During deep learning training, a typical performance mistake is transferring the computed loss for every minibatch from the GPU back to the main memory to report it on the command line or log it in a NumPy ndarray. This frequent cross-device data movement triggers Python's Global Interpreter Lock (GIL), which stalls all GPUs and causes a significant drop in training efficiency. To mitigate this overhead, a much more efficient strategy is to allocate memory for logging directly inside the GPU and only transfer larger, aggregated logs to the CPU at less frequent intervals.