ckg-nvidia-ai

# ckg-nvidia-ai **The NVIDIA AI developer stack as a traversable knowledge graph.** 20 CKGs · 998 nodes · MCP-native · 4× F1 of RAG · 11× fewer tokens · auditable by design --- Instead of sending an AI agent to scan thousands of pages of NVIDIA documentation, give it a graph it can traverse. Every concept, every dependency, every connection — declared, typed, and queryable in ~269 tokens per question. ``` NIM → TensorRT-LLM → quantization → FP8 precision → Hopper SM90 requirement ``` The agent calls `query_ckg()` and gets that chain. Not a summary. The actual dependency path. --- ## What's inside | Domain | Description | |---|---| | `nvidia-nim` | NVIDIA Inference Microservices — deployment, scaling, speculative decoding | | `nvidia-nemo` | NeMo framework — training, PEFT, guardrails, evaluation | | `nvidia-tensorrt-triton` | TensorRT-LLM + Triton Inference Server — quantization, batching, KV cache | | `nvidia-cuda-toolkit` | CUDA compiler, PTX, memory hierarchy, Hopper/Blackwell features | | `nvidia-cuda-x-libraries` | cuBLAS, cuDNN, cuFFT, NCCL, Thrust — the acceleration layer | | `nvidia-hpc-sdk` | OpenACC, OpenMP, CUDA Fortran, multi-GPU scaling | | `nvidia-omniverse` | Universal Scene Description, simulation, digital twins | | `nvidia-isaac` | Isaac Lab + Isaac Sim — robot learning, sensor simulation | | `nvidia-cosmos` | Physical AI world foundation models — video generation, tokenization | | `nvidia-drive` | Autonomous vehicle stack — perception, planning, safety validation | | `nvidia-jetson` | Edge AI platform — Orin NX, AGX, DeepStream, Holoscan | | `nvidia-clara` | Healthcare AI — MONAI, Parabricks genomics, BioNeMo, Holoscan SDK | | `nvidia-metropolis` | Intelligent video analytics — VLMs, TAO Toolkit, DeepStream | | `nvidia-riva` | Speech AI — ASR, TTS, NLP pipelines, streaming | | `nvidia-gameworks` | Graphics R&D — DLSS, RTX, PhysX, Reflex | | `nvidia-developer-tools` | Nsight, CUPTI, Compute Sanitizer, profiling stack | | `nvidia-graphics-research` | Research graphics — neural rendering, path tracing, differentiable rendering | | `nvidia-ai-enterprise` | Enterprise AI platform — NIM blueprints, governance, fleet management | | `nvidia-developer-ecosystem` | Cross-cutting: NGC, DGX, Inception, AgentIQ, MCP integration | | `nvidia-openshell` | Agent sandbox runtime — policy enforcement, CVEs, authorization gaps | --- ## Install ```bash pip install ckg-nvidia-ai ``` Or run without installing: ```bash uvx ckg-nvidia-ai ``` --- ## Use as MCP Server ### Claude Desktop ```json { "mcpServers": { "nvidia-ai": { "command": "uvx", "args": ["ckg-nvidia-ai"] } } } ``` ### Cursor / other MCP clients Same config — substitute `uvx` with `python -m ckg_nvidia_ai` if you prefer a venv install. --- ## Tools ### `list_domains()` Returns all 20 NVIDIA AI domains. Start here. ### `search_concepts(query, domain)` Find concepts by keyword within a domain. ``` search_concepts("speculative decoding", "nvidia-nim") → Speculative Decoding [Optimization] Draft Model [Component] KV Cache [Infrastructure] ``` ### `query_ckg(concept, domain, depth=3)` Traverse the graph from a concept — see what it requires and what depends on it. ``` query_ckg("TensorRT-LLM", "nvidia-tensorrt-triton", 3) → ## TensorRT-LLM · nvidia-tensorrt-triton Type: Framework ### Prerequisites (what you need first) - CUDA Toolkit - CUDA Driver API - cuBLAS - Hopper SM90 Architecture - FP8 / FP4 Quantization ### Builds toward - Triton Inference Server - NIM Microservice Runtime ``` ### `get_prerequisites(concept, domain)` Full ordered prerequisite chain — everything to understand or install first. ``` get_prerequisites("Isaac Lab", "nvidia-isaac") → Isaac Lab → Isaac Sim → USD Composer → Omniverse Kit → ... ``` --- ## How it works Each domain is a typed dependency graph stored as CSV: ``` ConceptID, ConceptLabel, Dependencies, TaxonomyID 1, TensorRT-LLM, "", Framework 2, CUDA Toolkit, "", Platform 3, FP8 Quantization, "2", Optimization 4, Hopper SM90, "2", Architecture 5, Speculative Decoding, "1|4", Optimization ``` When an agent queries a concept, the server runs BFS traversal over declared edges. The answer is composed entirely of traversed relationships — not probabilistic inference, not RAG retrieval, not token prediction over documentation. **The graph doesn't guess. It traverses.** --- ## Benchmark Built on the [KRB Benchmark v0.6.2](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf): | System | F1 | Tokens/query | Cost | |---|---|---|---| | **CKG** | **0.471** | **269** | **$7.81/1K** | | RAG | 0.123 | 2,982 | $76.23/1K | | GraphRAG | 0.120 | — | — | ~4× F1 · 11× fewer tokens · auditable by design --- ## Related - **[ckg-mcp](https://pypi.org/project/ckg-mcp/)** — 97 domains across all topics (includes NVIDIA + science, finance, law, healthcare, and more) - **[KRB Benchmark](https://huggingface.co/datasets/danyarm/ckg-benchmark)** — open benchmark dataset - **[graphifymd.com](https://graphifymd.com)** — CKG catalog and Context-as-a-Service --- *Built by [Graphify.md](https://graphifymd.com). Patent pending.*