Rekcurd

Rekcurd is the Project for serving ML module. This is a gRPC micro-framework and it can be used like Django and Flask.

Parent Project

https://github.com/rekcurd/community

Components

• Rekcurd: Project for serving ML module.
• Rekcurd-dashboard: Project for managing ML model and deploying ML module.
• Rekcurd-client: Project for integrating ML module.

Installation

From source:

$ git clone --recursive https://github.com/rekcurd/rekcurd-python.git
$ cd rekcurd-python
$ pip install -e .

From PyPi directly:

$ pip install rekcurd

How to use

Example is available here. You can generate Rekcurd template and implement necessary methods.

$ 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.

Type definition

PredictLabel type

V is the length of feature vector.

The "option" field needs to be a json format. Any style is Ok but we have some reserved fields below.

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.

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.

EvaluateDetail type

EvaluateDetail is the details of evaluation result.