If you are using a CPU with Hyper-Threading enabled, the code is written so that onnxruntime will infer in parallel with (number of physical CPU cores * 2 - 1) to maximize performance. I don't …

ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →

Benchmark ONNX models with CPUs, GPUs (CUDA/TensorRT), Intel CPU/NPU (OpenVINO), and MemryX MX3 Resources

ONNX Runtime Model Optimization. ONNX Shape Inference. Quantization requires tensor shape information to perform its best. Model optimization also improve the performance of …

Feb 1, 2024 · We bound a container A running onnxruntime program to a certain CPU processor, and another container B running the same onnxruntime program to another CPU processor. If …

The CPU version of ONNX Runtime provides a complete implementation of all operators in the ONNX spec. This ensures that your ONNX-compliant model can execute successfully. In order …

Jan 15, 2019 · I am testing the performance of onnx runtime on a machine with both CPU and GPU. Since I have installed both MKL-DNN and TensorRT, I am confused about whether my …

Export a pre-trained or custom trained YOLOv5 model to generate the respective ONNX, TorchScript and CoreML formats of the model. The pre-trained yolov5s.pt is the lightest and …

This repo has examples that demonstrate the use of ONNX Runtime (ORT) for inference.

The Phi-3 ONNX models are hosted here in a collection on Hugging Face. This tutorial downloads and runs the Phi-3 mini short context model. If you would like to use another model, please …