Distributed training is essential due to the increasing demand for processing larger data sets. Data parallelism involves splitting datasets across multiple GPUs to enhance training speed. Model ...

Multi-GPU tensor / context parallel diffusion on AMD ROCm — with the patch that makes it actually work. Companion repo: For the single-GPU AMD stack (5 torchao + diffusers backport patches that bring ...

Deep Neural Networks (DNNs) have facilitated tremendous progress across a range of applications, including image classification, translation, language modeling, and video captioning. DNN training is ...

Model Parallelism has two types: Inter-layer and intra-layer. We note Inter-layer model parallelism as MP, and intra-layer model parallelism as TP (tensor parallelism). some researchers may call TP ...

Concurrency and parallelism are two techniques for managing multiple tasks in a program, but they operate differently. Understanding the distinction between them in Python helps developers write ...