ryan Lee
Author-email: ryantlee9@gmail.com
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: # ServeIt
        [![Build Status](https://travis-ci.org/rtlee9/serveit.svg?branch=master)](https://travis-ci.org/rtlee9/serveit)
        [![PyPI version](https://badge.fury.io/py/ServeIt.svg)](https://badge.fury.io/py/ServeIt)
        [![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)](#installation-python-27-and-python-36)
        [![Python 3.7](https://img.shields.io/badge/python-3.6-blue.svg)](#installation-python-27-and-python-36)
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
        
        
        ServeIt deploys your trained models to a RESTful API for prediction serving. Current features include:
        
        1. Model prediction serving
        1. Supplementary information endpoint creation
        1. Configurable request and response logging (work in progress)
        
        
        ## Installation: Python 2.7 and Python 3.6
        * PyPi: `pip install serveit`
        * source: `git clone https://github.com/rtlee9/serveit.git && cd serveit && pip install -e .`  # WIP
        
        ## Supported libraries
        * Scikit-Learn
        
        ```python
        from sklearn.datasets import load_iris
        from sklearn.linear_model import LogisticRegression
        from serveit.sklearn_server import SklearnServer
        
        # fit a model on the Iris dataset
        data = load_iris()
        reg = LogisticRegression()
        reg.fit(data.data, data.target)
        
        # deploy model to a SkLearnServer
        sklearn_server = SklearnServer(reg, reg.predict)
        
        # add informational endpoints
        sklearn_server.create_model_info_endpoint()
        sklearn_server.create_info_endpoint('features', data.feature_names)
        sklearn_server.create_info_endpoint('target_labels', data.target_names.tolist())
        
        # start API
        sklearn_server.serve()
        ```
        
        Then try out your new API:
        ```bash
        curl -XPOST 'localhost:5000/predictions'\
        	-H "Content-Type: application/json"\
        	-d "[[5.6, 2.9, 3.6, 1.3], [4.4, 2.9, 1.4, 0.2], [5.5, 2.4, 3.8, 1.1], [5.0, 3.4, 1.5, 0.2], [5.7, 2.5, 5.0, 2.0]]"
        # [1, 0, 1, 0, 2]
        
        curl -XGET 'localhost:5000/info/model'
        # {"penalty": "l2", "tol": 0.0001, "C": 1.0, "classes_": [0, 1, 2], "coef_": [[0.4150, 1.4613, -2.2621, -1.0291], ...], ...}
        
        curl -XGET 'localhost:5000/info/features'
        # ["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]
        
        curl -XGET 'localhost:5000/info/target_labels'
        #  ["setosa", "versicolor", "virginica"]
        ```
        
        ## Coming soon:
        * TensorFlow
        * Keras
        * PyTorch
        
Keywords: machine learning model deployment serving API RESTful
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Internet :: WWW/HTTP :: WSGI :: Server
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
