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.
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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. -
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. -
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. -
ONNX Runtime 1.8: mobile, web, and accelerated training
The V1.8 release of ONNX Runtime includes many exciting new features. -
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. -
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. -
Announcing ONNX Runtime 1.0
One year after ONNX Runtime’s initial preview release, we’re excited to announce v1. -
Now available: ONNX Runtime 0.5 with support for edge hardware acceleration
ONNX Runtime 0.5, the latest update to the open source high performance inference engine for ONNX models, is now available.
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