วิเคราะห์คอขวด

Before optimizing, find out where time actually goes. Guessing wastes effort, while a quick profile shows you the real slow spots.

Training usually stalls in one of two places: the GPU compute doing math, or the data pipeline feeding it. Knowing which one matters.

Start simple by timing a loop section with the clock. A rough perf_counter reading often points you to the right area in seconds.

CUDA runs in the background, so naive timers lie. Call synchronize first to make sure the GPU has truly finished before you read the clock.

For real detail, use the torch.profiler context manager. It records how long every operation takes on both CPU and GPU.

Run the part you care about inside a profile block. Choosing both CPU and CUDA activities captures the whole picture.

Print results sorted by cost to see the heaviest ops at the top. The key_averages table groups identical operations together.

If the GPU often sits idle waiting, your DataLoader is too slow. More workers or cached data usually fixes that gap.

If one matmul or conv dominates the table, the limit is raw compute. Mixed precision or a smaller model is the lever to pull.

The profiler can also report peak memory. Tracking profile_memory reveals which layers eat the most, guiding what to trim.

Optimization is a loop: profile, make one change, then profile again. Trust numbers, not hunches, to confirm a fix actually helped.

Your GPU often sits idle between batches. What is the likely bottleneck?

Profile before you tune: synchronize for honest timings, use torch.profiler to find the heaviest ops, then fix data or compute and measure again. 🔍