Debugging and Profiling
The worst AI bugs don't crash. They train silently on garbage and report a beautiful loss curve. Language: Python Use conditional breakpoint() and debugprint to inspect tensor shapes, dtypes, and NaN values mid-training. Profile training loops with cProfile, lineprofiler, and tracemalloc to find bottlenecks. Detect common AI bugs: shape mismatches, NaN loss, data leakage, and wrong-device tensors. Set up TensorBoard to visualize loss curves, weight histograms, and gradient distributions. AI code fails differently than regular code. A web app crashes with a stack trace. A misconfigured training loop runs for 8 hours, burns $200 in GPU time, and produces a model that predicts the mean of every input. The code never errored. The bug was a tensor on the wrong device, a forgotten .detach(), or labels leaking into features. You need debugging tools that catch these silent failures before they waste your time and compute. AI debugging operates at three levels: Most people jump straight to level 3 (staring at TensorBoard). But 80% of AI bugs live at levels 1 and 2. Print debugging gets dismissed. It shouldn't. For tensor code, a targeted print statement beats stepping through a debugger because you need to see shapes, dtypes, and value ranges all at once. Call this after every suspicious operation. When the bug is found, remove the prints. Simple. The built-in…
Debugging and Profiling: The worst AI bugs don't crash. They train silently on garbage and report a beautiful loss curve. Language: Python
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