Gain better insights while training ML models with SageMaker Debugger
summary
SageMaker Debugger captures weights, gradients and activations with zero-code change and uploads snapshots (default every 500 steps) to S3 while a separate rule container runs built-in checks like dead ReLU, loss not decreasing and class imbalance. smdebug lets notebooks fetch tensors as NumPy arrays for histograms; real-time profiler samples system metrics at 500 ms, correlates GPU/CPU/I/O, and outputs a profiling report that may recommend larger batch size or auto-terminate bad jobs (example: training stops ~1500 steps, halving run time).
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chapters (10)
- 0:00Introduction to debugging
- 1:30ML lifecycle debugging needs
- 4:29SageMaker Debugger overview
- 8:28Real-time monitoring with smdebug
- 11:33Demo use cases overview
- 13:03Built-in rules for training
- 16:59Performance profiling with Debugger
- 21:55Profiling report insights
- 26:14Actions and auto-termination
- 27:43Resources and conclusion
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