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Scaling PyTorch Training with Meta’s FairScale
The framework includes some of the most popular techniques for parallelizing the training of neural networks.
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Large neural networks are the norm in the modern deep learning space. Training such large models requires not only a lot of computation power but complex concurrency and parallelization techniques. As a result, we are seeing an emergence of frameworks that attempt to streamline the parallel training of deep learning models. FairScale is a relatively new project in this area that was originally incubated by Meta and eventually open sourced.
FairScale is a PyTorch-based library that combines multiple approaches to scale and parallelize…
