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.

Field	Type	Description
input (required)	One of below - string - bytes - string[V] - int[V] - double[V]	Input data for inference. - "Nice weather." for a sentiment analysis. - PNG file for an image transformation. - ["a", "b"] for a text summarization. - [1, 2] for a sales forcast. - [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. 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 (required)	One of below -string -bytes -string[M] -int[M] -double[M]	Result of inference. -"positive" for a sentiment analysis. -PNG file for an image transformation. -["a", "b"] for a multi-class classification. -[1, 2] for a multi-class classification. -[0.9, 0.1] for a multi-class classification.
score (required)	One of below -double -double[M]	Score of result. -0.98 for a binary classification. -[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 (required)	int	Number of evaluation data.
accuracy (required)	double	Accuracy.
precision (required)	double[M]	Precision.
recall (required)	double[M]	Recall.
fvalue (required)	double[M]	F1 value.

EvaluateDetail type

EvaluateDetail is the details of evaluation result.

Field	Type	Description
result (required)	PredictResult	Prediction result.
is_correct (required)	bool	Correct or not.