Web Reference: Mar 1, 2021 · DistributedOptimizer takes remote references to parameters scattered across workers and applies the given optimizer locally for each parameter. This class uses get_gradients() in order to retrieve the gradients for specific parameters. Unlike typical PyTorch optimizers (e.g. Adam / AdamW), Dion and Muon require separating your model's parameters into different groups (same in spirit as Modula). Jan 16, 2026 · One of the key challenges in distributed training is to ensure that the model weights are synchronized across all the participating devices or nodes. In PyTorch, there are several ways to achieve this synchronization, which we will explore in this blog.
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