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* Fix PR #5550 reverted in #5911 (performance improvment for operator Transpose) (#5916) * Improves implementation of transpose operator * Fix issue mentioned in #5911 * adding unit test for function DoTransposeImpl * Make operator TreeEnsemble 5x faster for batches of size 100.000 (#5965) * improves processing time by 10 * extend coverage unit test coverage * better implementation for the multi regression case * better comment, keep parallelization by trees when not enough trees * Initialize a structure in operator ReduceSum (#6005) * fix initialisation issue * Fuse MatMulIntegerToFloat only when scales are scalar (#6008) MatMulIntegerToFloat fusion fuses per-row and per-column MatMulInteger, which is not supported by the MatMulIntegerToFloat kernel now. Limit the fusion to per-matrix only before we supporting the per-channel fully. * Disable Python 3.9 for training Python packaging build. (#6012) Disable Python 3.9 for training Python packaging build. Python 3.9 is not supported by the PyTorch dependency. * Fix bugs for 1: Calibrator should check model inputs; 2: (#6017) quantize_inupts forgot to use parameter initializer_use_weight_qtyp. * Bump highlight.js from 10.2.1 to 10.4.1 in /nodejs Bumps [highlight.js](https://github.com/highlightjs/highlight.js) from 10.2.1 to 10.4.1. - [Release notes](https://github.com/highlightjs/highlight.js/releases) - [Changelog](https://github.com/highlightjs/highlight.js/blob/master/CHANGES.md) - [Commits](https://github.com/highlightjs/highlight.js/compare/10.2.1...10.4.1) Signed-off-by: dependabot[bot] <support@github.com> * work around of the build break in mac (#6069) * Fix the build break in macos release * revert android change * Bump up API version for 1.6 release (#6076) * Update version to 1.6.0 (#6041) * Update version to 1.6.0 * Add v 1.5.3 info * Updating WindowsAI and ONNX version Co-authored-by: Du Li <duli@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Rsevert "Fuse MatMulIntegerToFloat only when scales are scalar (#6008)" This reverts commit beb950eb66308eeaa8c60e4db9a006948e2ba7bb. Co-authored-by: Xavier Dupré <xadupre@users.noreply.github.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Zhang Lei <zhang.huanning@hotmail.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Pranav Sharma <prs@microsoft.com> Co-authored-by: Du Li <duli@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
60 lines
3.8 KiB
Markdown
60 lines
3.8 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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The following table summarizes the relationship between the ONNX Runtime version and the ONNX opset version implemented in that release.
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Please note the backward 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 | [Windows ML Availability](https://docs.microsoft.com/en-us/windows/ai/windows-ml/release-notes/)|
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|------------------------------|--------------------|--------------------|----------------------|------------------|------------------|
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| 1.6.0 | **1.8** down to 1.2 | 13 | 2 | 7 | Windows AI 1.6+ |
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| 1.5.3 | **1.7** down to 1.2 | 12 | 2 | 7 | Windows AI 1.5+ |
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| 1.5.2 | **1.7** down to 1.2 | 12 | 2 | 7 | Windows AI 1.5+ |
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| 1.5.1 | **1.7** down to 1.2 | 12 | 2 | 7 | Windows AI 1.5+ |
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| 1.4.0 | **1.7** down to 1.2 | 12 | 2 | 7 | Windows AI 1.4+ |
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| 1.3.1 | **1.7** down to 1.2 | 12 | 2 | 7 | Windows AI 1.4+ |
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| 1.3.0 | **1.7** down to 1.2 | 12 | 2 | 7 | Windows AI 1.3+ |
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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 | Windows AI 1.3+ |
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| 1.0.0 | **1.6** down to 1.2 | 11 | 2 | 6 | Windows AI 1.3+ |
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| 0.5.0 | **1.5** down to 1.2 | 10 | 1 | 5 | Windows AI 1.3+ |
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| 0.4.0 | **1.5** down to 1.2 | 10 | 1 | 5 | Windows AI 1.3+ |
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| 0.3.1<br>0.3.0 | **1.4** down to 1.2 | 9 | 1 | 3 | Windows 10 2004+ |
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| 0.2.1<br>0.2.0 | **1.3** down to 1.2 | 8 | 1 | 3 | Windows 10 1903+ |
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| 0.1.5<br>0.1.4 | **1.3** down to 1.2 | 8 | 1 | 3 | Windows 10 1809+ |
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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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|[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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