To minimize data transfer bottlenecks during training in an AI project, what approach should be employed?

Increasing the batch size to reduce the number of data transfers between the CPU and GPU is an effective strategy for minimizing data transfer bottlenecks during training in an AI project. When the batch size is larger, the model processes more data at once in a single forward and backward pass. This reduces the frequency of transfers between the CPU and the GPU because fewer, larger batches are sent during the training process.

By optimizing how often data is transferred, it helps in achieving more efficient GPU utilization, as the GPU can perform computations without interruption for new data. This efficiency can lead to improved training times and overall performance as the model is less frequently paused for data loading.

Though other strategies can also be relevant, such as the use of multiple GPUs, they may not specifically address the frequent bottlenecks caused by data transfer between the CPU and GPU. Larger batch sizes provide a direct and straightforward method to tackle this issue by reducing the number of times data must be sent for processing.