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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.js, with improvements such as a more consistent developer...Read more

Introducing Distributed Data Parallel support on PyTorch Windows 

Model training has been and will be in the foreseeable future one of the most frustrating things machine learning developers face. It takes quite a long time and people can’t really do anything about it. If you have the luxury (especially at this moment of time) of having multiple GPUs, you are likely to find...Read more

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. Today, we are excited to announce a preview version of ONNX Runtime in...Read more

Microsoft Open Source success story—Babylon 

An ongoing series of stories about Microsoft people and projects making their world better through open source. If you haven’t heard of Babylon.js, there is no doubt that it’s already made your day more cheerful, powering Microsoft Teams’ Reactions‘ (those cute floating emojis), or your presentation faster and smoother as the engine that powers rendering...Read more

Accelerate and simplify Scikit-learn model inference with ONNX Runtime 

Scikit-learn is one of the most useful libraries for general machine learning in Python. To minimize the cost of deployment and avoid discrepancies, deploying scikit-learn models to production usually leverages Docker containers and pickle, the object serialization module of the Python standard library. Docker is a good way to create consistent environments and pickle saves...Read more

Introducing the Cluster API Provider for Azure (CAPZ) for Kubernetes cluster management 

Managing Kubernetes clusters is hard. Managing Kubernetes clusters at scale across a variety of infrastructures is—well—even harder. The Kubernetes community project Cluster API (CAPI) enables users to manage fleets of clusters across multiple infrastructure providers. The Cluster API Provider for Azure (CAPZ) is the solution for users who need to manage Kubernetes clusters on Azure...Read more

ONNX Runtime scenario highlight: Vespa.ai integration 

Since its open source debut two years ago, ONNX Runtime has seen strong growth with performance improvements, expanded platform and device compatibility, hardware accelerator support, an extension to training acceleration, and more. We are excited by its broad usage in production, powering more than a hundred models across Microsoft products and services and bringing concrete...Read more

Adding RoBERTa NLP to the ONNX model zoo for natural language predictions 

In summer 2019, I worked as a high school intern for the ONNX AI team at Microsoft and loved working on various projects with the team, including the BERT text classification model. However, due to Covid-19, the Microsoft Internship Program for high school students was canceled in the summer of 2020. This led two other...Read more

Introducing ONNX Runtime mobile – a reduced size, high performance package for edge devices 

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. Today, we are excited to announce ONNX Runtime release v1.5 as part of our AI at Scale initiative. This release includes ONNX Runtime mobile, a new feature targeting smartphones and other...Read more

Accelerate traditional machine learning models on GPU with ONNX Runtime 

With the growing trend towards deep learning techniques in AI, there are many investments in accelerating neural network models using GPUs and other specialized hardware. However, many models used in production are still based on traditional machine learning libraries or sometimes a combination of traditional machine learning (ML) and DNNs. We’ve previously shared the performance...Read more

Announcing Dapr integration in Azure API Management Service 

Dapr integration in the Azure API Management (APIM) service is now available. This new capability enables operations teams to directly expose Dapr microservices as APIs and make those APIs discoverable and easily consumable by developers with proper controls across multiple Dapr deployments—whether in the cloud, on-premises, or on the edge. Since its initial release last...Read more