**ONNX Runtime** is a performance-focused complete scoring engine for Open Neural Network Exchange (ONNX) models, with an open extensible architecture to continually address the latest developments in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX standard and supports all operators from the ONNX v1.2+ spec with both forwards and backwards compatibility. Please refer to [this page](docs/Versioning.md) for ONNX opset compatibility details.
[ONNX](https://onnx.ai) is an interoperable format for machine learning models supported by various ML and DNN frameworks and tools. The universal format makes it easier to interoperate between frameworks and maximize the reach of hardware optimization investments.
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 version compatibility details [here](./docs/Versioning.md).
In addition to DNN models, ONNX Runtime fully supports the [ONNX-ML profile](https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md) of the ONNX spec for traditional ML scenarios.
ONNX Runtime supports both CPU and GPU. Using various graph optimizations and accelerators, ONNX Runtime can provide lower latency compared to other runtimes for faster end-to-end customer experiences and minimized machine utilization costs.
Not all variations are supported in the [official release builds](#apis-and-official-builds), but can be built from source following [these instructions](./BUILD.md).
We are continuously working to integrate new execution providers for further improvements in latency and efficiency. If you are interested in contributing a new execution provider, please see [this page](docs/AddingExecutionProvider.md).
ONNX Runtime is currently available for Linux, Windows, and Mac with Python, C#, C++, and C APIs. Please see [API documentation and package installation](#installation).
If you have specific scenarios that are not supported, please share your suggestions and scenario details via [Github Issues](https://github.com/microsoft/onnxruntime/issues).
**Quick Start:** The [ONNX-Ecosystem Docker container image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem) is available on Dockerhub and includes ONNX Runtime (CPU, Python), dependencies, tools to convert from various frameworks, and Jupyter notebooks to help get started.
* Older ONNX Runtime releases: used **CUDA 9.1** and **cuDNN 7.1** - please refer to [prior release notes](https://github.com/microsoft/onnxruntime/releases)for more details.
* Python binaries are compatible with **Python 3.5-3.7**. See [Python Dev Notes](./docs/Python_Dev_Notes.md). If using `pip` to be download the Python binaries, run `pip install --upgrade pip` prior to downloading.
If additional build flavors and/or dockerfiles are needed, please find instructions at [Build ONNX Runtime](BUILD.md). For production scenarios, it's strongly recommended to build only from an [official release branch](https://github.com/microsoft/onnxruntime/releases).
* To export or convert a trained ONNX model trained from various frameworks, see [ONNX Tutorials](https://github.com/onnx/tutorials). Versioning compatibility information can be found under [Versioning](docs/Versioning.md#tool-compatibility)
ONNX Runtime can be deployed to the cloud for model inferencing using [Azure Machine Learning Services](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).
**ONNX Runtime Server (beta)** is a hosted application for serving ONNX models using ONNX Runtime, providing a REST API for prediction. Usage details can be found [here](./docs/ONNX_Runtime_Server_Usage.md), and image installation instructions are [here](./dockerfiles#onnx-runtime-server-preview).
The expanding focus and selection of IoT devices with sensors and consistent signal streams introduces new opportunities to move AI workloads to the edge.
This is particularly important when there are massive volumes of incoming data/signals that may not be efficient or useful to push to the cloud due to storage or latency considerations. Consider: surveillance tapes where 99% of footage is uneventful, or real-time person detection scenarios where immediate action is required. In these scenarios, directly executing model inferencing on the target device is crucial for optimal assistance.
To deploy AI workloads to these edge devices and take advantage of hardware acceleration capabilities on the target device, see [these reference implementations](https://github.com/Azure-Samples/onnxruntime-iot-edge).
### Local applications
ONNX Runtime packages are published to PyPi and Nuget (see [Official Builds](#official-builds) and/or can be [built from source](./BUILD.md) for local application development. Find samples [here](https://github.com/microsoft/onnxruntime/tree/master/samples/c_cxx) using the C++ API.
On newer Windows 10 devices (1809+), ONNX Runtime is available by default as part of the OS and is accessible via the [Windows Machine Learning APIs](https://docs.microsoft.com/en-us/windows/ai/windows-ml/). Find tutorials [here](https://docs.microsoft.com/en-us/windows/ai/windows-ml/get-started-desktop) for building a Windows Desktop or UWP application using WinML.
ONNX Runtime is open and extensible, supporting a broad set of configurations and execution providers for model acceleration. For performance tuning guidance, please see [this page](./docs/ONNX_Runtime_Perf_Tuning.md).
To tune performance for ONNX models, the [ONNX Go Live tool "OLive"](https://github.com/microsoft/OLive) provides an easy-to-use pipeline for converting models to ONNX and optimizing performance for inferencing with ONNX Runtime.
This project may collect usage data and send it to Microsoft to help improve our products and services. See the [privacy statement](docs/Privacy.md) for more details.
