Is Running a deep learning neural network model on multiple GPUs in PyTorch a very simple process?
Running a deep learning neural network model on multiple GPUs in PyTorch is not a simple process but can be highly beneficial in terms of accelerating training times and handling larger datasets. PyTorch, being a popular deep learning framework, provides functionalities to distribute computations across multiple GPUs. However, setting up and effectively utilizing multiple GPUs
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Introduction, Introduction to deep learning with Python and Pytorch
How does data parallelism work in distributed training?
Data parallelism is a technique used in distributed training of machine learning models to improve training efficiency and accelerate convergence. In this approach, the training data is divided into multiple partitions, and each partition is processed by a separate compute resource or worker node. These worker nodes operate in parallel, independently computing gradients and updating