/LIteCOW

Easily deploy inference models to dev, test, and production at scale

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Inference with Collected ONNX Weights

Easily deploy inference models to dev, test, and production at scale
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Getting Started

Installation 🚀

pip install litecow
pip install litecow-models

Usage 🐄

Try out ICOW with the sandbox!

Run the sandbox

curl -s https://raw.githubusercontent.com/Striveworks/LIteCOW/main/sandbox/setup.sh | bash

Import a model

litecow import-model --source https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/tiny-yolov3/model/tiny-yolov3-11.onnx tinyyolov3

Run the example object de

curl -s https://raw.githubusercontent.com/Striveworks/LIteCOW/main/sandbox/sandbox.py | python - https://github.com/Striveworks/LIteCOW/raw/main/sandbox/cow.jpeg

Testing 🧪

make sandbox

Generating documentation 📖

make docs

Roadmap 🛣️

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the Server Side Public License (SSPL). See LICENSE for more information.

Contact

Striveworks