Optimize Training Performance#12
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Implements several standard PyTorch optimizations to address a severe performance bottleneck in the training script. - Enables cuDNN autotuning (`benchmark=True`, `deterministic=False`) for faster GPU kernel selection. - Configures the DataLoader for high-performance GPU training by: - Setting `num_workers` to a reasonable maximum. - Enabling `pin_memory` for faster CPU-to-GPU data transfers. - Using `persistent_workers` to avoid worker respawn overhead between epochs. - Adds the missing `os` import to the training script. - Cleans up the debug configuration to be non-destructive and avoid conflicting settings.
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This submission optimizes the PyTorch
DataLoaderand enables cuDNN autotuning to resolve a critical performance bottleneck in the training script. After encountering and debugging a series of silent crashes caused by native library conflicts, the final solution incorporates robust data loading practices and corrected configuration files. The changes significantly improve training speed and stability, aligning with the user's goal of faster experimentation.Fixes #
PR created automatically by Jules for task 5891443029588383019 started by @Vishal-sys-code