Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

A Python gRPC client for Drucker.

Project description

# rekcurd-client

[![Build Status](https://travis-ci.com/rekcurd/drucker-client.svg?branch=master)](https://travis-ci.com/rekcurd/drucker-client)
[![PyPI version](https://badge.fury.io/py/rekcurd-client.svg)](https://badge.fury.io/py/rekcurd-client)
[![codecov](https://codecov.io/gh/rekcurd/drucker-client/branch/master/graph/badge.svg)](https://codecov.io/gh/rekcurd/drucker-client "Non-generated packages only")
[![pypi supported versions](https://img.shields.io/pypi/pyversions/rekcurd-client.svg)](https://pypi.python.org/pypi/rekcurd-client)

Rekcurd client is the project for integrating ML module. Any Rekcurd service is connectable. It can connect the Rekcurd service on Kubernetes.


## Parent Project
https://github.com/rekcurd/drucker-parent


## Components
- [Rekcurd](https://github.com/rekcurd/drucker): Project for serving ML module.
- [Rekcurd-dashboard](https://github.com/rekcurd/drucker-dashboard): Project for managing ML model and deploying ML module.
- [Rekcurd-client](https://github.com/rekcurd/drucker-client) (here): Project for integrating ML module.


## Installation
From source:

```
git clone --recursive https://github.com/rekcurd/drucker-client.git
cd drucker-client
python setup.py install
```

From [PyPi](https://pypi.org/project/rekcurd_client/) directly:

```
pip install rekcurd_client
```

## How to use
Example code is available [here](./example/sample.py).

```python
from drucker_client import DruckerWorkerClient
from drucker_client.logger import logger


host = 'localhost:5000'
client = DruckerWorkerClient(logger=logger, host=host)

input = [0,0,0,1,11,0,0,0,0,0,
0,7,8,0,0,0,0,0,1,13,
6,2,2,0,0,0,7,15,0,9,
8,0,0,5,16,10,0,16,6,0,
0,4,15,16,13,16,1,0,0,0,
0,3,15,10,0,0,0,0,0,2,
16,4,0,0]
response = client.run_predict_arrint_arrint(input)
```

When you use Kubernetes and deploy Rekcurd service via Rekcurd dashboard, you can access your Rekcurd service like the below.

```python
from drucker_client import DruckerWorkerClient
from drucker_client.logger import logger


domain = 'example.com'
app = 'drucker-sample'
env = 'development'
client = DruckerWorkerClient(logger=logger, domain=domain, app=app, env=env)

input = [0,0,0,1,11,0,0,0,0,0,
0,7,8,0,0,0,0,0,1,13,
6,2,2,0,0,0,7,15,0,9,
8,0,0,5,16,10,0,16,6,0,
0,4,15,16,13,16,1,0,0,0,
0,3,15,10,0,0,0,0,0,2,
16,4,0,0]
response = client.run_predict_arrint_arrint(input)
```

### DruckerWorkerClient
You need to use an appropriate method for your Rekcurd service. The methods are generated according to the input and output formats. *V* is the length of feature vector. *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.

|method |input: data<BR>(required) |input: option |output: label<BR>(required) |output: score<BR>(required) |output: option |
|:---|:---|:---|:---|:---|:---|
|run_predict_string_string |string |string (json) |string |double |string (json) |
|run_predict_string_bytes |string |string (json) |bytes |double |string (json) |
|run_predict_string_arrint |string |string (json) |int[*M*] |double[*M*] |string (json) |
|run_predict_string_arrfloat |string |string (json) |double[*M*] |double[*M*] |string (json) |
|run_predict_string_arrstring |string |string (json) |string[*M*] |double[*M*] |string (json) |
|run_predict_bytes_string |bytes |string (json) |string |double |string (json) |
|run_predict_bytes_bytes |bytes |string (json) |bytes |double |string (json) |
|run_predict_bytes_arrint |bytes |string (json) |int[*M*] |double[*M*] |string (json) |
|run_predict_bytes_arrfloat |bytes |string (json) |double[*M*] |double[*M*] |string (json) |
|run_predict_bytes_arrstring |bytes |string (json) |string[*M*] |double[*M*] |string (json) |
|run_predict_arrint_string |int[*V*] |string (json) |string |double |string (json) |
|run_predict_arrint_bytes |int[*V*] |string (json) |bytes |double |string (json) |
|run_predict_arrint_arrint |int[*V*] |string (json) |int[*M*] |double[*M*] |string (json) |
|run_predict_arrint_arrfloat |int[*V*] |string (json) |double[*M*] |double[*M*] |string (json) |
|run_predict_arrint_arrstring |int[*V*] |string (json) |string[*M*] |double[*M*] |string (json) |
|run_predict_arrfloat_string |double[*V*] |string (json) |string |double |string (json) |
|run_predict_arrfloat_bytes |double[*V*] |string (json) |bytes |double |string (json) |
|run_predict_arrfloat_arrint |double[*V*] |string (json) |int[*M*] |double[*M*] |string (json) |
|run_predict_arrfloat_arrfloat |double[*V*] |string (json) |double[*M*] |double[*M*] |string (json) |
|run_predict_arrfloat_arrstring |double[*V*] |string (json) |string[*M*] |double[*M*] |string (json) |
|run_predict_arrstring_string |string[*V*] |string (json) |string |double |string (json) |
|run_predict_arrstring_bytes |string[*V*] |string (json) |bytes |double |string (json) |
|run_predict_arrstring_arrint |string[*V*] |string (json) |int[*M*] |double[*M*] |string (json) |
|run_predict_arrstring_arrfloat |string[*V*] |string (json) |double[*M*] |double[*M*] |string (json) |
|run_predict_arrstring_arrstring |string[*V*] |string (json) |string[*M*] |double[*M*] |string (json) |

The input "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 output to the log message.


## Unittest
```
$ python -m unittest
```


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for drucker-client, version 0.4.5
Filename, size File type Python version Upload date Hashes
Filename, size drucker_client-0.4.5-py2.py3-none-any.whl (21.3 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes
Filename, size drucker_client-0.4.5.tar.gz (20.7 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page