---
name: ascend-profiling-collection
description: Collect one Ascend torch-profiler case end-to-end on a workspace-managed remote NPU container. Starts a profiled vLLM service, brackets a workload with /start_profile and /stop_profile, runs analyse() (db export by default), verifies the per-rank ascend_pytorch_profiler_*.db landed, and writes a manifest the analysis skill can consume. Use for requests like "采集 profiling", "torch profiler 跑一个 case", "采一份 profile 出来", "采 profiling 给我分析". Do not use for pure performance benchmarking, HBM/memory profiling, or for analysing already-collected profiling data (that is the analysis skill's job).
---

# ascend-profiling-collection

Collect one torch-profiler case, bracket a real workload, export per-rank data and return an analysis-ready manifest.

Choose a capture window and workload that expose the suspected bottleneck. Keep token counts and concurrency representative. For multimodal cases pass the local image and target height; encoding is platform-independent.

## Agent entry

Run from the repository root using the platform's Python launcher. The workspace
selects its installed platform environment automatically.

```text
python .agents/skills/ascend-profiling-collection/scripts/collect_torch_profile_case.py --model /models/example --served-model-name example --tp 1 --tag case --mode enforce_eager --request-kind text --benchmark-output-tokens 128
```

The workflow starts or observes the managed service, controls /start_profile and /stop_profile, runs analyse(), verifies expected rank outputs and records workload success. DB export is the default. Large traces stay near the data; the resulting manifest can be passed directly to analysis.

Use profiling-analysis for existing traces, memory-profiling for HBM attribution, and benchmark for throughput measurements without tracing.

Read the relevant detail only when needed:

- [behavior](references/behavior.md)