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Mcore Onboard Gb200 1node Tests Agent Skill

Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.

4k tokens
context cost
the whole folder, loaded on every use
5
files
instructions only
0
copies elsewhere
how many repositories repackaged it
17319
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/NVIDIA/Megatron-LM --skill mcore-onboard-gb200-1node-tests

What comes with it

9 966 bytes besides the instruction
BENCHMARK.md
evals/evals.json
skill-card.md
skill.oms.sig

What it tells the agent to use

found in the instruction text
Edit edits files in place

The instruction itself

11 sections, as written by the author

Onboard GB200 1-Node GitHub MR Tests

Create 1-node (mr-github) variants of existing 2-node (mr-scoped) GB200 functional tests.

Each GB200 node has 4 GPUs. A 2-node test uses 8 GPUs total; the 1-node variant uses 4.


Background

GB200 functional tests live in tests/test_utils/recipes/gb200/:

| Recipe file | Notes |

|-------------|-------|

| gpt.yaml | GPT dense tests, nodes: 2, gpus: 4 (8 total) |

| moe.yaml | MoE tests, nodes: 2, gpus: 4 (8 total) |

| moe-1node.yaml | Existing 1-node MoE tests, nodes: 1, gpus: 4 (4 total) |

| gpt-1node.yaml | 1-node GPT tests (create if not present) |

Model configs live at:

tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml

1-node test cases use the _1node suffix:

tests/functional_tests/test_cases/{model}/{test_case}_1node/model_config.yaml


Workflow

Step 1 — Find candidate tests

Scan the products: block in gpt.yaml and moe.yaml for entries with scope: [mr, ...] or scope: [mr-slim, ...]. These are the 2-node tests that need 1-node mr-github counterparts.

Ignore tests already covered in *-1node.yaml files, and ignore nightly, weekly, mr-broken scopes.

Step 2 — Read each model config

For each candidate, read its model_config.yaml and extract the key parallelism arguments:

--tensor-model-parallel-size   (TP)
--pipeline-model-parallel-size (PP)
--expert-model-parallel-size   (EP)
--expert-tensor-parallel-size  (ETP)
--context-parallel-size        (CP)
--global-batch-size
--micro-batch-size

Step 3 — Classify: trivial copy vs. needs adaptation

The world size formula is: world_size = TP × PP × DP where DP ≥ EP.

Going from 8 GPUs → 4 GPUs:

| Condition | Action |

|-----------|--------|

| TP × PP ≤ 4 | Trivial copy. Config unchanged; DP is halved automatically. |

| TP × PP = 8 (e.g. tp4 pp2) | Reduce PP. Set PP = PP / 2 (e.g. pp2→1). Verify TP × PP_new ≤ 4. |

| EP > 4 (e.g. ep8 with tp1 pp1) | Reduce EP. Set EP = 4. Experts stay at num-experts (each EP rank holds more experts). |

| EP > 4 and TP × PP > 4 | Reduce both PP and EP as above. |

| ETP test (ep × etp ≤ TP × DP) | Check EP × ETP ≤ TP × DP_new after PP reduction. Usually satisfied when pp→1. |

Do not change GBS — let gradient accumulation absorb the reduced DP.

Step 4 — Create _1node model config directories

# Trivial copy
mkdir -p tests/functional_tests/test_cases/{model}/{test_case}_1node
cp tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml \
   tests/functional_tests/test_cases/{model}/{test_case}_1node/model_config.yaml

# Then apply any parallelism changes (EP or PP) with Edit tool

Step 5 — Create or update recipe files

For GPT tests — create tests/test_utils/recipes/gb200/gpt-1node.yaml (if absent) by cloning gpt.yaml's spec block with nodes: 1. Use this template for the spec:

type: basic
format_version: 1
maintainers: [mcore]
loggers: [stdout]
spec:
  name: "{test_case}_{environment}_{platforms}"
  model: gpt          # or moe
  build: mcore-pyt-{environment}
  nodes: 1
  gpus: 4
  n_repeat: 5
  platforms: dgx_gb200
  script_setup: |    # copy verbatim from gpt.yaml / moe.yaml
    ...
  script: |-         # copy verbatim from gpt.yaml / moe.yaml
    ...

For MoE tests — append entries to the existing moe-1node.yaml.

Step 6 — Add products entries

Scope convention:

  • 1–2 most representative tests per recipe: scope: [mr-github, mr-github-slim]
  • All other tests: scope: [mr-github]
products:
  - test_case: [<test_case>_1node]
    products:
      - environment: [dev]
        scope: [mr-github, mr-github-slim]   # or [mr-github]
        platforms: [dgx_gb200]

Quick parallelism reference

| Original (8 GPUs) | 1-node config (4 GPUs) | Notes |

|-------------------|----------------------|-------|

| tp1 pp1 ep1 → dp8 | tp1 pp1 ep1 → dp4 | trivial |

| tp2 pp1 ep1 → dp4 | tp2 pp1 ep1 → dp2 | trivial |

| tp1 pp2 ep1 → dp4 | tp1 pp2 ep1 → dp2 | trivial |

| tp4 pp1 ep1 → dp2 | tp4 pp1 ep1 → dp1 | trivial |

| tp1 pp4 ep1 → dp2 | tp1 pp4 ep1 → dp1 | trivial |

| tp1 pp1 ep8 → dp8 | tp1 pp1 ep4 → dp4 | ep 8→4 |

| tp4 pp2 ep2 etp2 → dp1 | tp4 pp1 ep2 etp2 → dp1 | pp 2→1 |


Checklist

  • [ ] Identified all mr-scoped tests in gpt.yaml and moe.yaml not yet in *-1node.yaml
  • [ ] Read model config for each candidate
  • [ ] Classified trivial vs. adaptation needed
  • [ ] Created _1node/model_config.yaml for each test
  • [ ] Applied EP or PP reductions where needed
  • [ ] Created/updated recipe YAML with nodes: 1, gpus: 4
  • [ ] Assigned mr-github scope (+ mr-github-slim for 1–2 representative tests per recipe)
  • [ ] Verified no mr-github-slim overload (slim suite should stay small)

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