# deepstream-profile-pipeline

Profiles DeepStream pipelines with Nsight Systems to tune configs from benchmark data. Helps optimize batch sizes and locate bottlenecks without guessing.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 750 installs · ★ 3735
- Author: NVIDIA CORPORATION
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/deepstream-profile-pipeline/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/deepstream-profile-pipeline

## What it does

The skill configures and benchmarks video analytics pipelines running on DeepStream SDK 9.0. It starts by applying presets to `nvinfer` or `nvinferserver`, setting memory to NVMM, and selecting INT8 or FP16 precision. Afterwards, it triggers headless runs using `nsys profile` and `nsys stats` to collect execution metrics.

No graphical interface is needed. Profiling requires the `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` development image on Ubuntu 22.04 or 24.04. The slim `samples-multiarch` container lacks the NVTX injector, which causes per-plugin traces to return empty data. Both `nsys` (version 2024+) and `nvidia-smi` must be accessible in your PATH.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill deepstream-profile-pipeline -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/deepstream-profile-pipeline/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/deepstream-profile-pipeline/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill deepstream-profile-pipeline -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/deepstream-profile-pipeline/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill deepstream-profile-pipeline -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/deepstream-profile-pipeline/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills deepstream-profile-pipeline
```

2. **Manually: put the files in place**
- File: `.github/skills/deepstream-profile-pipeline/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill deepstream-profile-pipeline -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/deepstream-profile-pipeline
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/deepstream-profile-pipeline/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill deepstream-profile-pipeline -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/deepstream-profile-pipeline/SKILL.md

## When to use it
- benchmark DeepStream pipelines using Nsight Systems
- tune batch sizes for nvinfer and nvstreammux
- detect decoder and inference bottlenecks with NVTX
- configure buffer queues and nvtracker presets

## Example prompts
- Build an efficient DeepStream pipeline for 8 RTSP streams
- Profile this pipeline with nsys and locate the performance bottlenecks
- Optimize batch size and nvstreammux settings for maximum FPS

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