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Distributed Training on Ray using PyTorch

Delve into the process of distributed training on Ray utilizing PyTorch. Viewers will learn how to set up parallel training tasks, where each worker independently trains a separate instance of a model. The video is based on Rafay's comprehensive Getting Started Guide, which provides a step-by-step overview of aggregating trained parameters from multiple workers. Join us as we demonstrate the training of four independent instances of a simple PyTorch model, leveraging Ray's powerful distributed capabilities. #DistributedTraining #PyTorch #Ray https://docs.rafay.co/aiml/mlops-ray/... This trains four independent instances of a simple PyTorch model using Ray’s distributed capabilities, running each model training on a separate Ray worker.