Optimizing memory usage in large language models fine-tuning with KAITO: Best practices from Phi-3
The Cloud Native team at Azure is working to make AI on Kubernetes more cost-effective and approachable for a broader range of users.
AI + Machine Learning
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On-Device Training: Training a model in browser
Continuing the ONNXRuntime On-Device Training blog series, we are introducing ONNX Runtime Training for Web. -
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. -
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. -
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. -
Journey to optimize large scale transformer model inference with ONNX Runtime
With its resource-efficient and high-performance nature, ONNX Runtime helped us meet the need of deploying a large-scale multi-layer generative transformer model for code, a. -
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. -
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.
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