Microsoft Open Source Blog On-Device Training: Training a model in browser
Continuing the ONNXRuntime On-Device Training blog series, we are introducing ONNX Runtime Training for Web, a new feature in ONNX Runtime (ORT) that enables training models in the browser. Read more
ONNX Runtime Training
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. Read more
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. Read more
ONNX Runtime 1.8: mobile, web, and accelerated training
The V1.8 release of ONNX Runtime includes many exciting new features. This release launches ONNX Runtime machine learning model inferencing acceleration for Android and iOS mobile ecosystems (previously in preview) and introduces ONNX Runtime Web. Additionally, the release also debuts official packages for accelerating model training workloads in PyTorch. ONNX Runtime is a cross-platform runtime 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
GPT-2 fine-tuning with ONNX Runtime – a 34% speedup in training time
Model training is an important step when developing and deploying large scale Artificial Intelligence (AI) models. Training typically utilizes a large amount of compute resources to tune the model based on the input dataset. Transformer models, with millions and billions of parameters, are especially compute-intensive and training costs increase with model size and fine-tuning steps Read more