Metadata-Version: 2.4
Name: ckg-nvidia-ai
Version: 0.1.0
Summary: NVIDIA AI developer stack as a traversable knowledge graph — 20 CKGs, 998 nodes, MCP-native
Project-URL: Homepage, https://graphifymd.com
Project-URL: Repository, https://github.com/Yarmoluk/ckg-nvidia-ai
Project-URL: Bug Tracker, https://github.com/Yarmoluk/ckg-nvidia-ai/issues
Author-email: Daniel Yarmoluk <daniel.yarmoluk@gmail.com>
License: MIT
Keywords: ai-agents,cuda,knowledge-graph,llm,mcp,nemo,nim,nvidia,tensorrt
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Requires-Dist: mcp[cli]>=1.0.0
Description-Content-Type: text/markdown

# 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.*
