Releases 0.2.1
#79 : update to onnxruntime v1.16.0#77 : helpers to benchmark a model#74 : add a function to enumerate all intermediate results with onnxruntime#71 , #72 , #73 : add function to analyse a profile produce by onnxruntime#68 , #69 , #70 : add CPU implementation for CustomGemmFloat8#67 : add a function to extract a subgraph of a model#59 , #60 , #61 , #62 , #63 , #65 ,#66 , #68 , #69 , #70 :#57 : add C implementation for DynamicQuantizeLinear (for experimentation)#56 : add C implementation to cast a float into float 8#55 , #58 : add basic functionality to transform a graph, starts with basic quantization#51 : fix optmized TreeEnsembleRegressor and adds TreeEnsembleClassifier as custom ops#50 : add command line store to store intermediate outputs#49 : add option to save intermediate results in CReferenceEvaluator#45 : add option cuda-link to setup.py to specify how to link with CUDA library#41 : implements a custom kernel for RandomForestRegressor easier to optimize#34 : update to onnxruntime v1.15.1#31 : implement a custom CUDA kernel (gemm)#32 : update to onnxruntime v1.15.0#27 : add a custom kernel with parameters to onnxruntime#26 : add a custom kernel to onnxruntime#24 : use Eigen to implement Conv operator#23 : make pip wheel . work#22 : rename cmake into _cmake to avoid warnings related to cmake package#19 : minimal settings to use onnxruntime#14 : minimal setting to use CUDA#8 : support for C++ unit test
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