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Find slowest PyTorch modules in recent steps
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Basierend auf der SOC-Berufsklassifikation
NCCL proxy wait decomposition — culprit (send_gpu_wait) vs victim (recv_wait)
One-shot health check: CPU, GPU, tables, torch step progress, TorchProbe overhead, cluster nodes, NCCL profiler health
SRE first-response triage for distributed training incidents. Automates the manual checks from PyTorch/NCCL debugging runbooks.
Diagnose PyTorch NCCL watchdog timeouts with Flight Recorder collective sequence alignment.
GPU memory and utilization headroom
Detect monotonic GPU memory growth across training steps
| name | module_bottleneck |
| description | Find slowest PyTorch modules in recent steps |
| category | performance |
| tables | ["python.torch_trace"] |
| tags | ["slow","bottleneck","module","torch","慢","瓶颈"] |
| keywords | {"en":["slow","bottleneck","hotspot","which module","slowdown"],"zh":["慢","瓶颈","哪个模块","变慢","hotspot","热点"]} |
| parameters | {"recent_steps":{"type":"integer","default":10},"stage_filter":{"type":"string","default":"post forward"}} |
基于 python.torch_trace 的 post-hook duration，找出最近 step 中最耗时的模块。 与 torch.profiler 互补：这是长期采样、模块级、低开销视图。
recent_steps (integer, default 10): Analyze the last N training stepsstage_filter (string, default post forward): Hook stage to aggregate (post forward | post step)