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### Description <!-- Describe your changes. --> Added newly introduced API combinations to getting-started table. ### Motivation and Context Attempting to get merged before / in time for ORT 1.17 release --------- Co-authored-by: MaanavD <maanavdalal@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
106 lines
4.5 KiB
Markdown
106 lines
4.5 KiB
Markdown
---
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title: Compatibility
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parent: Reference
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toc: true
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nav_order: 2
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---
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# ONNX Runtime compatibility
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{: .no_toc }
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## Backwards compatibility
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Newer versions of ONNX Runtime support all models that worked with prior versions, so updates should not break integrations.
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## Environment compatibility
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ONNX Runtime is not explicitly tested with every variation/combination of environments and dependencies, so this list is not comprehensive. Please use this as starting reference. For specific questions or requests, please [file an issue](https://github.com/microsoft/onnxruntime/issues) on Github.
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### Platforms
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* Windows
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* Tested with Windows 10 and Windows Server 2019
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* May be compatible with Windows 7+
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* Windows Machine Learning ([Windows](../get-started/with-windows.md))
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* CPU: Windows 8.1+
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* GPU: Windows 10 1709+
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* Linux
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* Tested with CentOS 7
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* Should be compatible with [distributions supported by .NET Core](https://docs.microsoft.com/en-us/dotnet/core/install/linux)
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* Mac
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* Tested with 10.14 (Mojave)
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* May be compatible with 10.12+ (Sierra)
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* Android
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* Tested with API level 28 (v9 "Pie")
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* May be compatible with API level 21+ (v5 "Lollipop")
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* iOS
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* Tested with iOS 12
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* May be compatible with any 64bit iOS version (5S+)
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### Compilers
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* Windows 10: Visual C++ 2019
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* Linux: gcc>=4.8
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### Dependent Libraries
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* [Submodules](https://github.com/microsoft/onnxruntime/tree/main/cgmanifests)
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* See the [Execution Provider page](../execution-providers) for details on specific hardware libary version requirements
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## ONNX opset support
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ONNX Runtime supports all opsets from the latest released version of the [ONNX](https://onnx.ai) spec. All versions of ONNX Runtime support ONNX opsets from ONNX v1.2.1+ (opset version 7 and higher).
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* For example: if an ONNX Runtime release implements ONNX opset 9, it can run models stamped with ONNX opset versions in the range [7-9].
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* [Supported Operator Data Types](https://github.com/microsoft/onnxruntime/blob/main/docs/OperatorKernels.md)
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* *Operators not supported in the current ONNX spec may be available as a [Contrib Operator](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md)*
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* [How to add a custom operator/kernel](operators/add-custom-op.md)
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| ONNX Runtime version | [ONNX version](https://github.com/onnx/onnx/blob/master/docs/Versioning.md) | ONNX opset version | ONNX ML opset version | ONNX IR version |
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|------------------------------|--------------------|--------------------|----------------------|------------------|
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| 1.17 | **1.15** | 20 | 4 | 9 |
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| 1.16 | **1.14.1** | 19 | 3 | 9 |
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| 1.15 | **1.14** | 19 | 3 | 8 |
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| 1.14 | **1.13** | 18 | 3 | 8 |
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| 1.13 | **1.12** | 17 | 3 | 8 |
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| 1.12 | **1.12** | 17 | 3 | 8 |
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| 1.11 | **1.11** | 16 | 2 | 8 |
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| 1.10 | **1.10** | 15 | 2 | 8 |
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| 1.9 | **1.10** | 15 | 2 | 8 |
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| 1.8 | **1.9** | 14 | 2 | 7 |
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| 1.7 | **1.8** | 13 | 2 | 7 |
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| 1.6 | **1.8** | 13 | 2 | 7 |
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| 1.5 | **1.7** | 12 | 2 | 7 |
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| 1.4 | **1.7** | 12 | 2 | 7 |
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| 1.3 | **1.7** | 12 | 2 | 7 |
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| 1.2<br/>1.1 | **1.6** | 11 | 2 | 6 |
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| 1.0 | **1.6** | 11 | 2 | 6 |
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| 0.5 | **1.5** | 10 | 1 | 5 |
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| 0.4 | **1.5** | 10 | 1 | 5 |
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| 0.3 | **1.4** | 9 | 1 | 3 |
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| 0.2 | **1.3** | 8 | 1 | 3 |
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| 0.1 | **1.3** | 8 | 1 | 3 |
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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|
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|---|---|
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|[PyTorch](https://pytorch.org/)|[Latest stable](https://pytorch.org/get-started/locally/)|
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|[Tensorflow-ONNX](https://pypi.org/project/tf2onnx/)|[Latest stable](https://github.com/onnx/tensorflow-onnx/releases)|
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|[ONNXMLTools](https://pypi.org/project/onnxmltools/)<br>CatBoost, CoreML, LightGBM, XGBoost, LibSVM, SparkML|[Latest stable](https://github.com/onnx/onnxmltools/releases)|
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|[SKLearn-ONNX](https://pypi.org/project/skl2onnx/)|[Latest stable](https://github.com/onnx/sklearn-onnx/releases)|
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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/)|
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|[AzureML AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml)|[1.0.39+](https://pypi.org/project/azureml-automl-core) (ONNX v1.5) <br/>[1.0.33](https://pypi.org/project/azureml-automl-core/1.0.33/) (ONNX v1.4) |
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|[Paddle2ONNX](https://pypi.org/project/paddle2onnx/)| [Latest stable](https://github.com/PaddlePaddle/Paddle2ONNX/releases) |
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