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ONNX is an open format for machine learning (ML) models that is supported by various ML and DNN frameworks and tools. This format makes it easier to interoperate between frameworks and to maximize the reach of your hardware optimization investments. Learn more about ONNX on [https://onnx.ai](https://onnx.ai) or view the [Github Repo](https://github.com/onnx/onnx).
# Why use ONNX Runtime
ONNX Runtime is an open architecture that is continually evolving to adapt to and address the newest developments and challenges in AI and Deep Learning. We will keep ONNX Runtime up to date with the ONNX standard, supporting all ONNX releases with future compatibliity while maintaining backwards compatibility with prior releases.
ONNX Runtime has an open architecture that is continually evolving to address the newest developments and challenges in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX standard, supporting all ONNX releases with future compatibliity and maintaining backwards compatibility with prior releases.
ONNX Runtime continuously strives to provide top performance for a broad and growing number of usage scenarios in Machine Learning. Our investments focus on these 3 core areas:
ONNX Runtime continuously strives to provide top performance for a broad and growing number of usage scenarios in Machine Learning. Our investments focus on:
1. Run any ONNX model
2. High performance
3. Cross platform
@ -23,19 +23,17 @@ ONNX Runtime continuously strives to provide top performance for a broad and gro
### Alignment with ONNX Releases
ONNX Runtime provides comprehensive support of the ONNX spec and can be used to run all models based on ONNX v1.2.1 and higher. See ONNX version release details [here](https://github.com/onnx/onnx/releases).
As of March 2019, ONNX Runtime supports ONNX 1.4.
As of May 2019, ONNX Runtime supports ONNX 1.5 (opset10). See [this table](https://github.com/Microsoft/onnxruntime/blob/master/docs/Versioning.md#version-matrix) for details on ONNX Runtime and ONNX versioning compatibility,
### Traditional ML support
ONNX Runtime fully supports the ONNX-ML profile of the ONNX spec for traditional ML scenarios.
## High Performance
You can use ONNX Runtime with both CPU and GPU hardware. You can also plug in additional execution providers to ONNX Runtime. With many graph optimizations and various accelerators, ONNX Runtime can often provide lower latency and higher efficiency compared to other runtimes. This provides smoother end-to-end customer experiences and lower costs from improved machine utilization.
ONNX Runtime supports both CPU and GPU hardware through a variety of execution providers. With a variety of graph optimizations and accelerators, ONNX Runtime often provides lower latency and higher efficiency compared to other runtimes. This provides faster end-to-end customer experiences and lower costs from improved machine utilization.
Currently ONNX Runtime supports CUDA, MLAS (Microsoft Linear Algebra Subprograms), MKL-DNN, and MKL-ML for computation acceleration. See more details on available build options [here](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) or refer to [this page](docs/AddingExecutionProvider.md) to add a new execution provider.
Currently ONNX Runtime supports CUDA, TensorRT, MLAS (Microsoft Linear Algebra Subprograms), MKL-DNN, MKL-ML, and nGraph for computation acceleration. See more details on available build options [here](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md).
We are continuously working to integrate new execution providers to provide improvements in latency and efficiency. We have ongoing collaborations to integrate the following with ONNX Runtime:
* Intel MKL-DNN and nGraph
* NVIDIA TensorRT
We are continuously working to integrate new execution providers to provide improvements in latency and efficiency. If you are interested in contributing a new execution provider, please see [this page](docs/AddingExecutionProvider.md).
## Cross Platform
ONNX Runtime offers:
@ -44,35 +42,37 @@ ONNX Runtime offers:
See API documentation and package installation instructions [below](#Installation).
Looking ahead: To broaden the reach of the runtime, we will continue investments to make ONNX Runtime available and compatible with more platforms. If you have specific scenarios that are not currently supported, please share your suggestions via [Github Issues](https://github.com/microsoft/onnxruntime/issues).
We have ongoing investments to make ONNX Runtime compatible with more platforms and architectures. If you have specific scenarios that are not currently supported, please share your suggestions via [Github Issues](https://github.com/microsoft/onnxruntime/issues).
# Getting Started
If you need a model:
ONNX models:
* Check out the [ONNX Model Zoo](https://github.com/onnx/models) for ready-to-use pre-trained models.
* To get an ONNX model by exporting from various frameworks, see [ONNX Tutorials](https://github.com/onnx/tutorials).
If you already have an ONNX model, just [install the runtime](#Installation) for your machine to try it out. One easy way to deploy the model on the cloud is by using [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service). See [detailed instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx).
Once you have an ONNX model, you can [install the runtime](#Installation) for your machine to try it out. There is also an [ONNX-Ecosystem Docker container](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem) available and ready for use with the Python API.
One easy way to deploy the model on the cloud is by using [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service). See [detailed instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx).
# Installation
## APIs and Official Builds
| API Documentation | CPU package | GPU package* |
|-----|-------------|-------------|
| [Python](https://aka.ms/onnxruntime-python)** | [Available on Pypi](https://pypi.org/project/onnxruntime)<br/><ul><li> Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul><br/> | [Available on Pypi](https://pypi.org/project/onnxruntime-gpu) <br/><ul><li> Windows: x64</li><li>Linux: x64</li></ul><br/><br/> |
| [C#](docs/CSharp_API.md) | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul>| [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br/><ul><li> Windows: x64</li><li>Linux: x64</li></ul><br/>|
| [C](docs/C_API.md) | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul><br/>[Files (.zip, .tgz)](https://aka.ms/onnxruntime-release)<br/><ul><li>Windows: x64, x86</li><li>Linux: x64, x86</li><li>Mac OS X: x64</li></ul> | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li></ul><br/><br/>[Files (.zip, .tgz)](https://aka.ms/onnxruntime-release)<br/><ul><li>Windows: x64</li><li>Linux: x64</li></ul><br/> |
| [C++](onnxruntime/core/session/inference_session.h) | [Build from source](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) | [Build from source](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) |
&#42;Requires CUDA 9.1 and cuDNN 7.1<br/>
&#42;&#42;Compatible with Python 3.5-3.7
## System Requirements
* ONNX Runtime binaries in CPU packages use OpenMP and depends on the library being available at runtime in the
system.
* For Windows, OpenMP support comes as part of VC runtime. It is also available as redist packages:
[vc_redist.x64.exe](https://aka.ms/vs/15/release/vc_redist.x64.exe) and [vc_redist.x86.exe](https://aka.ms/vs/15/release/vc_redist.x86.exe)
* For Linux, the system must have the libgomp.so.1 which can be installed using ```apt-get install libgomp1```.
* The GPU builds require the CUDA9.1 and cuDNN 7.3 runtime libraries being installed in the system.
* The official GPU builds require the CUDA 9.1 and cuDNN 7.1 runtime libraries being installed in the system.
* Python binaries are compatible with Python 3.5-3.7.
## APIs and Official Builds
| API Documentation | CPU package | GPU package |
|-----|-------------|-------------|
| [Python](https://aka.ms/onnxruntime-python) | [Available on Pypi](https://pypi.org/project/onnxruntime)<br/><ul><li> Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul><br/> | [Available on Pypi](https://pypi.org/project/onnxruntime-gpu) <br/><ul><li> Windows: x64</li><li>Linux: x64</li></ul><br/><br/> |
| [C#](docs/CSharp_API.md) | Available on Nuget : [MLAS+Eigen](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/), [MKL-ML](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.MKLML/)</br><ul><li>Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64 (MLAS+Eigen only)</li></ul>| [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br/><ul><li> Windows: x64</li><li>Linux: x64</li></ul><br/>|
| [C](docs/C_API.md) | Available on Nuget : [MLAS+Eigen](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/), [MKL-ML](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.MKLML/)</br><ul><li>Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64 (MLAS+Eigen only)</li></ul><br/>[Files (.zip, .tgz)](https://aka.ms/onnxruntime-release)<br/><ul><li>Windows: x64, x86</li><li>Linux: x64, x86</li><li>Mac OS X: x64 (MLAS+Eigen only)</li></ul> | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li></ul><br/><br/>[Files (.zip, .tgz)](https://aka.ms/onnxruntime-release)<br/><ul><li>Windows: x64</li><li>Linux: x64</li></ul><br/> |
| [C++](onnxruntime/core/session/inference_session.h) | [Build from source](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) | [Build from source](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) |
For builds using other execution providers, see Build Details below.
## Build Details
For details on the build configurations and information on how to create a build, see [Build ONNX Runtime](BUILD.md).