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https://github.com/rekcurd/rekcurd-python
Project for serving ML module. This is a gRPC micro-framework.
https://github.com/rekcurd/rekcurd-python
drucker grpc kubernetes ml rekcurd
Last synced: 3 months ago
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Project for serving ML module. This is a gRPC micro-framework.
- Host: GitHub
- URL: https://github.com/rekcurd/rekcurd-python
- Owner: rekcurd
- License: apache-2.0
- Created: 2018-07-17T01:27:51.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-10-01T05:14:40.000Z (over 4 years ago)
- Last Synced: 2024-11-09T18:09:54.273Z (3 months ago)
- Topics: drucker, grpc, kubernetes, ml, rekcurd
- Language: Python
- Homepage:
- Size: 217 KB
- Stars: 47
- Watchers: 4
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Rekcurd
[](https://travis-ci.com/rekcurd/rekcurd-python)
[](https://badge.fury.io/py/rekcurd)
[](https://codecov.io/gh/rekcurd/rekcurd-python "Non-generated packages only")
[](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
(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. |