How to Speed Up PyTorch Model Training

The tutorial fine-tunes the pretrained DistilBERT language model for binary sentiment classification on 50,000 IMDB movie reviews, partitioned into training, validation, and test sets, using full-model fine-tuning rather than feature extraction or partial-layer tuning. A plain PyTorch implementation trains for three epochs on an A100 GPU in 21.33 minutes and achieves 89.92% test accuracy, with validation results indicating slight overfitting after the second epoch. Recasting the workflow with PyTorch Lightning’s LightningModule and Trainer simplifies training management, logging, reproducibility, and checkpointing, allowing evaluation of the model with the best validation accuracy and yielding 92.6% test accuracy in 21.79 minutes. Automatic mixed precision reduces this runtime to 8.25 minutes while maintaining approximately 93.2% accuracy, and four-GPU distributed data parallel training cuts it further to 3.07 minutes with 93.1% accuracy. The discussion also compares memory-oriented multi-GPU options such as DeepSpeed sharding and ZeRO strategies, noting that they are particularly useful when larger models cannot fit on individual GPUs. Finally, Lightning Fabric is presented as a lightweight alternative that requires only small changes to ordinary PyTorch code and completes the same four-GPU mixed-precision fine-tuning task in 1.8 minutes, while the Trainer is positioned as better suited to larger projects requiring more built-in structure, callbacks, and logging.

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