Accelerate PyTorch Models via OpenVINO™ Integration with Torch-ORT

Machine learning projects often struggle to reach production, with 85% reportedly failing, according to Gartner. To enhance the performance of models that do enter production, accelerated inference can be employed, leveraging specialized hardware and libraries to speed up the process. Intel, collaborating with Microsoft, has integrated OpenVINO™ with Torch-ORT to improve inference on Intel® hardware while retaining the native PyTorch experience. This integration allows PyTorch developers to achieve significant performance gains on Intel hardware without needing to refactor existing code, as demonstrated with a YOLOv7 model re-trained on a custom dataset. By adding only two lines of code, developers can achieve a performance improvement of approximately 13% when using OpenVINO™ with Torch-ORT compared to native PyTorch, all while maintaining accuracy. The case study also highlights the cost efficiency of using CPU over GPU for inference, as CPU instances are significantly cheaper while offering competitive performance when optimized. The integration supports a seamless workflow by allowing inline optimizations and simplifying the installation process, making it an attractive option for deploying computer vision models efficiently.