History

current - 2020-08-08 - 0.00Mb

  • issue159: Implements runtime for ConvTranspose (2020-08-07)

  • issue160: Fixes #159, add operator ConvTranspose, refactoring. (2020-08-07)

  • issue158: Fixes benchmark import issues (2020-08-03)

  • issue157: Simplify scenarios, reduce time for benchmark. (2020-08-02)

  • issue155: Fixes API on documentation (2020-08-02)

  • issue156: Fixes #155, improves documentation (2020-08-02)

  • issue154: Fixes y_train dtype for most of the problems. Fixes subproblems with GridSearchCV (2020-07-31)

  • issue152: Set n_jobs to the number of core - 1 when doing benchmark (2020-07-31)

  • issue153: Fixes #152, set set n_jobs to the number of CPU (2020-07-31)

  • issue151: Force operator Conv to use continuous array (2020-07-30)

  • issue150: Fixes nan issue in operator conv (2020-07-29)

  • issue145: Fixes missing shape inference for operator conv (2020-07-29)

  • issue147: Fixes #145, #150, shape inference for operator Conv (2020-07-29)

  • issue148: Add operator atan (2020-07-22)

  • issue149: Fixes #148, add operator Atan (2020-07-22)

  • issue144: Implements operator GlobalAveragePool (2020-07-21)

  • issue146: Fixes #144, add operator GlobalAveragePool (2020-07-21)

  • issue142: Implement python runtime for operator BatchNormalization (2020-07-21)

  • issue143: Fixes #142, add operator BatchNormalization (2020-07-21)

  • issue140: Implement runtime for QuantizeLinear, DequantizeLinear (2020-07-20)

  • issue141: Fixes #140, add runtime for QuantizeLinear, DequantizeLinear (2020-07-20)

0.4.1204 - 2020-07-09 - 0.31Mb

  • issue139: Add runtime for operator EyeLike (2020-07-08)

  • issue138: Add code to register custom python operator (2020-07-08)

  • issue137: Remove parameter dtype (onnx conversion) (2020-07-08)

  • issue136: Add parameter reshape to OnnxTransformer (2020-07-03)

  • issue135: Add a function to change the first dimension output (ONNX). (2020-07-03)

  • issue133: Implements runtime for operator Gather (ONNX) (2020-06-18)

  • issue132: Add operator StringNormalizer, Tokenizer, TfidfVectorizer (ONNX) (2020-06-15)

  • issue131: Add custom operator solve (2020-06-12)

  • issue130: Add operator Erf (ONNX) (2020-06-11)

  • issue129: Add operator Einsum (ONNX) (2020-06-11)

  • issue128: Fixes #127, implements OnnxPipeline, train, convert at each step (2020-06-08)

  • issue127: Implements a pipeline which replaces early stages by onnx (2020-06-08)

0.3.1129 - 2020-06-04 - 0.29Mb

  • issue123: Enables opset 12 (ONNX) (2020-06-04)

  • issue117: Support for op_version in onnx grammar (2020-06-04)

0.3.1108 - 2020-05-20 - 0.29Mb

  • issue126: Fix xgboost converter for xgboost >= 1.0 (2020-05-18)

  • issue125: Refactor rewritten sklearn operators (2020-05-18)

  • issue124: Fixes #122, capture standard C ouptput with dump_data_model, first step for #123 (2020-05-16)

  • issue122: Captures C output when calling dump_data_and_model (2020-05-16)

0.3.1082 - 2020-05-01 - 2.84Mb

  • issue121: Add function to convert array to bytes and bytes to array (onnx tensor) (2020-04-30)

  • issue120: Fix discrepencies for SVM classifier (ONNX) (2020-04-30)

  • issue119: Keep order in topk implementation (2020-04-17)

  • issue118: opset is not propagated in OnnxTransformer (2020-04-09)

0.3.1070 - 2020-04-07 - 0.29Mb

  • issue115: Add a function to replay a benchmark when this one was dumped (more accurate) (2020-04-06)

  • issue116: Makes ZipMapDictionary picklable (2020-03-30)

  • issue114: Add more parameters to specify benchmark time (2020-03-30)

  • issue113: Add operators for opset 12 (2020-03-26)

  • issue112: Number of feature is wrong for problem num-tr-clus (2020-03-20)

0.3.1029 - 2020-03-17 - 0.28Mb

  • issue111: Reduce the number of allocation in TreeEnsemble when it is parallelized (cache) (2020-03-13)

  • issue110: Implements runtime for operator Constant-12 (2020-03-06)

  • issue109: Generate a benchmark with asv to compare different runtime. Update modules in asv. (2020-03-06)

  • issue108: Add a function to reduce the memory footprint (2020-02-25)

  • issue106: Add operator Neg (2020-02-25)

  • issue101: Fix DecisionTreeClassifier disappearance on the benchmark graph (2020-02-25)

  • issue107: Add operator IsNaN (2020-02-24)

  • issue105: Support string labels for Linear, TreeEnsemble, SVM classifiers. (2020-02-24)

  • issue104: Enable / disable parallelisation in topk (2020-02-23)

  • issue103: Implements plot benchmark ratio depending on two parameters (2020-02-22)

  • issue102: Fix conversion for xgboost 1.0 (2020-02-21)

0.3.975 - 2020-02-19 - 0.28Mb

  • issue100: add notebook on TreeEnsemble (2020-02-19)

  • issue99: Fixes #93, use same code for TreeEnsembleClassifier and TreeEnsembleRegression (2020-02-19)

  • issue93: Use pointer for TreeClassifier (2020-02-19)

  • issue98: mlprodict i broken after onnxruntime, skl2onnx update (2020-02-15)

  • issue97: Add runtime for operator Conv (2020-01-24)

  • issue96: Fixes #97, add runtime for operator Conv (2020-01-24)

  • issue95: Fix OnnxInference where an output and an operator share the same name (2020-01-15)

  • issue94: Raw scores are always positive for TreeEnsembleClassifier (binary) (2020-01-13)

  • issue90: Implements a C++ runtime for topk (2019-12-17)

  • issue86: Use pointers to replace treeindex in tree ensemble cpp runtime (2019-12-17)

  • issue92: Implements a C++ version of ArrayFeatureExtractor (2019-12-14)

  • issue89: Implements a function which extracts some informations on the models (2019-12-14)

  • issue88: Fix bug in runtime of GatherElements (2019-12-14)

0.3.853 - 2019-12-13 - 0.24Mb

  • issue87: Add converter for HistGradientBoostRegressor (2019-12-09)

  • issue85: Implements a precompiled run method in OnnxInference (runtime=’python_compiled’) (2019-12-07)

  • issue84: Automatically creates files to profile time_predict function in the benchmark with py-spy (2019-12-04)

  • issue83: ONNX: includes experimental operators in the benchmark (2019-12-04)

  • issue82: Function translate_fct2onnx: use of opset_version (2019-12-04)

  • issue81: ONNX benchmark: track_score returns scores equal to 0 or 1 (unexpected) (2019-12-04)

  • issue80: ONNX: extend benchmark to decision_function for some models (2019-12-03)

  • issue77: Improves ONNX benchmark to measure zipmap impact. (2019-12-03)

  • issue76: Implements ArgMax 12, ArgMax 12 (python onnx runtime) (2019-11-27)

  • issue75: ONNX: fix random_state whevever it is available when running benchmark (2019-11-27)

0.3.765 - 2019-11-21 - 0.22Mb

  • issue59: ONNX: Investigate kmeans and opset availability. (2019-11-21)

  • issue66: ONNX: improves speed of python runtime for decision trees (2019-11-19)

  • issue74: Function _modify_dimension should return the same dataset if called the same parameter (even if it uses random functions) (2019-11-15)

  • issue73: ONNX: fix links on benchmark page (opset is missing) (2019-11-07)

  • issue72: ONNX: support of sparse tensor for a unary and binary python operators (2019-11-06)

  • issue71: ONNX: add operator Constant (2019-11-06)

  • issue67: ONNX: improves speed of svm regressor (2019-11-06)

  • issue70: ONNX: write tools to test convervsion for models in scikit-learn examples (2019-10-29)

  • issue65: ONNX: investigate discrepencies for k-NN (2019-10-28)

  • issue69: ONNX: side by side should work by name and not by positions (2019-10-23)

  • issue68: ONNX: improves speed of SGDClassifier (2019-10-23)

  • issue61: Implements a function to create a benchmark based on asv (ONNX) (2019-10-17)

  • issue63: Export asv results to csv (ONNX) + command line (2019-10-11)

  • issue64: Add an example with lightgbm and categorical variables (ONNX) (2019-10-07)

  • issue62: Implements command line for the asv benchmark (ONNX) (2019-10-04)

  • issue60: Improve lightgbm converter (ONNX) (2019-09-30)

  • issue58: Fix table checking model, merge is wrong in documentation (2019-09-20)

0.2.542 - 2019-09-15 - 0.59Mb

  • issue57: ONNX: handles dataframe when converting a model (2019-09-15)

  • issue56: ONNX: implements cdist operator (2019-09-12)

  • issue54: ONNX: fix summary, it produces multiple row when model are different when opset is different (2019-09-12)

  • issue51: ONNX: measure the time performance obtained by using optimization (2019-09-11)

  • issue52: ONNC-cli: add a command line to optimize an onnx model (2019-09-10)

  • issue49: ONNX optimization: remove redundant subparts of a graph (2019-09-09)

  • issue48: ONNX optimization: reduce the number of Identity nodes (2019-09-09)

  • issue47: Implements statistics on onnx graph and sklearn models, add them to the documentation (2019-09-06)

  • issue46: Implements KNearestNeibhorsRegressor supporting batch mode (ONNX) (2019-08-31)

  • issue45: KNearestNeighborsRegressor (2019-08-30)

  • issue44: Add an example to look into the performance of every node for a particular dataset (2019-08-30)

  • issue43: LGBMClassifier has wrong shape (2019-08-29)

0.2.452 - 2019-08-28 - 0.13Mb

  • issue42: Adds a graph which visually summarize the validating benchmark (ONNX). (2019-08-27)

  • issue41: Enables to test multiple number of features at the same time (ONNX) (2019-08-27)

  • issue40: Add a parameter to change the number of featuress when validating a model (ONNX). (2019-08-26)

  • issue39: Add a parameter to dump all models even if they don’t produce errors when being validated (ONNX) (2019-08-26)

  • issue24: support double for TreeEnsembleClassifier (python runtime ONNX) (2019-08-23)

  • issue38: See issue on onnxmltools. https://github.com/onnx/onnxmltools/issues/321 (2019-08-19)

  • issue35: Supports parameter time_kwargs in the command line (ONNX) (2019-08-09)

  • issue34: Add intervals when measuring time ratios between scikit-learn and onnx (ONNX) (2019-08-09)

  • issue31: Implements shape inference for the python runtime (ONNX) (2019-08-06)

  • issue15: Tells operator if the execution can be done inplace for unary operators (ONNX). (2019-08-06)

  • issue27: Bug fix (2019-08-02)

  • issue23: support double for TreeEnsembleRegressor (python runtime ONNX) (2019-08-02)

0.2.363 - 2019-08-01 - 0.11Mb

  • issue26: Tests all converters in separate processeses to make it easier to catch crashes (2019-08-01)

  • issue25: Ensures operator clip returns an array of the same type (ONNX Python Runtime) (2019-07-30)

  • issue22: Implements a function to shake an ONNX model and test float32 conversion (2019-07-28)

  • issue21: Add customized converters (2019-07-28)

  • issue20: Enables support for TreeEnsemble operators in python runtime (ONNX). (2019-07-28)

  • issue19: Enables support for SVM operators in python runtime (ONNX). (2019-07-28)

  • issue16: fix documentation, visual graph are not being rendered in notebooks (2019-07-23)

  • issue18: implements python runtime for SVM (2019-07-20)

0.2.272 - 2019-07-15 - 0.09Mb

  • issue17: add a mechanism to use ONNX with double computation (2019-07-15)

  • issue13: add automated benchmark of every scikit-learn operator in the documentation (2019-07-05)

  • issue12: implements a way to measure time for each node of the ONNX graph (2019-07-05)

  • issue11: implements a better ZipMap node based on dedicated container (2019-07-05)

  • issue8: implements runtime for decision tree (2019-07-05)

  • issue7: implement python runtime for scaler, pca, knn, kmeans (2019-07-05)

  • issue10: implements full runtime with onnxruntime not node by node (2019-06-16)

  • issue9: implements a onnxruntime runtime (2019-06-16)

  • issue6: first draft of a python runtime for onnx (2019-06-15)

  • issue5: change style highlight-ipython3 (2018-01-05)