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Use when asked to deploy, install, or rebuild Slurm on one or more GPU servers with this repository.\n---\n\n# Deploy a Slurm GPU cluster\n\n## Preconditions\n\n- Ubuntu 22.04\u002F24.04 or RHEL\u002FRocky 8\u002F9 hosts you may fully manage (driver\n  installs may reboot them; no active users or workloads).\n- SSH access from the provisioning machine to every host as a sudo-capable\n  user.\n- Run everything from the repository root.\n\n## Procedure\n\n1. Prepare the environment and verify it:\n\n   ```bash\n   git submodule update --init --recursive\n   .\u002Fscripts\u002Fsetup.sh\n   cp -r config.example config\n   ```\n\n2. Edit `config\u002Finventory`: put the controller under `[slurm-master]` and\n   compute nodes under `[slurm-node]` (a single machine can be both). Set\n   the connection user in `[all:vars]` if not root.\n\n3. Preflight — must pass before deploying:\n\n   ```bash\n   python3 scripts\u002Fvalidation\u002Fdeepops_doctor.py --remote --json\n   ```\n\n   Fix anything in `failures` (each check's `detail` says how) and rerun.\n\n4. Deploy:\n\n   ```bash\n   ansible-playbook -l slurm-cluster playbooks\u002Fslurm-cluster.yml\n   ```\n\n   This installs NVIDIA drivers, builds and configures Slurm, and sets up\n   munge, NFS, and node health checks. Expect roughly 30–60 minutes on a\n   first run.\n\n5. Validate on a cluster node — the success signal is this, not the play\n   recap:\n\n   ```bash\n   python3 scripts\u002Fvalidation\u002Fvalidate_slurm.py --json\n   ```\n\n   Require `\"ok\": true` with `gpu_job_ok: true` and\n   `nodes_unavailable: 0`.\n\n## Failure branches\n\n- **Playbook fails on a transient error** (mirror timeout, apt lock,\n  network blip): rerun the same playbook; it is idempotent. 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Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[583,586],{"name":584,"slug":585,"type":15},"CI\u002FCD","ci-cd",{"name":13,"slug":14,"type":15},"2026-07-14T05:25:59.97109",{"slug":589,"name":589,"fn":590,"description":591,"org":592,"tags":593,"stars":574,"repoUrl":575,"updatedAt":599},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[594,595,596],{"name":584,"slug":585,"type":15},{"name":13,"slug":14,"type":15},{"name":597,"slug":598,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":601,"name":601,"fn":602,"description":603,"org":604,"tags":605,"stars":574,"repoUrl":575,"updatedAt":611},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[606,607,608],{"name":520,"slug":521,"type":15},{"name":597,"slug":598,"type":15},{"name":609,"slug":610,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":613,"name":613,"fn":614,"description":615,"org":616,"tags":617,"stars":574,"repoUrl":575,"updatedAt":624},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[618,621],{"name":619,"slug":620,"type":15},"Best Practices","best-practices",{"name":622,"slug":623,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":626,"name":626,"fn":627,"description":628,"org":629,"tags":630,"stars":574,"repoUrl":575,"updatedAt":638},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[631,634,637],{"name":632,"slug":633,"type":15},"Machine Learning","machine-learning",{"name":635,"slug":636,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":640,"name":640,"fn":641,"description":642,"org":643,"tags":644,"stars":574,"repoUrl":575,"updatedAt":649},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[645,646],{"name":537,"slug":538,"type":15},{"name":647,"slug":648,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":651,"name":651,"fn":652,"description":653,"org":654,"tags":655,"stars":574,"repoUrl":575,"updatedAt":658},"mcore-run-on-slurm","launch distributed training jobs on SLURM","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[656,657],{"name":13,"slug":14,"type":15},{"name":18,"slug":19,"type":15},"2026-07-14T05:25:49.362534",{"slug":660,"name":660,"fn":661,"description":662,"org":663,"tags":664,"stars":574,"repoUrl":575,"updatedAt":672},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[665,668,669],{"name":666,"slug":667,"type":15},"Code Review","code-review",{"name":597,"slug":598,"type":15},{"name":670,"slug":671,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":674,"name":674,"fn":675,"description":676,"org":677,"tags":678,"stars":574,"repoUrl":575,"updatedAt":681},"mcore-testing","run and manage Megatron-LM tests","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[679,680],{"name":537,"slug":538,"type":15},{"name":647,"slug":648,"type":15},"2026-07-14T05:25:54.928983",{"slug":683,"name":683,"fn":684,"description":685,"org":686,"tags":687,"stars":574,"repoUrl":575,"updatedAt":692},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[688,691],{"name":689,"slug":690,"type":15},"Automation","automation",{"name":584,"slug":585,"type":15},"2026-07-30T05:29:03.275638",496]