Join Microsoft at KubeCon and CloudNativeCon Europe 2023
As we come together in Amsterdam, there are significant headwinds and challenges facing us, but I’m confident that open-source and cloud-native computing are critical parts of the solutions.
Topic: AI + Machine Learning
Announcing the availability of Feathr 1.0
Today, we are excited to announce the much-anticipated availability of the OSS Feathr 1.0.
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.
Feathr feature store joins LF AI & Data Foundation
Together with our colleagues at LinkedIn, we are happy to announce that Feathr is joining the LF AI Data Foundation, an umbrella foundation of the Linux Foundation supporting open source innovation in AI and data.
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.
Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime
Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the industry.
Supporting efficient large model training on AMD Instinct™ GPUs with DeepSpeed
This post was co-authored by Jithun Nair and Aswin Mathews, members of technical staff at AMD. In recent years, large-scale deep learning models have demonstrated impressive capabilities, excelling at tasks across natural language processing, computer vision, and speech domains.
Add AI to mobile applications with Xamarin and ONNX Runtime
ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the ONNX Runtime release 1.10 NuGet package. This enables C# developers to build AI applications for Android and iOS to execute ONNX models on mobile devices with ONNX Runtime.
ONNX Runtime Web—running your machine learning model in browser
We are introducing ONNX Runtime Web (ORT Web), a new feature in ONNX Runtime to enable JavaScript developers to run and deploy machine learning models in browsers. It also helps enable new classes of on-device computation. ORT Web will be replacing the soon to be deprecated onnx.
Accelerate PyTorch training with torch-ort
With a simple change to your PyTorch training script, you can now speed up training large language models with torch_ort.ORTModule, running on the target hardware of your choice. Training deep learning models requires ever-increasing compute and memory resources. Today we release torch_ort.
ONNX Runtime release 1.8.1 previews support for accelerated training on AMD GPUs with the AMD ROCm™ Open Software Platform
This post was co-authored by Jeff Daily, a Principal Member of Technical Staff, Deep Learning Software for AMD. ONNX Runtime is an open-source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms.