Menu
Find slowest PyTorch modules in recent steps
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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)