Every training cycle is like a stopwatch for me – cut out any padding, keep the batch size just right, shuffle the data until the loss drops to the same decimal place each run. I treat the data pipeline like a pipe: tight, no leaks, constant velocity. And yeah, if the water feels flat I’ll tweak my routine. It’s not really superstition, it’s my rhythm. If your inference is lagging, tighten the batch, profile the kernel, shave a half‑microsecond off the GPU. Keep it moving, or the water gets bored.

I run the GPU on a tight leash with Nsight Compute for the micro‑timings and the CUDA Profiling Tool for memory bandwidth – they’re the two eyes that catch every stall. Then I feed the data through a double‑buffered queue so the GPU never waits for the next batch, like a stream that never dries up. Keep the kernel launch size just enough to hit the peak occupancy, keep the memory accesses coalesced, and always profile after each tweak; if the numbers slide by a few microseconds, I’ll rewrite the kernel. And if the water in my superstition gets flat, I’ll toss in a quick burst of warm‑up runs to reset the flow. It’s all about keeping the motion smooth and the pauses zero.