Recommenders joins LF AI & Data as new Sandbox project
LF AI & Data Foundation announced Recommenders as its latest Sandbox project.
LF AI & Data Foundation announced Recommenders as its latest Sandbox project.
ONNX models can be accelerated with ONNX Runtime, which works cross-platform and provides coverage for many cloud and language models.
ONNX Runtime harnesses Intel® AMX to accelerate performance for the 4th Gen Intel® Xeon® CPUs.
KEDA reduces the complexity of infrastructure autoscaling, making it simpler to configure, manage, and secure the application auto-scaler.
ONNX Script is a new open-source library for directly authoring ONNX models in Python.
Using ONNX Runtime to unlock the promise of developments in science for solving real world problems.
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.
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.
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.
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.
Today, we are excited to announce the much-anticipated availability of the OSS Feathr 1.0.
To celebrate FOSS Fund #25 we have invited all employees whose projects were not selected in past FOSS Fund to propose a project for a one-time $500.00 USD award. We expect this to result in over 50 projects receiving this microgrant for a total of $25,000 USD.