https://github.com/laactechnology/foxcross

AsyncIO serving for data science models
https://github.com/laactechnology/foxcross

async data-science dataframe http machine-learning pandas python pytorch rest-api scikit-learn serving

Last synced: 26 days ago
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AsyncIO serving for data science models

• Host: GitHub
• URL: https://github.com/laactechnology/foxcross
• Owner: laactechnology
• License: bsd-3-clause
• Created: 2019-05-31T02:08:19.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T08:56:45.000Z (almost 2 years ago)
• Last Synced: 2024-09-27T20:22:41.772Z (about 1 month ago)
• Topics: async, data-science, dataframe, http, machine-learning, pandas, python, pytorch, rest-api, scikit-learn, serving
• Language: Python
• Homepage: https://www.foxcross.dev/
• Size: 3.2 MB
• Stars: 24
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/contributing.md
  • License: LICENSE.md

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README

        
# Foxcross

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AsyncIO serving for data science models built on [Starlette](https://www.starlette.io/)

**Requirements**: Python 3.6.1+

## Quick Start

Installation using `pip`:

```bash

pip install foxcross

```

Create some test data and a simple model in the same directory to be served:

directory structure

```

.

+-- data.json

+-- models.py

```

data.json

```json

[1,2,3,4,5]

```

models.py

```python

from foxcross.serving import ModelServing, run_model_serving

class AddOneModel(ModelServing):

    test_data_path = "data.json"

    def predict(self, data):

        return [x + 1 for x in data]

if __name__ == "__main__":

    run_model_serving()

```

Run the model locally

```bash

python models.py

```

Navigate to `localhost:8000/predict-test/` in your web browser, and you should see the

list incremented by 1. You can visit `localhost:8000/` to see all the available

endpoints for your model.

## Why does this package exist?

Currently, some of the most popular data science model building frameworks such as PyTorch

and Scikit-Learn do not come with a built in serving library similar to TensorFlow Serving.

To fill this gap, people create Flask applications to serve their model. This can be error

prone, and the implementation can differ between each model. Additionally, Flask is a

[WSGI](https://en.wikipedia.org/wiki/Web_Server_Gateway_Interface)

web framework whereas Foxcross is built on [Starlette](https://www.starlette.io/), a

more performant [ASGI](https://asgi.readthedocs.io/en/latest/) web framework.

Foxcross aims to be the serving library for data science models built with frameworks

that do not come with their own serving library. Using Foxcross enables consistent

and testable serving of data science models.

## Security

If you believe you've found a bug with security implications, please do not disclose this

issue in a public forum.

Email us at [[email protected]](mailto:[email protected])