mcore-build-and-dependency

name	mcore-build-and-dependency
description	Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.
license	Apache-2.0
when_to_use	Adding, removing, or updating a dependency; editing pyproject.toml or uv.lock; uv.lock merge conflict; setting up a dev environment; pulling or building the CI container; container build errors; uv errors; 'how do I install', 'uv sync fails', 'ModuleNotFoundError'.
metadata	{"author":"Oliver Koenig <[e-mail]>"}

Build & Dependency Guide

The core principle: build and develop inside containers — the CI container ships the correct CUDA toolkit, PyTorch build, and pre-compiled native extensions (TransformerEngine, DeepEP, …) that cannot be reproduced on a bare host.

Answer-First Constants

For text-only dependency or container questions, give these repo-specific facts up front before the longer workflow:

• Run dependency work inside the Megatron-LM CI container, not on the host.
• The container venv is /opt/venv, already on PATH.
• Default dev uses docker/.ngc_version.dev and the dev uv group; lts uses docker/.ngc_version.lts and the lts uv group. The container::lts PR label selects the LTS path; otherwise CI uses dev.
• lts is opt-in only when the user explicitly asks for it. It is the older long-term-support base, not a routine second lane — never attach container::lts, build the LTS image, or run the lts uv group on your own initiative, not even for a container or dependency change.
• Install commands inside the container: uv sync --locked --group dev --group test, uv sync --locked --only-group linting, or uv sync --locked --group lts --group test.
• Dependency edits use uv add <package> followed by uv lock, both inside the container.
• docker/Dockerfile.ci.dev has main and jet stages. The jet stage needs an internal secret; local/public builds should pass --target main.

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Why Containers

Megatron-LM depends on CUDA, NCCL, PyTorch with GPU support, TransformerEngine, and optional components like ModelOpt and DeepEP. Installing these on a bare host is fragile and hard to reproduce. The project ships Dockerfiles that pin every dependency.

Use the container as your development environment. This guarantees:

• Identical CUDA / NCCL / cuDNN versions across all developers and CI.
• uv.lock resolves the same way locally and in CI.
• GPU-dependent operations (training, testing) work out of the box.

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dev vs lts

Two image variants exist, each with its own Dockerfile, selected by the container::lts PR label. The defining difference is the base container: dev tracks the latest NGC PyTorch release, while lts ("long-term support") pins the previous, still-supported NGC PyTorch/CUDA release. container::lts exists to verify a change still works on that older base — the dependency differences below follow from it, they are not the point.

Variant	Base image pin	Dockerfile	Where deps live	When used
dev	docker/.ngc_version.dev (latest NGC release)	docker/Dockerfile.ci.dev	pyproject.toml dev extra (uv-resolved)	Default — CI, local development, most PRs
lts	docker/.ngc_version.lts (older long-term-support release)	docker/Dockerfile.ci.lts	docker/lts/requirements.txt (pinned, sourced from main's uv.lock at AUT-479)	Backward-compat lane — verify the change still runs on the older NGC base; extras not carried on it (ModelOpt, the CUDA-13 TransformerEngine build) are dropped

LTS deps used to live in [project.optional-dependencies].lts in pyproject.toml. They were moved into docker/lts/requirements.txt so pyproject.toml can host meaningful module-level extras without colliding with the LTS pin set. To bump an LTS dependency, edit the version in docker/lts/requirements.txt and rebuild docker/Dockerfile.ci.lts.

Use dev for everything. lts is off-limits unless the user explicitly asks for it. CI runs dev by default, and that is the only variant you touch on your own initiative. Treat container::lts as a high barrier, not a fallback: do not attach the label, build docker/Dockerfile.ci.lts, or run the lts uv group unless the user has explicitly requested LTS validation — not even for a container or dependency change. When they do ask, container::lts verifies the change still works on the older long-term-support PyTorch/CUDA base that LTS users run. The @pytest.mark.flaky_in_dev marker skips tests in the dev environment; @pytest.mark.flaky skips them in lts.

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Step 1 — Acquire an Image

Option A — NVIDIA-internal: pull a CI-built image

⚠️ Requires access to the internal GitLab instance. See @tools/trigger_internal_ci.md for setup (adding the git remote, obtaining a token).

The internal GitLab CI publishes images to its container registry. Derive the registry host from your configured gitlab remote — the same host you use for trigger_internal_ci.py:

# Derive host from your 'gitlab' remote:
GITLAB_HOST=$(git remote get-url gitlab | sed 's/.*@\(.*\):.*/\1/')

docker pull ${GITLAB_HOST}/adlr/megatron-lm/mcore_ci_dev:main

Option B — Build from scratch (works for everyone)

⚠️ Dockerfile.ci.dev has two stages: main and jet. The jet stage requires an internal build secret and will fail without it. Always pass --target main to stop at the public stage.

# dev image (default)
docker build \
  --target main \
  --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev) \
  --build-arg IMAGE_TYPE=dev \
  -f docker/Dockerfile.ci.dev \
  -t megatron-lm:local .

# lts image (uses a dedicated Dockerfile; no IMAGE_TYPE arg)
docker build \
  --target main \
  --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.lts) \
  -f docker/Dockerfile.ci.lts \
  -t megatron-lm:local-lts .

Which image variant is used is controlled by the PR label container::lts; absent that label, dev is used.

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Step 2 — Launch the Container

Option A — Local Docker runtime

docker run --rm --gpus all \
  -v $(pwd):/workspace \
  -w /workspace \
  megatron-lm:local \
  bash -c "<your command>"

Option B — Slurm cluster (for those without a local Docker runtime)

NVIDIA clusters typically use Pyxis + enroot. Request an interactive session:

srun \
  --nodes=1 --gpus-per-node=8 \
  --container-image megatron-lm:local \
  --container-mounts $(pwd):/workspace \
  --container-workdir /workspace \
  --pty bash

For clusters that require a .sqsh archive first:

enroot import -o megatron-lm.sqsh dockerd://megatron-lm:local
srun \
  --nodes=1 --gpus-per-node=8 \
  --container-image $(pwd)/megatron-lm.sqsh \
  --container-mounts $(pwd):/workspace \
  --container-workdir /workspace \
  --pty bash

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Dependency Management

Dependencies are declared in pyproject.toml. The venv lives at /opt/venv inside the container (already on PATH).

All uv operations must be run inside the container. Never run uv sync / uv pip install on the host.

uv Dependency Groups

Group	Purpose
training	Runtime training extras
dev	Full dev environment (TransformerEngine, ModelOpt, …)
test	pytest, coverage, nemo-run
linting	ruff, black, isort, pylint
build	Cython, pybind11, nvidia-mathdx

The previous lts extra has been emptied. LTS deps are pinned in docker/lts/requirements.txt rather than pyproject.toml. Do not add new packages under [project.optional-dependencies].lts.

Install commands (inside the container):

# Full dev + test environment
uv sync --locked --group dev --group test

# Linting only
uv sync --locked --only-group linting

The LTS environment is reproduced by building docker/Dockerfile.ci.lts end-to-end; there is no uv sync-only equivalent because the LTS deps no longer live in pyproject.toml. The LTS top-level pin set is in docker/lts/requirements.txt; bump versions there and rebuild the image.

Several dependencies are sourced directly from git (TransformerEngine, nemo-run, FlashMLA, Emerging-Optimizers, nvidia-resiliency-ext). The locked uv.lock file pins exact revisions; update it with uv lock when changing pyproject.toml.

Adding a New Dependency

Follow this three-step workflow:

1. Acquire a container image — see Step 1 above.

2. Launch the container interactively — see Step 2 above.

3. Update the lock file inside the container, then commit it:

   # Inside the container:
   uv add <package>          # adds to pyproject.toml and resolves
   uv lock                   # regenerates uv.lock
   # Exit the container, then on the host:
   git add pyproject.toml uv.lock
   git commit -S -s -m "build: add <package> dependency"
   

Resolving a merge conflict in uv.lock

uv.lock is machine-generated; never resolve conflicts manually. Instead:

git checkout origin/main -- uv.lock   # take main's version as the base
# then inside the container:
uv lock                               # re-resolve on top of your pyproject.toml changes

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Common Pitfalls

Problem	Cause	Fix
uv sync --locked fails	Dependency conflict or stale uv.lock	Re-run uv lock inside the container and commit updated lock
ModuleNotFoundError after pip install	pip installed outside the uv-managed venv	Use uv add and uv sync, never bare pip install
uv: command not found inside container	Wrong container image	Use the megatron-lm image built from Dockerfile.ci.dev
No space left on device during uv ops	Cache fills container's /root/.cache/	Mount a host cache dir via -v $HOME/.cache/uv:/root/.cache/uv
docker build fails with secret-related error	Dockerfile.ci.dev has a jet stage that requires an internal secret	Add --target main to stop before the jet stage
access forbidden when pulling	Registry URL includes an explicit port (e.g. :5005)	Use ${GITLAB_HOST}/adlr/... with no port — the sed extracts the hostname only