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HelloWorldBeginner
GitHub creator profile

HelloWorldBeginner

Repository-level view of 10 collected skills across 1 GitHub repositories, including approximate occupation coverage.

skills collected
10
repositories
1
occupation fields
1
updated
2026-05-18
occupation focus
Major fields detected across this creator.
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Top repositories by collected skill count, with their share of this creator catalog and occupation spread.

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Repositories and representative skills

#001
Megatron-LM
10 skills00updated 2026-05-18
100% of creator
create-issue
ソフトウェア開発者

Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.

2026-05-18
bump-base-image
ソフトウェア開発者

Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:<YY.MM>-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker/.ngc_version.dev` and GitLab CI in `.gitlab/stages/01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs

2026-05-11
nightly-sync
ソフトウェア開発者

Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.

2026-05-05
build-and-dependency
ソフトウェア開発者

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.

2026-05-02
cicd
ソフトウェア開発者

CI/CD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI, and CI failure investigation.

2026-05-02
linting-and-formatting
ソフトウェア開発者

Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.

2026-05-02
onboard-gb200-1node-tests
ソフトウェア品質保証アナリスト・テスター

Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.

2026-05-02
run-on-slurm
ソフトウェア開発者

How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.

2026-05-02
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