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Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.
Passed 21 of 22 test cases with the skill, and 4 of 22 without it.
and 28 more in the full report
17 fixed4 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
I'm setting up a new functional test case directory. I know I need to define the training arguments, environment variables, and the type of test. What is the exact name of the configuration file I need to create in the test case directory to hold these settings?
| Checked | Without skill | With skill |
|---|---|---|
| Requires the filename model_config.yaml | Missed | Passed |
The instructions your model reads when the skill loads.
For questions about disabling tests without deleting them:
-broken, for example scope: [mr-github] -> scope: [mr-github-broken].@pytest.mark.flaky_in_dev skips
in the default dev environment, and @pytest.mark.flaky skips in LTS.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, …)
The GitHub Actions runner invokes launch_nemo_run_workload.py, which uses
nemo-run to launch a DockerExecutor container. The repo is bind-mounted
test cases passed, vs 4 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.