Mcore Testing
NVIDIAOfficialCreated by Philip PetrakianDistributed by NVIDIA
- Skill
- Guidelines
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to testing within the Megatron-LM ecosystem. It details the directory layout for unit and functional tests, explains the YAML-based recipe system for CI integration, and outlines procedures for local execution, debugging, and managing test lifecycle, including handling flaky tests and golden value updates.
What it does
- Defines the directory structure for unit and functional test suites.
- Explains the YAML recipe format used for defining and scoping test workloads.
- Provides commands for executing tests locally using torch.distributed.run.
- Outlines the process for adding new unit and functional tests.
- Describes how to manage flaky tests using pytest markers and scope suffixes.
- Details the workflow for downloading and committing golden values for functional tests.
When to use it
- When adding new unit or functional tests to the Megatron-LM codebase.
- When debugging test failures or reproducing CI issues locally.
- When modifying test recipes or updating golden values after code changes.
- When needing to temporarily disable a test without deleting its configuration.
Advantages
- Ensures consistency between local development and CI environments.
- Provides clear patterns for handling flaky tests without blocking pipelines.
- Simplifies the process of adding new test cases through standardized YAML recipes.
- Offers specific guidance on multi-rank testing and distributed execution.
Usage examples
- “How do I run a single unit test file locally?”
- The user is provided with the specific torch.distributed.run command structure required to execute a single test file with the necessary GPU configuration.
- “How can I temporarily disable a functional test in the CI?”
- The user is instructed to suffix the scope in the recipe YAML with -broken to skip the test while preserving the configuration for future use.
- “What is the process for updating golden values after a code change?”
- The user is guided to run the test, then use the download_golden_values.py script with the appropriate pipeline ID to fetch and commit the new values.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/mcore-testing/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip mcore-testing.zip -d .agents/skills
Documentation by Philip Petrakian
Testing Guide
Answer-First Testing Facts
For questions about disabling tests without deleting them:
Functional recipe entries stay in YAML; disable by suffixing scope with
-broken, for examplescope: [mr-github]->scope: [mr-github-broken].Unit-test skips use pytest markers instead:
@pytest.mark.flaky_in_devskips in the default dev environment, and@pytest.mark.flakyskips in LTS.Do not delete the test case or recipe entry when the goal is discoverability and easy re-enable.
Test Layout
tests/
├── unit_tests/ # pytest, 1 node × 8 GPUs, torch.distributed runner
├── functional_tests/ # end-to-end shell + training scripts
│ └── test_cases/
│ └── {model}/{test_case}/
│ ├── model_config.yaml # training args
│ └── golden_values_{env}_{platform}.json
└── test_utils/
├── recipes/
│ ├── h100/ # YAML recipes for H100 jobs
│ └── gb200/ # YAML recipes for GB200 jobs
└── python_scripts/ # helpers (recipe_parser, golden-value download, …)How Tests Execute
The GitHub Actions runner invokes launch_nemo_run_workload.py, which uses
nemo-run to launch a DockerExecutor container. The repo is bind-mounted
at /opt/megatron-lm; training data is mounted at /mnt/artifacts.
Unit tests are dispatched through torch.distributed.run:
Ranks 0 and 3 are tee-d to stdout; all other ranks write only to log files.
Per-rank log files land at
{assets_dir}/logs/1/and are uploaded as a GitHub artifact after the run.
Functional tests are driven by
tests/functional_tests/shell_test_utils/run_ci_test.sh. Only rank 0 runs the
pytest validation step; training output from all ranks is uploaded as an artifact.
Flaky-failure auto-retry: launch_nemo_run_workload.py retries up to
3 times for known transient patterns (NCCL timeout, ECC error, segfault,
HuggingFace connectivity, …) before declaring a genuine failure.
Recipe YAML Structure
Recipes live in tests/test_utils/recipes/ and are parsed by
tests/test_utils/python_scripts/recipe_parser.py. Each file expands a
cartesian products block into individual workload specs:
type: basic
format_version: 1
maintainers: [mcore]
loggers: [stdout]
spec:
name: "{test_case}_{environment}_{platforms}"
model: gpt # maps to tests/functional_tests/test_cases/{model}/
build: mcore-pyt-{environment}
nodes: 1
gpus: 8
n_repeat: 5
platforms: dgx_h100
time_limit: 1800
script_setup: |
...
script: |-
bash tests/functional_tests/shell_test_utils/run_ci_test.sh ...
products:
- test_case: [my_test]
products:
- environment: [dev, lts]
scope: [mr-github]
platforms: [dgx_h100]Key runtime placeholders: {assets_dir}, {artifacts_dir}, {test_case},
{environment}, {platforms}, {n_repeat}.
Disabling a Test Without Deleting It
To temporarily disable a test case in a recipe YAML, suffix its scope value
with -broken — do not delete the entry:
# before (test runs in CI)
scope: [mr-github]
# after (test is skipped; entry preserved for easy re-enable)
scope: [mr-github-broken]Running Unit Tests Locally
All unit tests initialize a torch.distributed group, so every invocation
requires GPU access and must go through torch.distributed.run:
# Full suite
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
tests/unit_tests
# Single file
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
tests/unit_tests/models/test_gpt_model.py
# Single test
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
tests/unit_tests/models/test_gpt_model.py::TestGPTModel::test_constructor
# Filter by name substring
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
tests/unit_tests -k optimizerMarker filters
# Exclude flaky tests during development
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
tests/unit_tests -m "not flaky and not flaky_in_dev"
# Include experimental tests
uv run python -m torch.distributed.run --nproc-per-node 8 -m pytest -q \
tests/unit_tests --experimentalCI parity
Use tests/unit_tests/run_ci_test.sh to reproduce a CI bucket failure exactly.
For ad-hoc runs, prefer the direct torch.distributed.run invocations above.
Gotchas
pyproject.tomlsetsaddopts = --durations=15 -s -rA— stdout is not captured (-s), so ranks interleave during multi-rank runs. Override with--capture=fdwhen debugging a specific rank.tests/unit_tests/conftest.pylooks for test data under/opt/dataand attempts a download if missing. Supply it manually or skip data-dependent tests when running outside the canonical container.
Adding a Unit Test
Create
tests/unit_tests/<category>/test_<name>.py.Use fixtures from
tests/unit_tests/conftest.py.Apply markers as needed:
@pytest.mark.internal— skipped onlegacytag@pytest.mark.flaky_in_dev— skipped indevenvironment (CI default; use this to disable a flaky test without blocking the standard pipeline)@pytest.mark.flaky— skipped inltsenvironment@pytest.mark.experimental—latesttag only
Verify locally (see Running Unit Tests Locally above).
If the test needs a dedicated CI bucket, add an entry to
tests/test_utils/recipes/h100/unit-tests.yaml.
Adding a Functional / Integration Test
Create
tests/functional_tests/test_cases/<model>/<test_name>/.Write
model_config.yamlwithMODEL_ARGS,ENV_VARS, andTEST_TYPE.Add a YAML recipe under
tests/test_utils/recipes/h100/(andgb200/if needed). Required fields:scope,environment,platform,n_repeat,time_limit.Push the PR, add the label "Run functional tests" to trigger a full run.
After a successful run, download golden values:
python tests/test_utils/python_scripts/download_golden_values.py \ --source github --pipeline-id <run-id>Commit the downloaded golden values.
Common Pitfalls
| Problem | Cause | Fix |
|---|---|---|
| Test passes locally but fails in CI | Different environment or data path | Check DATA_PATH, DATA_CACHE_PATH, and the environment tag (dev vs lts) |
| Golden value mismatch after a code change | Numerical regression | Download new golden values via download_golden_values.py after a clean run |
cicd-integration-tests-gb200 not triggered | GB200 jobs require maintainer status | Ask a maintainer to trigger, or add the Run functional tests label |
Files
- ATTRIBUTION.mdAdded by AutoGineer907 B
- BENCHMARK.md2.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md7.3 KiB
- evals/evals.json3 B
- skill-card.md2.5 KiB
- skill.oms.sig4.5 KiB
LICENSE
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python, Needs Node.js
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Philip Petrakian
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/mcore-testing · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Philip Petrakian or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download mcore-testing.zip