rekcurd-python
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rekcurd
Project for serving ML module. This is a gRPC micro-framework.
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 <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. |
rekcurd