torch-ort-infer 1.12.0

OpenVINO™ Integration with Torch-ORT accelerates PyTorch models using OpenVINO™ Execution Provider for ONNX Runtime. This product is designed for PyTorch developers who want to get started with OpenVINO™ in their inferencing applications. It delivers OpenVINO™ inline optimizations that enhance inferencing performance with minimal code modifications.

OpenVINO™ Integration with Torch-ORT accelerates inference across many AI models on a variety of Intel® hardware such as:

• Intel® CPUs
• Intel® integrated GPUs
• Intel® Movidius™ Vision Processing Units - referred to as VPU.

Installation

Requirements

• Ubuntu 18.04, 20.04
• Python 3.7, 3.8 or 3.9

This package supports:

• Intel® CPUs
• Intel® integrated GPUs
• Intel® Movidius™ Vision Processing Units (VPUs).

The torch-ort-infer package has dependency on the onnxruntime-openvino package that will be installed by default to run inference workloads. This onnxruntime-openvino package comes with pre-built libraries of OpenVINO™ version 2022.1.0 eliminating the need to install OpenVINO™ separately. The OpenVINO™ libraries are prebuilt with CXX11_ABI flag set to 0.

For more details, please refer to OpenVINO™ Execution Provider for ONNX Runtime.

Post-installation step

Once torch-ort-infer is installed, there is a post-installation step:

python -m torch_ort.configure

Usage

By default, Intel® CPU is used to run inference. However, you can change the default option to either Intel® integrated GPU or Intel® VPU for AI inferencing. Invoke the provider options to change the hardware on which inferencing is done.

For more API calls and environment variables, see Usage.

Samples

For quick start, explore the samples for Bert Sequence Classification and Image Classification scenarios.

License

OpenVINO™ Integration with Torch-ORT is licensed under MIT. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

Support

Please submit your questions, feature requests and bug reports via GitHub Issues.

How to Contribute

We welcome community contributions to OpenVINO™ Integration with Torch-ORT. If you have an idea for improvement:

• Share your proposal via GitHub Issues.
• Submit a Pull Request.