Deploy models in ONNX format
Project description
audonnx deploys machine learning models stored in ONNX format.
Machine learning models can be trained in a variety of frameworks, e.g. scikit-learn, TensorFlow, Torch. To be independent of the training framework and its version models can be exported in ONNX format, which enables you to deploy and combine them easily.
audonnx allows you to name inputs and outputs of your model, and automatically loads the correct feature extraction from a YAML file stored with your model.
Have a look at the installation and usage instructions.
Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
Version 0.4.1 (2022-01-10)
Fixed: author email address in Python package metadata
Version 0.4.0 (2022-01-10)
Added: first public release
Changed: switch to MIT license
Changed: move repo to Github
Fixed: remove audsp from docstring example as we no longer depend on it
Version 0.3.3 (2021-12-30)
Changed: use Python 3.8 as default
Version 0.3.2 (2021-11-01)
Changed: use audobject >=0.6.1
Version 0.3.1 (2021-10-05)
Fixed: audonnx.load() try to load model from ONNX if YAML does not exist
Version 0.3.0 (2021-10-01)
Changed: audobject >=0.5.0
Changed: force .yaml extension when model is saved
Fixed: if possible load model from .yaml in audonnx.load()
Version 0.2.2 (2021-09-23)
Fixed: link to ONNX runtime CUDA mapping table
Version 0.2.1 (2021-09-15)
Fixed: loading of old models that contain a model.yaml file
Version 0.2.0 (2021-07-20)
Added: InputNode, Model.__call__(), Model.inputs, Model.outputs, OutputNode
Changed: reshape input to expected shape
Changed: do not depend on existing models in tests and documentation
Changed: support multiple input nodes
Changed: make Model serializable
Deprecated: Model.forward() Model.labels, Model.predict(), Model.transform
Removed: Model.input_node, Model.input_shape, Model.input_type, Model.output_nodes, Model.output_shape, Model.output_type,
Version 0.1.1 (2021-03-31)
Changed: update documentation how to select specific GPU device
Version 0.1.0 (2021-03-25)
Added: initial release
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