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This change adds a new execution provider powered by [DirectML](https://aka.ms/DirectML). DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning on Windows. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers. The DirectML execution provider is capable of greatly improving evaluation time of models using commodity GPU hardware, without sacrificing broad hardware support or requiring vendor-specific extensions to be installed. **Note** that the DML EP code was moved verbatim from the existing WindowsAI project, which is why it doesn't yet conform to the onnxruntime coding style. This is something that can be fixed later; we would like to keep formatting/whitespace changes to a minimum for the time being to make it easier to port fixes from WindowsAI to ORT during this transition. Summary of changes: * Initial commit of DML EP files under onnxruntime/core/providers/dml * Add cmake entries for building the DML EP and for pulling down the DirectML redist using nuget * Add a submodule dependency on the Windows Implementation Library (WIL) * Add docs under docs/execution_providers/DirectML-ExecutionProvider.md * Add support for DML EP to provider tests and perf tests * Add support for DML EP to fns_candy_style_transfer sample * Add entries to the C ABI for instantiating the DML EP |
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README.md
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 with complete implementation of all ONNX operators, and supports all ONNX releases (1.2+) with both future and backwards compatibility. Please refer to this page for ONNX opset compatibility details.
ONNX 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.
Setup
Usage
More Info
Key Features
Run any ONNX model
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.
Traditional ML support
In addition to DNN models, ONNX Runtime fully supports the ONNX-ML profile of the ONNX spec for traditional ML scenarios.
For the full set of operators and types supported, please see operator documentation
Note: Some operators not supported in the current ONNX version may be available as a Contrib Operator
High Performance
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.
Currently ONNX Runtime supports the following accelerators:
- MLAS (Microsoft Linear Algebra Subprograms)
- DirectML
- MKL-DNN - subgraph optimization
- MKL-ML
- Intel nGraph
- CUDA
- TensorRT
- OpenVINO
- Nuphar
Not all variations are supported in the official release builds, but can be built from source following these instructions. Find Dockerfiles here.
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.
Cross Platform
API documentation and package installation
ONNX Runtime is currently available for Linux, Windows, and Mac with Python, C#, C++, and C APIs. If you have specific scenarios that are not supported, please share your suggestions and scenario details via Github Issues.
Installation
Quick Start: The ONNX-Ecosystem Docker container image is available on Dockerhub and includes ONNX Runtime (CPU, Python), dependencies, tools to convert from various frameworks, and Jupyter notebooks to help get started.
Additional dockerfiles for some features can be found here.
APIs and Official Builds
API Documentation
Official Builds
| CPU (MLAS+Eigen) | CPU (MKL-ML) | GPU (CUDA) | |
|---|---|---|---|
| Python | pypi: onnxruntime Windows (x64) Linux (x64) Mac OS X (x64) |
-- | pypi: onnxruntime-gpu Windows (x64) Linux (x64) |
| C# | Nuget: Microsoft.ML.OnnxRuntime Windows (x64, x86) Linux (x64, x86) Mac OS X (x64) |
Nuget: Microsoft.ML.OnnxRuntime.MKLML Windows (x64) Linux (x64) Mac OS X (x64) |
Nuget: Microsoft.ML.OnnxRuntime.Gpu Windows (x64) Linux (x64) |
| C/C++ wrapper | Nuget: Microsoft.ML.OnnxRuntime .zip, .tgz Windows (x64, x86) Linux (x64, x86) Mac OS X (x64) |
Nuget: Microsoft.ML.OnnxRuntime.MKLML Windows (x64) Linux (x64) Mac OS X (x64) |
Nuget: Microsoft.ML.OnnxRuntime.Gpu .zip, .tgz Windows (x64) Linux (x64) |
System Requirements (pre-requisite dependencies)
- ONNX Runtime binaries in the CPU packages use OpenMP and depend 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 and vc_redist.x86.exe
- For Linux, the system must have libgomp.so.1 which can be installed using
apt-get install libgomp1.
- GPU builds require CUDA runtime libraries being installed on the system:
- Version: CUDA 10.0 and cuDNN 7.3
- Linux Python packages require CUDA 10.1 and cuDNN 7.6
- Older ONNX Runtime releases: used CUDA 9.1 and cuDNN 7.1 - please refer to prior release notes for more details.
- Python binaries are compatible with Python 3.5-3.7. See Python Dev Notes. If using
pipto be download the Python binaries, runpip install --upgrade pipprior to downloading. - Certain operators makes use of system locales. Installation of the English language package and configuring
en_US.UTF-8 localeis required.- For Ubuntu install language-pack-en package
- Run the following commands:
locale-gen en_US.UTF-8update-locale LANG=en_US.UTF-8 - Follow similar procedure to configure other locales on other platforms.
Building from Source
If additional build flavors are needed, please find instructions on building from source at Build ONNX Runtime. For production scenarios, it's strongly recommended to build from an official release branch.
Dockerfiles are available here to help you get started.
Usage
Getting ONNX Models
- The ONNX Model Zoo has popular ready-to-use pre-trained models.
- To export or convert a trained ONNX model trained from various frameworks, see ONNX Tutorials. Versioning compatibility information can be found under Versioning
- Other services that can be used to create ONNX models include:
Deploying ONNX Runtime
ONNX Runtime can be deployed to the cloud for model inferencing using Azure Machine Learning Services. See detailed instructions and sample notebooks.
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, and image installation instructions are here.
Performance Tuning
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.
To tune performance for ONNX models, the ONNX Go Live tool "OLive" provides an easy-to-use pipeline for converting models to ONNX and optimizing performance for inferencing with ONNX Runtime.
Examples and Tutorials
Python
Inference only
- Model Inferencing (single node Sigmoid)
- Model Inferencing (Resnet50)
- Model Inferencing using ONNX-Ecosystem Docker image
- Model Inferencing using ONNX Runtime Server (SSD Single Shot MultiBox Detector)
Inference with model conversion
Inference and deploy through AzureML
-
Inferencing using ONNX Model Zoo models:
-
Convert existing model for Inferencing:
-
Train a model with PyTorch and Inferencing:
-
GPU: Inferencing with TensorRT Execution Provider (AKS)
Inference and Deploy wtih Azure IoT Edge
Other
C#
C/C++
Technical Design Details
Extensibility Options
- Add a custom operator/kernel
- Add an execution provider
- Add a new graph transform
- Add a new rewrite rule
Contribute
We welcome contributions! Please see the contribution guidelines.
Feedback
For any feedback or to report a bug, please file a GitHub Issue.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.