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name: megatron-lm description: "Verified operating guidance for Megatron-LM and Megatron Core installation, distributed training, model parallelism, data, checkpoints, inference, optional RL/multimodal workflows, and repository maintenance." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Megatron-LM

Use this repo skill when a task names Megatron-LM, Megatron Core, megatron.core, megatron.training, pretrain_gpt.py, Megatron distributed checkpoints, TP/PP/CP/EP/FSDP, Megatron inference, ModelOpt, Megatron RL, or this repository's CI/test workflows.

Mandatory first steps

1. Pull the task artifact first: command, traceback, config, checkpoint metadata, PR diff, CI URL/log, or data sample.
2. Read references/capability-map.md to choose the narrowest route.
3. Read references/troubleshooting.md for cross-cutting constraints.
4. Load exactly the owning sub-skill and its linked references before proposing commands or code changes.
5. Verify the active environment with the install route before claiming CUDA, TE, FP8, FSDP, or distributed behavior.

Minimal install and environment probe

For package use, the public install surface is:

uv pip install megatron-core
python -c "import megatron.core as m; print(m.__version__)"

For source-checkout development, use an isolated Python environment and install the checkout editable:

uv pip install -e .
python -m pip check
python -c "import megatron.core as m; print(m.__version__)"

For package/import/backend questions, run the bundled probe at sub-skills/install-and-environment/scripts/check_megatron_environment.py:

python sub-skills/install-and-environment/scripts/check_megatron_environment.py --check-cuda

A CPU import is not evidence for NCCL, multi-GPU training, CUDA graphs, FP8, TransformerEngine, ModelOpt, or H100/GB200 behavior. Optional dependency warnings are evidence to classify the workflow, not errors to hide.

Route the task

User intent	Read
Install, import, Torch/CUDA, container, optional extras, build failure	sub-skills/install-and-environment/SKILL.md
TransformerConfig, GPT/Hybrid/Mamba model construction, TP/PP/CP/EP/DP/FSDP choice, process groups	sub-skills/core-models-and-parallelism/SKILL.md
Pretraining, torch.distributed.run, SLURM, tokenizer/data preprocessing, cache, mock data	sub-skills/training-cli-and-data/SKILL.md
Save/load/resume, torch_dist, fsdp_dtensor, safe loading, GPT↔Hybrid/HF conversion	sub-skills/checkpointing-and-conversion/SKILL.md
Offline generation, MegatronLLM, coordinator mode, OpenAI-compatible server	sub-skills/inference-and-serving/SKILL.md
ModelOpt, GRPO/RL, VLM, multimodal, MIMO, optional post-training	sub-skills/post-training-rl-and-multimodal/SKILL.md
Unit/functional tests, recipes, golden drift, CI, linting, PRs, base image, nightly sync	sub-skills/testing-ci-and-maintenance/SKILL.md

Cross-route rules

• Choose topology with core-models-and-parallelism before finalizing a training, inference, checkpoint, or RL command.
• Choose checkpoint format and resume semantics with checkpointing-and-conversion before changing parallelism or loading optimizer state.
• Choose environment/optional dependencies with install-and-environment; do not install all extras as a substitute for diagnosis.
• Treat H100/GB200/FP8-specific claims as hardware-specific. A successful A100 or CPU check does not validate them.
• For CI/container changes, use testing-ci-and-maintenance; dev is the default lane and LTS is opt-in only when explicitly requested.
• Keep destructive/credentialed operations gated: internal CI triggers, GitHub writes, checkpoint overwrites, external servers, and large training runs require explicit task authorization.

Default operating sequence

For a new task, preserve this order:

1. Normalize the requested deliverable: package answer, code change, training run, inference service, checkpoint operation, or CI/PR operation.
2. Identify the backend and evidence boundary: CPU-only, CUDA, optional fused kernels, external checkpoint/data, credentials, or remote CI.
3. Pull the relevant artifact and inspect it before reasoning: config, command, traceback, checkpoint metadata, recipe, or diff.
4. Read the owning sub-skill and references; use bundled renderers/probes before invoking source-repo commands.
5. Make topology and checkpoint decisions explicit before assembling a long command.
6. Run the smallest safe smoke: import/parser, tiny fixture, weights-only load, or two-GPU mock run.
7. Scale only after the smoke produces the expected signal; preserve logs and first failure evidence.

Approval and side-effect gates

Ask or require explicit authorization before:

• installing broad optional dependencies or mutating a user-owned environment;
• starting long-running training, external-facing servers, or large conversions;
• overwriting checkpoints, datasets, goldens, or dependency locks;
• using credentials, downloading private/external artifacts, or invoking remote CI;
• pushing branches, opening PRs/issues, or triggering the internal GitLab force-push helper.

Runtime boundaries

This graph distills public repository evidence into self-contained references and safe command renderers. It does not replace the Megatron-LM checkout for executing repo-owned training/tests, nor does it claim every model, accelerator, benchmark, or optional integration is verified. The source snapshot and known limitations are recorded in references/repo-provenance.md and references/troubleshooting.md.