To v0.4.9 #501
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To v0.4.9 #501
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Looks like torch script compile was generating an OnnxPad operation with None passed to constant_value argument. This was causing a crash in the execution engine. This was uncovered after fixing a bug of handling prim::Constant Op correctly.
Little hack to pin ONNX ir_version to 8 if onnxruntime version is less than 1.15. This should get fixed soon with update of onnx package. See microsoft/onnxruntime#15874
It works but its slow.
Version update
Seems like ONNX has switched to using / characters in node names. This was causing issues with expression parsing because output port ids are used in expressions.
…nto fix_torch_v2
Keras model serialized in examples/TensorFlow/Keras/kr_N_model.h5 is failing to load on new TF version. Getting: TypeError: weight_decay is not a valid argument, kwargs should be empty for `optimizer_experimental.Optimizer`.
Keras model serialized in examples/TensorFlow/Keras/kr_N_model.h5 is failing to load. Getting: TypeError: weight_decay is not a valid argument, kwargs should be empty for `optimizer_experimental.Optimizer`. Can't reproduce locally, trying this suggested fix: https://stackoverflow.com/questions/74667876/typeerror-weight-decay-is-not-a-valid-argument-kwargs-should-be-empty-for-opt
Re-enable full testing of package on 3.10
Looks like scikit-learn wheels aren't available for 3.11. Getting compilation errors.
Testing Fix torch v2
* nml_examples: Use latest nml libraries; Regenerated multiple nml files Adding generated mdf files and images for pynn based examples Specify py version for actions/setup-python@v4 One more gha action version update Update versions github actions Latests tests More tests More tests on pynn examples Improved handling of spike transfers Updates to test pynn hh Formatted Initial tests NeuroMLlite->PyNN->MDF type info in input ports in generated graphs Fix issue with onnx trying to add int to float Regenerated with latest nml libraries Test latest modelspec
…ankur31-feat/py311
To v0.4.9; Add support for torch>=2, torchvision>=0.15.2, onnx>=1.14
Regenerated neuroml examples
Use graph_scheduler<1.2.0,>=1.1.1
Update GHA versions
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