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Backend library, framework, and API for models in modelhub http://modelhub.ai/
Library and common framework on which model contributors must base their model contribution. The framework handles/provides
- data I/O
- data conversion to/from numpy (typical data format used in deep learning libraries)
- generic API for accessing and working with the model
- “slots” for preprocessing, postprocessing, and inference, which have to be populated by the contributor with the model specific code
For a detailed technical documentation of the whole Modelhub project and infrastructure see modelhub.readthedocs.io.
/docs contains Sphinx documentation sources for the modelhub project.
/framework contains the modelhub framework code.
Template files for the contributer source (integrating the actual net plus required pre- and postprocessing) are locateded here. Instructions on how to prepare a new contribution can be found here.
For deployment of a model the framework is part of the runtime docker. However, when developing on the framework you would not want to re-build a docker to test each change. Hence the start script here has the option to mount a framework folder, temporarily replacing the internal framwork. After installing the start script via pip install modelhub-ai, simply run:
modelhub MODEL_NAME -mf PATH_TO_FRAMEWORK
Execute the docker build command from the modelhub-engine directory, with the following command:
docker build -t <name+tag> -f docker/<docker-file-name> .
Make sure the Docker name starts with "modelhub/" and the tag should also be a unique version number (e.g. "modelhub/release:3").
Then login to docker with your credentials:
docker login
Upload the docker you created:
docker push <name+tag>
Logout:
docker logout
See NOTICE file in /framework for acknowledgements of third party technologies used.