Applications & models - Page 16#
Explore the latest blogs about applications and models in the ROCm ecosystem, including machine learning frameworks, AI models, and application case studies.
Image classification using Vision Transformer with AMD GPUs
Image classification using Vision Transformer with AMD GPUs
Retrieval Augmented Generation (RAG) using LlamaIndex
Retrieval Augmented Generation (RAG) using LlamaIndex
Total body segmentation using MONAI Deploy on an AMD GPU
Total body segmentation using MONAI Deploy with ROCm
Automatic mixed precision in PyTorch using AMD GPUs
In this blog, we will discuss the basics of AMP, how it works, and how it can improve training efficiency on AMD GPUs. As models increase in size, the time and memory needed to train them--and consequently, the cost--also increases. Therefore, any measures we take to reduce training time and memory usage can be highly beneficial. This is where Automatic Mixed Precision (AMP) comes in.
Large language model inference optimizations on AMD GPUs
LLM Inference optimizations on AMD Instinct (TM) GPUs
Building a decoder transformer model on AMD GPU(s)
Building a decoder transformer model
Question-answering Chatbot with LangChain on an AMD GPU
Question-answering Chatbot with LangChain
Music Generation With MusicGen on an AMD GPU
Music Generation With MusicGen on an AMD GPU
Efficient image generation with Stable Diffusion models and ONNX Runtime using AMD GPUs
Efficient image generation with Stable Diffusion models and ONNX Runtime using AMD GPUs
Simplifying deep learning: A guide to PyTorch Lightning
Simplifying deep learning: A guide to PyTorch Lightning
Two-dimensional images to three-dimensional scene mapping using NeRF on an AMD GPU
Two-dimensional images to three-dimensional scene mapping using NeRF on an AMD GPU