https://github.com/davidgasquez/moser

:postbox: A simple way to serve your Machine Learning models.
https://github.com/davidgasquez/moser

machine-learning python scikit-learn

Last synced: about 1 month ago
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:postbox: A simple way to serve your Machine Learning models.

• Host: GitHub
• URL: https://github.com/davidgasquez/moser
• Owner: davidgasquez
• License: mit
• Created: 2016-08-25T09:21:48.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2021-07-30T16:15:38.000Z (over 3 years ago)
• Last Synced: 2023-05-31T21:30:49.568Z (over 1 year ago)
• Topics: machine-learning, python, scikit-learn
• Language: Python
• Homepage:
• Size: 49.8 KB
• Stars: 6
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Moser: A Model Server Prototype

Moser is a simple way to serve your Machine Learning models. The goal of Moser

is making super easy to setup any kind of predictive model in a production

server and being able to interact with them through RESTful API calls.

## Features

- Set Models

- Set Functions

## Get Started

You can test it running the container (`make`) and heading

to [http://localhost:5000](http://localhost:5000)

1. Generate the model `pkl`

2. Build the container `make build`

3. Run the API with `make`

4. Make some requests to [http://localhost:5000](http://localhost:5000):

    - Add a model to the API with a `PUT` call to `/api/models/your_model_name`

    - Use the previous model calling `/api/models/your_model_name/predict` with

      a `POST` request providing a single JSON with the feature names and

      values.

### Example

Generate the `pkl` file using `joblib`:

```python

import pandas as pd

import numpy as np

from sklearn.datasets import load_iris

from sklearn.ensemble import RandomForestClassifier

from sklearn.externals import joblib

iris = load_iris()

data = pd.DataFrame(data=np.c_[iris['data'], iris['target']],

                    columns=iris['feature_names'] + ['target'])

clf = RandomForestClassifier()

clf.fit(data.drop('target', axis=1), data['target'])

joblib.dump(clf, 'model.pkl', compress=9)

```

Once we have the file we need to run the server with `make` and make some API

requests:

```python

import requests

base_url = 'http://localhost:5000'

filename = 'model.pkl'

# Set the model

with open(filename, 'rb') as f:

    model = f.read()

    r = requests.put(base_url + '/api/models/iris', data=model)

    print(r)

# Make predictions from JSON

data = {

    "features": ["sepal_length", "sepal_width", "petal_length", "petal_width"],

    "values": [

        [1, 4, 1, 1],

        [2, 0, 6, 1],

        [1, 4, 8, 1]

    ]

}

r = requests.post(base_url + '/api/models/iris/predict', json=data)

print(r, r.json())

```