Faster PyTorch Training by Reducing Peak Memory (combining backward pass + optimizer step)

Before   After   The curse of OOM One of the main challenges in training multi-billion parameter models is dealing with limited GPU memory while training. In fact, getting out-of-memory (OOM) errors is arguably one of the bummers of every practitioner. During training, there are several sets of tensor data to keep in memory, which... Read more »

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