ONNX Runtime is an open source inference engine for ONNX Models. ONNX Runtime Execution Providers (EPs) enables the execution of any ONNX model using a single set of inference APIs that provide access to the best hardware acceleration available.
In simple terms, developers no longer need to worry about the nuances of hardware specific custom libraries to accelerate their machine learning models. This repository demonstrates that by enabling the same code with ROS 2 to run on different hardware platforms using their respective AI acceleration libraries for optimized execution of the ONNX model.
ONNX Runtime team is releasing different binaries for Windows ML or Linux CPU and GPU (CUDA) support.
mkdir colcon_ws\src
cd colcon_ws
wget https://raw.githubusercontent.com/ms-iot/ros_msft_onnx/master/onnx_windows.repos
vcs import src < onnx.repos
colcon build- CPU Only
mkdir colcon_ws/src
cd colcon_ws
wget https://raw.githubusercontent.com/ms-iot/ros_msft_onnx/master/onnx.repos
vcs import src < onnx.repos
colcon build --cmake-args -DCUDA_SUPPORT=OFF- Cuda GPU
mkdir colcon_ws/src
cd colcon_ws
wget https://raw.githubusercontent.com/ms-iot/ros_msft_onnx/master/onnx.repos
vcs import src < onnx.repos
colcon build --cmake-args -DCUDA_SUPPORT=ONThis project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
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.