The Hype Around Distributed Training: Unnecessary Redundancy in Models
Despite its widespread adoption and touted benefits, I firmly believe that distributed training often overkills the model, leading to an explosion of redundant parameters that hinder performance rather than enhance it. 🤯 By pruning and fine-tuning locally trained models, we can achieve similar results with significantly reduced computational overhead, making them more efficient, scalable, and easier to maintain.
The Problem with Distributed Training
Distributed training distributes the workload across multiple machines, accelerating the training process. However, this comes at a cost. As models become larger and more complex, they tend to develop redundant parameters – weights that don't contribute significantly to the model's performance. These redundant parameters not only increase the model's size, but also its computational requirements, making it more challenging to deploy and maintain.
**The Pow...
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