NVIDIA/omniperf

Perfil de NVIDIA

Última actividad de origen registrada: 14 de mayo de 2026 a las 01:41

Catálogo de SkillsMP actualizado: 17 de octubre de 2026 a las 00:09

skills recopiladas: 13

Estrellas en GitHub: 34
Forks en GitHub: 6

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# Omniperf — Performance AI Skills and Dashboards for Omniverse, Isaac Sim, and Isaac Lab Omniperf bundles two things for performance work across [Omniverse](https://developer.nvidia.com/omniverse), [Isaac Sim](https://developer.nvidia.com/isaac/sim), and [Isaac Lab](https://github.com/isaac-sim/IsaacLab): 1. **Performance AI Skills** — a library of Claude Code agent skills that install, benchmark, profile, and diagnose Kit-based applications. See [Agent Skills](#agent-skills) below. 2. **Performance Dashboards** — a static site that tracks benchmark results (FPS, GPU utilization, memory, startup times) across GPUs and historical CI runs. **Live dashboard:** <https://nvidia.github.io/omniperf/> ## Overview The **dashboards** are a lightweight, zero-dependency static site driven by JSON benchmark data generated by internal CI/CD pipelines and committed to this repo. The **AI skills** live under [`.agents/skills/`](.agents/skills/) and encode the install, benchmark, and profile-analysis workflows used to produce and interpret that data. ### Dashboard features - **Multi-GPU comparison** — switch between GPU datasets (e.g. NVIDIA L40, RTX PRO 6000) - **Overview tab** — sparkline grid of all benchmarks grouped by workflow (Non-RL, RL Games, RSL RL) - **Detail tab** — full time-series charts, per-run summaries, and an all-benchmarks overview table - **Search & filter** — autocomplete search to quickly find specific benchmarks - **Metric selection** — explore runtime metrics (FPS, GPU/CPU utilization, memory) and startup metrics ## Getting Started ### Local Development ```bash git clone https://github.com/NVIDIA/omniperf.git cd omniperf ./serve.sh # Open http://localhost:8765 ``` Requires Python 3 (for the built-in HTTP server). No build step or dependencies needed. > **Note:** The default clone skips the benchmark preview images (~1.5 GB) to keep clones lightweight. To also download them, run `git lfs pull --exclude=""` after cloning. ### Data Format Benchmark data lives in `docs/data/`. The `manifest.json` file lists available GPU datasets: ```json { "generated_at": "2026-03-04T10:47:26Z", "datasets": [ { "file": "ada_l40.json", "gpu_display_name": "NVIDIA L40", "total_runs": 228 } ] } ``` Each GPU data file contains an array of benchmark runs with commit metadata, runtime metrics, and startup metrics. ### Adding New Data 1. Generate the benchmark JSON using the [omniperf](https://github.com/NVIDIA/omniperf) tooling 2. Place the JSON file in `docs/data/` 3. Update `docs/data/manifest.json` to reference the new file 4. Commit and push — GitHub Pages will automatically deploy ## Deployment The site is deployed automatically via GitHub Actions to GitHub Pages on every push to `main`. The workflow: 1. Checks out the repo (with LFS) 2. Uploads `docs/` as a Pages artifact 3. Deploys to GitHub Pages To enable GitHub Pages for a fresh clone: 1. Go to **Settings → Pages** in the GitHub repo 2. Under **Source**, select **GitHub Actions** ## Requirements - **Data files** are tracked with [Git LFS](https://git-lfs.github.com/) — install it before cloning - **Chart.js 4.4.7** is loaded from CDN (no npm install needed) - **Python 3** for local development server ## Agent Skills This repo ships a set of [Claude Code agent skills](.agents/skills/) for working with Omniverse, Isaac Sim, Isaac Lab, and their profiling tools. They encode install steps, benchmark recipes, and profile-analysis workflows validated from live testing. - [install-isaacsim](.agents/skills/install-isaacsim/SKILL.md) — install Isaac Sim via pip or source build - [install-isaaclab](.agents/skills/install-isaaclab/SKILL.md) — install Isaac Lab for Isaac Sim-backed or kit-less/Newton workflows - [install-profilers](.agents/skills/install-profilers/SKILL.md) — set up Nsight Systems, Tracy, and related tooling - [benchmark-isaacsim](.agents/skills/benchmark-isaacsim/SKILL.md) — run Isaac Sim benchmarks - [benchmark-isaaclab](.agents/skills/benchmark-isaaclab/SKILL.md) — run Isaac Lab RL and environment benchmarks - [profiling](.agents/skills/profiling/SKILL.md) — capture traces with Tracy and Nsight Systems - [profiling-api](.agents/skills/profiling-api/SKILL.md) — add profiling zones, metrics, and annotations to Kit code - [nsys-analyze](.agents/skills/nsys-analyze/SKILL.md) — analyze Kit-based `.nsys-rep` profiles and compare versions - [tracy-memory](.agents/skills/tracy-memory/SKILL.md) — profile CPU and GPU memory allocations in Tracy - [nvtx-python](.agents/skills/nvtx-python/SKILL.md) — trace Python functions with NVTX outside Kit/Carbonite - [diagnose-perf](.agents/skills/diagnose-perf/SKILL.md) — first-responder triage for slow FPS, stutter, or latency - [perf-tuning](.agents/skills/perf-tuning/SKILL.md) — apply guide-backed fixes for known performance bottlenecks - [kit-app-streaming-debug](.agents/skills/kit-app-streaming-debug/SKILL.md) — debug Kit app livestream bottlenecks using StreamSDK logs and browser WebRTC stats ## Security - Vulnerability disclosure: See `SECURITY.md` ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines, including the Developer Certificate of Origin (DCO) requirement. ## License This project is licensed under the [Apache License 2.0](LICENSE).

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Desarrolladores de software

Use when investigating Kit app livestream performance bottlenecks, WebRTC/native StreamSDK lag, freezes, dropped frames, browser WebRTC stats, copy fence timeouts, NVST_R_BUSY, disconnects, or resolution mismatch warnings in omni.kit.livestream.