Summary
A new PyTorch sampler, 'dynabatch', was developed to address Out-of-Memory (OOM) errors and low GPU utilization encountered when fine-tuning large encoder-decoder models like NLLB-200. The problem stems from fixed batch sizes being dictated by the longest sequences, leading to inefficient GPU usage for shorter examples. Dynamic batching aims to optimize training by adjusting batch sizes based on sequence length, thereby preventing OOM and improving overall GPU efficiency.
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