APIs and tools to work with abstract "models" - files with numpy arrays and metadata. It is possible to publish models, list them. There is a built-in cache. Storage has backends.
Modelforge is a foundation for sharing trained machine learning models. It is a set of command line tools and a Python library. Modelforge maintains model files in a third-party remote storage service ("cloud") using the backend mechanism. Model metadata (download links, names, descriptions, versions, etc.) resides in a Git repository called the "Index", and documentation is automatically generated there. Modelforge does no assumptions about the models: they can be of any origin, such as TensorFlow, scikit-learn, or your custom. The underlying model storage format - Advanced Scientific Data Format - can wrap any data easily and efficiently, but it's the developer's responsibility to convert.
Learn more about:
You can run Modelforge through Docker:
docker run -it --rm srcd/modelforge --help
or install it using the Python package manager:
pip3 install modelforge
The project exposes two interfaces: command line and API.
Contributions are pretty much welcome! Please follow the contributions guide and the code of conduct.
If you wish to make your MLonCode model available in src-d/models,
please fork that repository and run modelforge publish to upload your model on your fork. Then
create a pull request. You should provide read access to the model file for everybody. If you
consider using our Google Cloud Storage bucket, feel free to contact us through GitHub issues.
FAQs
APIs and tools to work with abstract "models" - files with numpy arrays and metadata. It is possible to publish models, list them. There is a built-in cache. Storage has backends.
We found that modelforge demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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