Metadata-Version: 2.1
Name: rekcurd
Version: 1.0.1a2
Summary: A Python gRPC framework for serving a machine learning module written in Python.
Home-page: https://github.com/rekcurd/rekcurd-python
Author: Rekcurd
Author-email: 
License: Apache License Version 2.0
Description: # Rekcurd
        
        [![Build Status](https://travis-ci.com/rekcurd/rekcurd-python.svg?branch=master)](https://travis-ci.com/rekcurd/rekcurd-python)
        [![PyPI version](https://badge.fury.io/py/rekcurd.svg)](https://badge.fury.io/py/rekcurd)
        [![codecov](https://codecov.io/gh/rekcurd/rekcurd-python/branch/master/graph/badge.svg)](https://codecov.io/gh/rekcurd/rekcurd-python "Non-generated packages only")
        [![pypi supported versions](https://img.shields.io/pypi/pyversions/rekcurd.svg)](https://pypi.python.org/pypi/rekcurd)
        
        Rekcurd is the Project for serving ML module. This is a gRPC micro-framework and it can be used like [Django](https://docs.djangoproject.com/) and [Flask](http://flask.pocoo.org/).
        
        
        ## Parent Project
        https://github.com/rekcurd/community
        
        
        ## Components
        - [Rekcurd](https://github.com/rekcurd/rekcurd-python): Project for serving ML module.
        - [Rekcurd-dashboard](https://github.com/rekcurd/dashboard): Project for managing ML model and deploying ML module.
        - [Rekcurd-client](https://github.com/rekcurd/python-client): Project for integrating ML module.
        
        
        ## Installation
        From source:
        
        ```bash
        $ git clone --recursive https://github.com/rekcurd/rekcurd-python.git
        $ cd rekcurd-python
        $ pip install -e .
        ```
        
        From [PyPi](https://pypi.org/project/rekcurd/) directly:
        
        ```bash
        $ pip install rekcurd
        ```
        
        ## How to use
        Example is available [here](https://github.com/rekcurd/rekcurd-example/tree/master/python/sklearn-digits). You can generate Rekcurd template and implement necessary methods.
        
        ```bash
        $ rekcurd startapp {Your application name}
        $ cd {Your application name}
        $ vi app.py
        $ python app.py
        ```
        
        
        ## Unittest
        ```
        $ python -m unittest
        ```
        
        
        ## Kubernetes support
        Rekcurd can be run on Kubernetes. See [community repository](https://github.com/rekcurd/community).
        
        
        ## Type definition
        ### `PredictLabel` type
        *V* is the length of feature vector.
        
        |Field |Type |Description |
        |:---|:---|:---|
        |input <BR>(required) |One of below<BR>- string<BR>- bytes<BR>- string[*V*]<BR>- int[*V*]<BR>- double[*V*] |Input data for inference.<BR>- "Nice weather." for a sentiment analysis.<BR>- PNG file for an image transformation.<BR>- ["a", "b"] for a text summarization.<BR>- [1, 2] for a sales forcast.<BR>- [0.9, 0.1] for mnist data. |
        |option |string| Option field. Must be json format. |
        
        The "option" field needs to be a json format. Any style is Ok but we have some reserved fields below.
        
        |Field |Type |Description |
        |:---|:---|:---|
        |suppress_log_input |bool |True: NOT print the input and output to the log message. <BR>False (default): Print the input and outpu to the log message. |
        |YOUR KEY |any |YOUR VALUE |
        
        ### `PredictResult` type
        *M* is the number of classes. If your algorithm is a binary classifier, you set *M* to 1. If your algorithm is a multi-class classifier, you set *M* to the number of classes.
        
        |Field |Type |Description |
        |:---|:---|:---|
        |label<BR>(required) |One of below<BR> -string<BR> -bytes<BR> -string[*M*]<BR> -int[*M*]<BR> -double[*M*] |Result of inference.<BR> -"positive" for a sentiment analysis.<BR> -PNG file for an image transformation.<BR> -["a", "b"] for a multi-class classification.<BR> -[1, 2] for a multi-class classification.<BR> -[0.9, 0.1] for a multi-class classification. |
        |score<BR>(required) |One of below<BR> -double<BR> -double[*M*] |Score of result.<BR> -0.98 for a binary classification.<BR> -[0.9, 0.1] for a multi-class classification. |
        |option |string |Option field. Must be json format. |
        
        ### `EvaluateResult` type
        `EvaluateResult` is the evaluation score. *N* is the number of evaluation data. *M* is the number of classes. If your algorithm is a binary classifier, you set *M* to 1. If your algorithm is a multi-class classifier, you set *M* to the number of classes.
        
        |Field |Type |Description |
        |:---|:---|:---|
        |num<BR>(required)|int |Number of evaluation data. |
        |accuracy<BR>(required) |double |Accuracy. |
        |precision<BR>(required) |double[*M*] |Precision. |
        |recall<BR>(required) |double[*M*] |Recall. |
        |fvalue<BR>(required) |double[*M*] |F1 value. |
        
        ### `EvaluateDetail` type
        `EvaluateDetail` is the details of evaluation result.
        
        |Field |Type |Description |
        |:---|:---|:---|
        |result<BR>(required) |PredictResult |Prediction result. |
        |is_correct<BR>(required) |bool |Correct or not. |
        
Keywords: Rekcurd,Kubernetes
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Web Environment
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
