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* link to folder instead of READMEs inside folder (#3938) otherwise hard to find the source code * [Node.js binding] fix linux build (#3927) * [Node.js binding] add build flag for node.js binding (#3948) * [Nodejs binding] create a new pipeline to generate signed binaries (#4104) * add yml files * update pipeline * fix yaml syntax * yaml pop BuildCSharp * udpate yaml * do not stage codesign summary * fix build: pipeline Node.js version to 12.16.3 (#4145) * [Node.js binding] upgrade node-addon-api to 3.0 (#4148) * [Node.js binding] add linux and mac package (#4157) * try mac pipeline * fix path separator * copy prebuilds folder * split esrp yaml for win/mac * disable mac signing temporarily * add linux * fix indent * add nodetool in linux * add nodetool in win-ci-2019 * replace linux build by custom docker scripts * use manylinux as node 12.16 not working on centos6 * try ubuntu * loosen timeout for test case - multiple runs calls * add script to support update nodejs binding version (#4164) * [java] Adds a CUDA test (#3956) * [java] - adding a cuda enabled test. * Adding --build_java to the windows gpu ci pipeline. * Removing a stray line from the unit tests that always enabled CUDA for Java. * Update OnnxRuntime.java for OS X environment. (#3985) onnxruntime init failure due to wrong path of reading native libraries. In OS X 64 system, the arch name is detected as x86 which generates invalid path to read native libraries. Exception java.lang.UnsatisfiedLinkError: no onnxruntime in java.library.path at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1867) at java.lang.Runtime.loadLibrary0(Runtime.java:870) at java.lang.System.loadLibrary(System.java:1122) at ai.onnxruntime.OnnxRuntime.load(OnnxRuntime.java:174) at ai.onnxruntime.OnnxRuntime.init(OnnxRuntime.java:81) at ai.onnxruntime.OrtEnvironment.<clinit>(OrtEnvironment.java:24) * Create Java publishing pipeline (#3944) Create CPU and GPu Java publishing pipelines. Final jars are tested on all platforms. However, signing and publishing to maven are manual steps. * Change group id to com.microsoft.onnxruntime per requirements. * Java GPu artifact naming (#4179) Modify gradle build so artifactID has _gpu for GPU builds. Pass USE_CUDA flag on CUDA build Adjust publishing pipelines to extract POM from a correct path. Co-Authored-By: @Craigacp * bump up ORT version to 1.3.1 (#4181) * move back to toolset 14.16 to possibly work around nvcc bug (#4180) * Symbolic shape inference exit on models without onnx opset used (#4090) * Symbolic shape inference exit on models without onnx opset used * Temporary fix for ConvTranspose with symbolic input dims Co-authored-by: Changming Sun <me@sunchangming.com> * Fix Nuphar test failure * Enlarge the read buffer size in C#/Java test code (#4150) 1. Enlarge the read buffer size further, so that our code can run even faster. TODO: need apply the similar changes to python some other language bindings. 2. Add coreml_VGG16_ImageNet to the test exclusion set of x86_32. It is not a new model but previously we didn't run the test against x86_32. * Temporarily disable windows static analysis CI job * skip model coreml_Imputer-LogisticRegression_sklearn_load_breast_cancer * Delete unused variable Co-authored-by: Prasanth Pulavarthi <prasantp@microsoft.com> Co-authored-by: Yulong Wang <yulongw@microsoft.com> Co-authored-by: Adam Pocock <adam.pocock@oracle.com> Co-authored-by: jji2019 <49252772+jji2019@users.noreply.github.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: Dmitri Smirnov <dmitrism@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: KeDengMS <kedeng@microsoft.com> Co-authored-by: Changming Sun <me@sunchangming.com> Co-authored-by: Changming Sun <chasun@microsoft.com>
55 lines
3.4 KiB
Markdown
55 lines
3.4 KiB
Markdown
# Versioning
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## API
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ONNX Runtime follows [Semantic Versioning 2.0](https://semver.org/) for its public API.
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Each release has the form MAJOR.MINOR.PATCH, adhering to the definitions from the linked semantic versioning doc.
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## Current stable release version
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The version number of the current stable release can be found
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[here](../VERSION_NUMBER).
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## Release cadence
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See [Release Management](ReleaseManagement.md)
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# Compatibility
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## Backwards compatibility
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All versions of ONNX Runtime will support ONNX opsets all the way back to (and including) opset version 7.
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In other words if an ONNX Runtime release implements ONNX opset ver 9, it'll be able to run all
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models that are stamped with ONNX opset versions in the range [7-9].
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### Version matrix
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Following table summarizes the relationship between the ONNX Runtime version and the ONNX
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opset version implemented in that release. Please note the Backwards and Forward compatibility notes above.
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For more details on ONNX Release versions, see [this page](https://github.com/onnx/onnx/blob/master/docs/Versioning.md).
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| ONNX Runtime release version | ONNX release version | ONNX opset version | ONNX ML opset version | Supported ONNX IR version | [WinML compatibility](https://docs.microsoft.com/en-us/windows/ai/windows-ml/)|
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|------------------------------|--------------------|--------------------|----------------------|------------------|------------------|
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| 1.3.1 | **1.7** down to 1.2 | 12 | 2 | 6 | -- |
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| 1.3.0 | **1.7** down to 1.2 | 12 | 2 | 6 | -- |
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| 1.2.0<br>1.1.2<br>1.1.1<br>1.1.0 | **1.6** down to 1.2 | 11 | 2 | 6 | -- |
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| 1.0.0 | **1.6** down to 1.2 | 11 | 2 | 6 | -- |
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| 0.5.0 | **1.5** down to 1.2 | 10 | 1 | 5 | -- |
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| 0.4.0 | **1.5** down to 1.2 | 10 | 1 | 5 | -- |
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| 0.3.1<br>0.3.0 | **1.4** down to 1.2 | 9 | 1 | 3 | -- |
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| 0.2.1<br>0.2.0 | **1.3** down to 1.2 | 8 | 1 | 3 | 1903 (19H1)+ |
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| 0.1.5<br>0.1.4 | **1.3** down to 1.2 | 8 | 1 | 3 | 1809 (RS5)+ |
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## Tool Compatibility
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A variety of tools can be used to create ONNX models. Unless otherwise noted, please use the latest released version of the tools to convert/export the ONNX model. Most tools are backwards compatible and support multiple ONNX versions. Join this with the table above to evaluate ONNX Runtime compatibility.
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|Tool|Recommended Version|Supported ONNX version(s)|
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|---|---|---|
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|[PyTorch](https://pytorch.org/)|[Latest stable](https://pytorch.org/get-started/locally/)|1.2-1.6|
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|[ONNXMLTools](https://pypi.org/project/onnxmltools/)<br>CoreML, LightGBM, XGBoost, LibSVM|[Latest stable](https://github.com/onnx/onnxmltools/releases)|1.2-1.6|
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|[ONNXMLTools](https://pypi.org/project/onnxmltools/)<br> SparkML|[Latest stable](https://github.com/onnx/onnxmltools/releases)|1.4-1.5|
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|[SKLearn-ONNX](https://pypi.org/project/skl2onnx/)|[Latest stable](https://github.com/onnx/sklearn-onnx/releases)|1.2-1.6|
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|[Keras-ONNX](https://pypi.org/project/keras2onnx/)|[Latest stable](https://github.com/onnx/keras-onnx/releases)|1.2-1.6|
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|[Tensorflow-ONNX](https://pypi.org/project/tf2onnx/)|[Latest stable](https://github.com/onnx/tensorflow-onnx/releases)|1.2-1.6|
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|[WinMLTools](https://docs.microsoft.com/en-us/windows/ai/windows-ml/convert-model-winmltools)|[Latest stable](https://pypi.org/project/winmltools/)|1.2-1.6|
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|[AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml)|[1.0.39+](https://pypi.org/project/azureml-automl-core)|1.5|
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| |[1.0.33](https://pypi.org/project/azureml-automl-core/1.0.33/)|1.4|
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