Accelerate PyTorch models with ONNX Runtime. Contribute to tryweirdier/py-onnx development by creating an account on GitHub.

When deploying large-scale deep learning applications, C++ may be a better choice than Python to meet application demands or to optimize model performance. Therefore, I specifically document my recent ...

According to Facebook, PyTorch 1.0 takes the modular, production-oriented capabilities from Caffe2 and ONNX and combines them with PyTorch's existing flexible, research-focused design to provide a ...

Alternatively, PyTorch 1.0 integrates the capabilities from Caffe2 and ONNX and combines it with PyTorch's ability to provide a seamless path from research prototyping to production deployment.

🐛 Describe the bug As the title, when using onnx to export a quantized convolution layer, the outcome will have plus or minus one difference in some positions with the quantized convolution layer. ...

Along with helping developers write ONNX machine learning models without being ONNX experts, Microsoft said the project also serves as the foundation for its new PyTorch ONNX exporter to support ...

ONNX will act as the model export format in PyTorch 1.0 and will allow for the integration of accelerated runtimes or hardware-specific libraries.

In the final article of a four-part series on binary classification using PyTorch, Dr. James McCaffrey of Microsoft Research shows how to evaluate the accuracy of a trained model, save a model to file ...