What is the best action to optimize GPU utilization and reduce data loading bottlenecks during deep learning training?

Upgrading to a faster storage solution like NVMe SSDs is indeed the best action to optimize GPU utilization and reduce data loading bottlenecks during deep learning training. In deep learning, GPU performance can be significantly hindered by how quickly data can be loaded and provided to the GPU for processing. NVMe SSDs offer much higher data transfer rates compared to traditional hard drives or even SATA SSDs, which allows for faster access to training data. This reduction in data loading time ensures that the GPUs remain fed with data, maximizing their utilization and minimizing idle time.

While enabling data augmentation can help improve the robustness of the model by providing more varied training data, it does not directly address the speed at which data can be loaded onto the GPU. Similarly, reducing the batch size could mitigate memory issues, but this may also result in lower GPU utilization as the GPUs could finish processing batches faster than data can be loaded. Increasing the number of GPUs might enhance throughput, but it would not solve the underlying issue of data loading speed and could lead to other inefficiencies if the data transfer remains a bottleneck. Therefore, the optimal approach is indeed to enhance the storage speed, ensuring a steady flow of data to the GPUs.