accelprof-knowledge

accelprof-knowledge

An MCP server that answers "the profile says X — what do I do about it?" for NVIDIA GPU and AWS Neuron.

It serves a small set of curated tuning playbooks, each written as symptom → likely cause → what to check → what to try, with links to the upstream vendor documentation. An agent (or you) driving the profile → analyze → improve loop searches for a symptom and gets back the relevant playbook.

The corpus is small and hand-written, so search is a transparent keyword rank — not an embedding index that would need building and tuning.

It needs no external services — no accelerator, no cloud account, no cluster; its only runtime dependencies are the mcp SDK and PyYAML.

Install and run

pip install accelprof-knowledge
accelprof-knowledge-mcp            # streamable-http on MCP_PORT (default 8080)

Register it with any MCP client — for example Claude Code:

claude mcp add --transport http accelprof-knowledge http://127.0.0.1:8080/mcp

Two optional environment variables: MCP_PORT sets the listen port (default 8080), and KNOWLEDGE_ROOT points the server at your own playbook directory instead of the packaged one.

Tools, resources, and prompt

// search_knowledge("memory bound but occupancy is high", chip="gpu")
{ "query": "memory bound but occupancy is high", "chip": "gpu", "count": 2,
  "results": [
    { "id": "gpu/roofline", "chip": "gpu", "score": 12.0,
      "title": "Roofline diagnosis — compute-bound vs memory-bound vs latency-bound",
      "snippet": "The roofline places a kernel by its arithmetic intensity (FLOPs per byte)…" },
    { "id": "gpu/memory-and-fusion", "chip": "gpu", "score": 7.0, "title": "…", "snippet": "…" }
  ] }

Resources knowledge://index (a browsable list) and knowledge://{chip}/{slug} (each playbook) expose the same content to clients that prefer resources, and the diagnose(chip, symptom) prompt primes an agent to search → read → propose one change.

Content

knowledge_mcp/playbooks/
  common/   methodology · metrics glossary
  gpu/      roofline · memory-and-fusion · tensor-cores-and-occupancy
  neuron/   utilization (MFU/MBU/HFU) · dma-and-collectives · compile-and-capture

Each file is Markdown with a small front matter (id, chip, title, tags, refs) and is shipped inside the wheel. Add a playbook by dropping a new .md under the right chip directory, with no code change; it is loaded at startup and becomes searchable and browsable. The playbooks are in English; search with English keywords.

Testing

pip install -e ".[test]"
python -m pytest knowledge_mcp/ -q

The suite covers front-matter parsing, list/get/search, duplicate-id detection, and that every packaged playbook loads and is well-formed.

Hosting

Dockerfile is a reference image that installs the package and runs the console script. It assumes no orchestrator; hosting this MCP alongside others is a separate concern handled by a deployment repo (distributed-ai), not a dependency of this one.

Related projects

• accelprof — an experiment store plus an analysis MCP that maps a run to its profile files and analyzes them. Pair it with this one so a finding leads to a next step. When hosting both, give each its own MCP_PORT.
• The official MLflow MCP — run discovery and search.

Contributing & license

Playbooks are the main contribution surface: add a Markdown file under knowledge_mcp/playbooks/<chip>/ with the front matter above, keep it distilled (symptom → cause → what to check → what to try), and link the upstream source rather than copying it. Code changes should keep python -m pytest knowledge_mcp/ -q green.

Licensed under the Apache License 2.0 — see LICENSE.