ONNX Runtime Web unleashes generative AI in the browser using WebGPU
ONNX Runtime Web featuring WebGPU is now available in the ONNX Runtime 1.17 release—unlocking new possibilities.
Tag: ONNX Runtime
On-Device Training: Training a model in browser
Continuing the ONNXRuntime On-Device Training blog series, we are introducing ONNX Runtime Training for Web.
Boosting performance in ONNX Runtime with Intel® AMX for 4th Gen Intel® Xeon® Processors
ONNX Runtime harnesses Intel® AMX to accelerate performance for the 4th Gen Intel® Xeon® CPUs.
Connect fluid dynamics, machine learning, and virtual reality with ONNX Runtime
Using ONNX Runtime to unlock the promise of developments in science for solving real world problems.
On-Device Training with ONNX Runtime: A deep dive
Building upon the foundation we established earlier, this blog will present comprehensive information about the underlying details of training models directly on user devices using ORT. Equipped with these technical details, we encourage you to try out On-Device Training with ONNX Runtime for your custom scenario.
Olive: A user-friendly toolchain for hardware-aware model optimization
Introducing Olive, an easy-to-use toolchain for optimizing models with hardware awareness. With Olive, you don't need to be an expert to explore diverse hardware optimization toolchains.
Automate optimization techniques for transformer models
Intel has collaborated with Microsoft to integrate Intel® Neural Compressor into Olive, enabling developers to easily take advantage of model compression techniques in their deployment platform, including Intel processors and accelerators.
On-Device Training: Efficient training on the edge with ONNX Runtime
ONNX Runtime is a high-performance cross-platform inference and training engine that can run a variety of machine learning models. ORT provides an easy-to-use experience for the AI developers to run models on multiple hardware and software platforms.
High-performance deep learning in Oracle Cloud with ONNX Runtime
In this blog post, we’ll share challenges our team faced, and how ONNX Runtime solves these as the backbone of success for high-performance inferencing.
Performant on-device inferencing with ONNX Runtime
The team at Pieces shares the problems and solutions evaluated for their on-device model serving stack and how ONNX Runtime enables their success.
Improve BERT inference speed by combining the power of Optimum, OpenVINO™, ONNX Runtime, and Azure
Make large models smaller and faster with OpenVino Execution Provider, NNCF and ONNX Runtime leveraging Azure Machine Learning.
Hugging Face Transformers now enabled in Apache OpenNLP by ONNX Runtime
We’re excited to share the recent integration of ONNX Runtime in Apache OpenNLP! Apache OpenNLP is a Java machine learning library for natural language processing (NLP) tasks.
Live demos of machine learning models with ONNX and Hugging Face Spaces
Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are all reasons why you may find yourself wanting to quickly run inference on a model.