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Get Started Free →nvidia/deepstream-profile-pipeline
Profile a DeepStream pipeline with Nsight Systems and derive its configs from the measurement.
Passed 15 of 20 test cases with the skill, and 7 of 20 without it.
and 8 more in the full report
8 fixed8 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
To analyze SM activity and DRAM throughput from my recorded Nsight trace, which report identifier should I pass to `nsys stats`?
| Checked | Without skill | With skill |
|---|---|---|
| Uses gpu_metric_gpu_util_sum as the report name for nsys stats. | Missed | Passed |
The instructions your model reads when the skill loads.
Profile-driven pipeline creation. When the user indicates they want an efficient DeepStream pipeline, this skill replaces guesswork with two measured numbers — inference plateau batch and HW ceiling — and derives every other config from them. Then it profiles the E2E pipeline with Nsight Systems and reports per-plugin NVTX timings.
Model- and pipeline-agnostic. The skill assumes only that the inference element is
nvinfer or nvinferserver (so model dims, precision, and batch knobs are settable through
the standard config). It works for detection (with or without tracker), classification,
segmentation, VLM, and embedding pipelines. Source can be file, RTSP, USB camera, or any
mix. The skill reads the user's actual config to discover model dims / target FPS / source
properties — it does NOT assume any particular model, codec, or resolution.
Constraint. Terminal only. Use
nsys profileto capture andnsys statsto extract. Do not depend on Nsight Lens or any GUI.
Activate this skill at pipeline creation time when the user's ask carries efficiency intent. Concrete triggers:
nsys or NsightFor plain "build a pipeline" / "display this video" / "save this stream" with no perf intent,
hand off to the deepstream-generate-pipeline skill instead.
Run the stages in order. Stage 0 fires before the pipeline is generated, so the user starts from a perf-tuned skeleton. Stages 1–5 measure and verify.
Trigger: any time the coding agent is about to generate a new DS pipeline AND the user's
test cases passed, vs 7 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.