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, ...]

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

bash

# 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:

yaml

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]
```

yaml

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)

onboard-gb200-1node-tests

NPX Install

Tags

SKILL.md Content