[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-amc-run-sample-calibration":3,"mdc-j2tqj1-key":31,"related-repo-nvidia-amc-run-sample-calibration":4083,"related-org-nvidia-amc-run-sample-calibration":4178},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":29,"mdContent":30},"amc-run-sample-calibration","run AMC sample dataset calibration","Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,17],{"name":13,"slug":14,"type":15},"Data Analysis","data-analysis","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Testing","testing",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-17T05:29:03.913266","Apache-2.0",281,[],{"repoUrl":21,"stars":20,"forks":24,"topics":27,"description":28},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Famc-run-sample-calibration","---\nname: \"amc-run-sample-calibration\"\ndescription: \"Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.\"\nowner: \"NVIDIA CORPORATION\"\nservice: \"auto-magic-calib\"\nversion: \"1.0.0\"\nreviewed: \"2026-04-28\"\nlicense: \"Apache-2.0\"\nmetadata:\n  author: \"NVIDIA CORPORATION\"\n  tags: [amc, calibration, sample, rest-api, validation, python]\n---\n\n# Skill: Calibrate Sample Dataset\n\n## When to Use This Skill\n\nActivate this skill when the user wants to sanity-check a running AMC stack with the bundled sample dataset. Typical prompts:\n\n- \"test the sample dataset\" \u002F \"run sample calibration\"\n- \"verify AMC install\"\n- \"launch and test\" (chain with `amc-setup-calibration-stack` if the MS isn't already running)\n\n**Do NOT use this skill when:**\n\n- The user references their own video paths (e.g. `\u002Fdata\u002Fvideos\u002F`, `cam_*.mp4` not from the bundled zip) — route to `amc-run-video-calibration`.\n- The user provides live RTSP streams or `rtsp:\u002F\u002F...` URLs — route to `amc-run-rtsp-calibration`.\n- This skill is exclusively for `assets\u002Fsdg_08_2_sample_data_010926.zip`.\n\nPrerequisite: AMC microservice running on a port in 8000-8009. If no backend is detected, delegate to `amc-setup-calibration-stack` first.\n\nIf execution cannot proceed in the current environment (no backend, missing sample data, etc.), surface the blocker AND describe the expected workflow + API sequence concisely so the user understands what will run once prerequisites are met. Do not fabricate calibration outputs, evaluation metrics, or trajectories.\n\n## Overview\n\nRun a full calibration on the bundled sample dataset (`sdg_08_2_sample_data_010926.zip`, 4 synthetic warehouse cameras with ground truth) against a running AutoMagicCalib microservice. Useful for verifying that a freshly-launched stack works end-to-end before throwing real data at it.\n\nThe sample includes GT, so the run produces evaluation metrics (L2 distance, reprojection error) — no calibration parameter tuning needed.\n\n## Prerequisites\n\n- [ ] AMC microservice running (follow `skills\u002Famc-setup-calibration-stack\u002FSKILL.md` if not)\n- [ ] Sample zip present at `assets\u002Fsdg_08_2_sample_data_010926.zip`\n- [ ] Python 3 with `requests` available, or use the Swagger UI path below\n  - The bundled script self-heals: if `requests` is missing it creates a throwaway venv under `${TMPDIR:-\u002Ftmp}\u002Famc-sample-test-venv` (nothing written to the repo)\n  - If `python3 -m venv` itself fails with `ensurepip not available`: `sudo apt install -y python3-venv python3-pip`\n\n## Instructions\n\n**\"launch AMC and test sample dataset\" (or similar):**\n\n1. Run `skills\u002Famc-setup-calibration-stack\u002FSKILL.md` first.\n2. Wait for `\u002Fv1\u002Fready` to return OK.\n3. Extract sample data (snippet below) — idempotent, safe to re-run.\n4. Run the bundled script in [Run Script](#run-script).\n5. Report final metrics + UI URL for manual inspection.\n6. VGGT refinement is attempted by default when the project reports `vggt_state: READY`; otherwise the script explains that VGGT setup is optional and can be enabled later for refinement.\n\n**\"test sample dataset\" (MS already running):**\n\n1. Detect backend: scan ports 8000–8009 for a `\u002Fv1\u002Fready` response.\n2. If none → point to the setup skill.\n3. Extract sample data if not already cached.\n4. Run the bundled script.\n5. Report metrics.\n\n### Detect Running Backend\n\n```bash\nMS_PORT=\"\"\nfor port in {8000..8009}; do\n  if curl -s \"http:\u002F\u002Flocalhost:$port\u002Fv1\u002Fready\" | grep -q '\"code\":0'; then\n    MS_PORT=$port; break\n  fi\ndone\n[ -z \"$MS_PORT\" ] && { echo \"No running backend. Run amc-setup-calibration-stack skill first.\"; exit 1; }\necho \"Backend on port $MS_PORT\"\n```\n\n### Locate + Extract Sample Data (idempotent)\n\n```bash\n: \"${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}\"\ngrep -q \"AutoMagicCalib\" \"$REPO_ROOT\u002FREADME.md\" 2>\u002Fdev\u002Fnull && grep -q \"auto-magic-calib-ms\" \"$REPO_ROOT\u002Fcompose\u002Fms\u002Fcompose.yml\" 2>\u002Fdev\u002Fnull || { echo \"ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT\" >&2; exit 1; }\n\nSAMPLE_ZIP=\"$REPO_ROOT\u002Fassets\u002Fsdg_08_2_sample_data_010926.zip\"\n[ -f \"$SAMPLE_ZIP\" ] || { echo \"Sample zip not found at $SAMPLE_ZIP\"; exit 1; }\n\n# Cache directory next to the zip.\nSAMPLE_DIR=\"$(dirname \"$SAMPLE_ZIP\")\u002F.cache\u002Fsdg_08_2_sample_data_010926\"\n\nif [ ! -d \"$SAMPLE_DIR\" ]; then\n  mkdir -p \"$SAMPLE_DIR\"\n  unzip -q \"$SAMPLE_ZIP\" -d \"$SAMPLE_DIR\"\nfi\nls \"$SAMPLE_DIR\"\n# Expected (possibly inside a wrapper folder): alignment_data\u002F  GT.zip  videos\u002F\n```\n\n## Run Script\n\nRun the bundled script from the `amc-run-sample-calibration` skill package, not from the `auto-magic-calib` repo root. If the user points the agent at this skill folder directly instead of installing it, set `AMC_SAMPLE_SKILL_DIR` to the directory containing this `SKILL.md`, or run the command from that directory. Set `REPO_ROOT` to the AutoMagicCalib checkout resolved by `amc-setup-calibration-stack`; the script reads `compose\u002F.env` from that checkout for the backend port, accepts `BASE_URL`, `MS_PORT`, `SAMPLE_DIR`, and `RUN_VGGT` overrides, creates a fresh project each run, attempts VGGT when ready, and prints the NGC warehouse dataset note at the end.\n\n```bash\n# REPO_ROOT must point to the auto-magic-calib checkout, not the DeepStream repo.\n: \"${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}\"\ngrep -q \"AutoMagicCalib\" \"$REPO_ROOT\u002FREADME.md\" 2>\u002Fdev\u002Fnull && grep -q \"auto-magic-calib-ms\" \"$REPO_ROOT\u002Fcompose\u002Fms\u002Fcompose.yml\" 2>\u002Fdev\u002Fnull || { echo \"ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT\" >&2; exit 1; }\n\n# If AMC was resolved from DeepStream's tools\u002Fauto-magic-calib submodule,\n# derive the DeepStream root so the unpacked repo skill can be used directly.\nif [ -z \"${DEEPSTREAM_REPO_ROOT:-}\" ] && [ -d \"$REPO_ROOT\u002F..\u002F..\u002Fskills\u002Famc-run-sample-calibration\" ]; then\n  DEEPSTREAM_REPO_ROOT=\"$(cd \"$REPO_ROOT\u002F..\u002F..\" && pwd)\"\nfi\n\nSCRIPT_PATH=\"\"\nfor candidate in \\\n  \"${AMC_SAMPLE_SKILL_DIR:+$AMC_SAMPLE_SKILL_DIR\u002Fscripts\u002Frun_sample_calibration.py}\" \\\n  \"$PWD\u002Fscripts\u002Frun_sample_calibration.py\" \\\n  \"${DEEPSTREAM_REPO_ROOT:+$DEEPSTREAM_REPO_ROOT\u002Fskills\u002Famc-run-sample-calibration\u002Fscripts\u002Frun_sample_calibration.py}\" \\\n  \"$PWD\u002Fskills\u002Famc-run-sample-calibration\u002Fscripts\u002Frun_sample_calibration.py\" \\\n  \"$HOME\u002F.claude\u002Fskills\u002Famc-run-sample-calibration\u002Fscripts\u002Frun_sample_calibration.py\" \\\n  \"$HOME\u002F.codex\u002Fskills\u002Famc-run-sample-calibration\u002Fscripts\u002Frun_sample_calibration.py\" \\\n  \"$HOME\u002F.cursor\u002Fskills\u002Famc-run-sample-calibration\u002Fscripts\u002Frun_sample_calibration.py\"; do\n  if [ -f \"$candidate\" ]; then\n    SCRIPT_PATH=\"$candidate\"\n    break\n  fi\ndone\n\n[ -n \"$SCRIPT_PATH\" ] || {\n  echo \"ERROR: could not find amc-run-sample-calibration\u002Fscripts\u002Frun_sample_calibration.py\" >&2\n  echo \"Set AMC_SAMPLE_SKILL_DIR to the amc-run-sample-calibration skill directory, or run this block from that directory.\" >&2\n  exit 1\n}\n\npython3 \"$SCRIPT_PATH\"\n```\n\n## Alternative: Swagger UI Walkthrough\n\n> **Agent shortcut**: if the user explicitly requested a Swagger UI walkthrough (or said \"no Python\"), emit the table below and stop — do not invoke shell tooling, read other sections, or run the bundled Python script.\n\nThe microservice exposes an interactive OpenAPI UI at **`http:\u002F\u002F\u003CHOST_IP>:\u003CMS_PORT>\u002Fdocs`**. If you prefer clicking through the API by hand:\n\n1. Open `http:\u002F\u002F\u003CHOST_IP>:\u003CMS_PORT>\u002Fdocs` in a browser.\n2. Unzip `sdg_08_2_sample_data_010926.zip` into a cache directory next to it.\n3. Execute these endpoints **in order**, copying the `project_id` from step 1 into subsequent paths:\n\n   | # | Endpoint | Body \u002F Files |\n   |---|---|---|\n   | 1 | `POST \u002Fv1\u002Fcreate_project` | `project_name`: any string |\n   | 2 | `POST \u002Fv1\u002Fupload_video_files\u002F{project_id}` | `files`: upload all 4 `videos\u002Fcam_0*.mp4` **sorted by name** |\n   | 3 | `POST \u002Fv1\u002Fupload_alignment\u002F{project_id}` | `alignment_file`: `alignment_data\u002Falignment_data.json` |\n   | 4 | `POST \u002Fv1\u002Fupload_layout\u002F{project_id}` | `layout_file`: `alignment_data\u002Flayout.png` |\n   | 5 | `POST \u002Fv1\u002Fupload_gt_file\u002F{project_id}` | `gt_file`: `GT.zip` |\n   | 6 | `POST \u002Fv1\u002Fverify_project\u002F{project_id}` | — (expect `project_state: READY`) |\n   | 7 | `POST \u002Fv1\u002Fcalibrate\u002F{project_id}` | JSON: `{\"detector_type\": \"resnet\"}` |\n   | 8 | `GET \u002Fv1\u002Fget_project_info\u002F{project_id}` | Refresh every ~10 s until `project_state` = `COMPLETED` |\n   | 9 | `GET \u002Fv1\u002Fresult\u002F{project_id}\u002Fevaluation_statistics` | Read L2 distance + reprojection error |\n   | 10 optional | `POST \u002Fv1\u002Fvggt\u002Fcalibrate\u002F{project_id}` then `GET \u002Fv1\u002Fvggt_results\u002F{project_id}\u002Fevaluation_statistics` | Run only when `vggt_state` is `READY`; poll `vggt_state` until `COMPLETED` |\n\nThis is the same sequence the bundled Python script runs, just executed manually. Step 10 is attempted by default when `vggt_state` is `READY`; otherwise it is skipped with setup guidance.\n\n### Status Fields from `get_project_info`\n\n`project_info.project_state` is the AMC calibration lifecycle for the project. Poll it until it reaches `COMPLETED` (or stop on `ERROR`).\n\n`project_info.vggt_state` is a **per-project** VGGT refinement lifecycle, a project-scoped status rather than a direct global service or model-load status. A newly created project can report `vggt_state: \"INIT\"` even when the VGGT model is present and mounted. The expected lifecycle is `INIT` → `READY` after AMC calibration completes → `RUNNING` while VGGT refinement runs → `COMPLETED` (or `ERROR`). Interpret `INIT` on a new or uncalibrated project as normal project state. If AMC calibration is complete and the project remains in a non-ready VGGT state, confirm VGGT setup and model availability with the setup skill checks and service logs.\n\n## Success Criteria\n\n- Project reaches `project_state == \"COMPLETED\"` within ~30 min.\n- `\u002Fv1\u002Fresult\u002F{id}\u002Fevaluation_statistics` returns non-empty `statistics` (GT was uploaded).\n- VGGT either runs to `vggt_state == \"COMPLETED\"` and reports `\u002Fv1\u002Fvggt_results\u002F{id}\u002Fevaluation_statistics`, or is skipped with setup guidance because the project is not `READY` for VGGT.\n- No `ERROR` state encountered.\n\nRepresentative metrics for the sample (yours should be similar):\n\n```\nAverage L2 distance(m)               : \u003C 1.5\nAverage reprojection error 0(px)     : \u003C 10\n```\n\n## Key Output Files (on the server)\n\nResults persist under `$REPO_ROOT\u002Fprojects\u002Fproject_\u003Cproject_id>\u002F`:\n\n```\nprojects\u002Fproject_\u003Cproject_id>\u002F\n├── output\u002F\n│   ├── single_view_results\u002Fcam_XX\u002F\n│   │   ├── camInfo_hyper_XX.yaml\n│   │   └── trajDump_Stream_0_3d.txt\n│   └── multi_view_results\u002FBA_output\u002Fresults_ba\u002Frefined\u002F\n│       └── camInfo_XX.yaml          # ← final calibration (use this)\n└── calibration.log\n```\n\n## Monitoring Progress\n\n```bash\nPROJECT_ID=\u003Cid_from_step_1>\n: \"${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}\"\ngrep -q \"AutoMagicCalib\" \"$REPO_ROOT\u002FREADME.md\" 2>\u002Fdev\u002Fnull && grep -q \"auto-magic-calib-ms\" \"$REPO_ROOT\u002Fcompose\u002Fms\u002Fcompose.yml\" 2>\u002Fdev\u002Fnull || { echo \"ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT\" >&2; exit 1; }\ntail -F --retry \"$REPO_ROOT\u002Fprojects\u002Fproject_${PROJECT_ID}\u002Fcalibration.log\"\n```\n\nOr stream MS logs:\n\n```bash\n: \"${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}\"\ngrep -q \"AutoMagicCalib\" \"$REPO_ROOT\u002FREADME.md\" 2>\u002Fdev\u002Fnull && grep -q \"auto-magic-calib-ms\" \"$REPO_ROOT\u002Fcompose\u002Fms\u002Fcompose.yml\" 2>\u002Fdev\u002Fnull || { echo \"ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT\" >&2; exit 1; }\ndocker compose -f \"$REPO_ROOT\u002Fcompose\u002Fcompose.yml\" logs -f auto-magic-calib-ms\n```\n\n## Troubleshooting\n\n| Issue | Fix |\n|---|---|\n| `requests` not installed | Inside a venv: `python3 -m venv venv && .\u002Fvenv\u002Fbin\u002Fpip install requests`. If `python3 -m venv` fails: `sudo apt install -y python3-venv python3-pip` first |\n| `[2] Uploaded N videos` where N >> 4 | `SAMPLE_DIR` resolved to the repo root (or another over-broad path) and `rglob(\"cam_*.mp4\")` swept stale videos from `.cache\u002F`, `projects\u002F`, etc. Stop the run (`POST \u002Fv1\u002Fstop_calibration\u002F{id}`), delete the project (`DELETE \u002Fv1\u002Fdelete_project\u002F{id}`), set `SAMPLE_DIR` explicitly to the extracted sample dir, re-run. The script anchors on `videos\u002F` and asserts `len(videos) \u003C= 16` to fail loud |\n| `verify_project` returns state `!= READY` | Confirm all 4 videos + alignment + layout + GT uploaded; inspect `GET \u002Fv1\u002Fget_project_info\u002F{id}` response |\n| Sample not extracted | `unzip \u003Crepo_root>\u002Fassets\u002Fsdg_08_2_sample_data_010926.zip -d \u003Crepo_root>\u002Fassets\u002F.cache\u002Fsdg_08_2_sample_data_010926\u002F` |\n| `cam_*.mp4` glob finds 0 files | Check wrapper-folder depth: `find \u003Csample_dir> -name \"cam_*.mp4\"` |\n| Calibration times out (>60 min) | Check `calibration.log` for \"insufficient tracklets\"; see root `README.md` guidelines on input videos |\n| Upload returns 413 | Raise server upload limit, or split files (sample files are \u003C200 MB total so this is unusual) |\n| Port scan finds no backend | Backend not running — run `amc-setup-calibration-stack` skill |\n\n## Additional Sample Dataset\n\nThe root `README.md` also documents `nv_warehouse_032326.zip`, a real-world warehouse dataset available from NGC. Download it with `ngc registry resource download-version \"nvidia\u002Famc-nv-warehouse\"`; then use `amc-run-video-calibration`, upload `nv_warehouse_config.json` in the config step, and run with the `transformer` detector. It does not include ground-truth data.\n\n## Related Skills\n\n- `skills\u002Famc-setup-calibration-stack\u002FSKILL.md` — launch MS + UI (prerequisite).\n- `skills\u002Famc-run-video-calibration\u002FSKILL.md` — run calibration on your own pre-recorded MP4s.\n- `skills\u002Famc-run-rtsp-calibration\u002FSKILL.md` — run calibration from live RTSP streams through VIOS capture.\n\nRoot `README.md` \"Sample Data Setup\" and \"Calibration Workflow (UI)\" sections cover the human-oriented path through the same sample.\n\n\u003C!-- signing marker -->\n",{"data":32,"body":45},{"name":4,"description":6,"owner":33,"service":34,"version":35,"reviewed":36,"license":23,"metadata":37},"NVIDIA CORPORATION","auto-magic-calib","1.0.0","2026-04-28",{"author":33,"tags":38},[39,40,41,42,43,44],"amc","calibration","sample","rest-api","validation","python",{"type":46,"children":47},"root",[48,57,64,70,99,108,172,184,189,195,208,213,219,326,332,340,404,412,447,454,767,773,1361,1366,1451,2385,2391,2405,2421,2843,2861,2873,2899,2969,2975,3050,3055,3065,3071,3083,3092,3098,3400,3405,3678,3684,3967,3973,4024,4030,4065,4077],{"type":49,"tag":50,"props":51,"children":53},"element","h1",{"id":52},"skill-calibrate-sample-dataset",[54],{"type":55,"value":56},"text","Skill: Calibrate Sample Dataset",{"type":49,"tag":58,"props":59,"children":61},"h2",{"id":60},"when-to-use-this-skill",[62],{"type":55,"value":63},"When to Use This Skill",{"type":49,"tag":65,"props":66,"children":67},"p",{},[68],{"type":55,"value":69},"Activate this skill when the user wants to sanity-check a running AMC stack with the bundled sample dataset. 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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},[4219,4222],{"name":4220,"slug":4221,"type":15},"CI\u002FCD","ci-cd",{"name":4107,"slug":4108,"type":15},"2026-07-14T05:25:59.97109",{"slug":4225,"name":4225,"fn":4226,"description":4227,"org":4228,"tags":4229,"stars":4210,"repoUrl":4211,"updatedAt":4235},"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},[4230,4231,4232],{"name":4220,"slug":4221,"type":15},{"name":4107,"slug":4108,"type":15},{"name":4233,"slug":4234,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":4237,"name":4237,"fn":4238,"description":4239,"org":4240,"tags":4241,"stars":4210,"repoUrl":4211,"updatedAt":4249},"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},[4242,4245,4246],{"name":4243,"slug":4244,"type":15},"Debugging","debugging",{"name":4233,"slug":4234,"type":15},{"name":4247,"slug":4248,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":4251,"name":4251,"fn":4252,"description":4253,"org":4254,"tags":4255,"stars":4210,"repoUrl":4211,"updatedAt":4262},"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},[4256,4259],{"name":4257,"slug":4258,"type":15},"Best Practices","best-practices",{"name":4260,"slug":4261,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":4264,"name":4264,"fn":4265,"description":4266,"org":4267,"tags":4268,"stars":4210,"repoUrl":4211,"updatedAt":4276},"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},[4269,4272,4275],{"name":4270,"slug":4271,"type":15},"Machine Learning","machine-learning",{"name":4273,"slug":4274,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":4278,"name":4278,"fn":4279,"description":4280,"org":4281,"tags":4282,"stars":4210,"repoUrl":4211,"updatedAt":4287},"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},[4283,4286],{"name":4284,"slug":4285,"type":15},"QA","qa",{"name":18,"slug":19,"type":15},"2026-07-14T05:25:53.673039",{"slug":4289,"name":4289,"fn":4290,"description":4291,"org":4292,"tags":4293,"stars":4210,"repoUrl":4211,"updatedAt":4296},"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},[4294,4295],{"name":4107,"slug":4108,"type":15},{"name":4110,"slug":4111,"type":15},"2026-07-14T05:25:49.362534",{"slug":4298,"name":4298,"fn":4299,"description":4300,"org":4301,"tags":4302,"stars":4210,"repoUrl":4211,"updatedAt":4310},"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},[4303,4306,4307],{"name":4304,"slug":4305,"type":15},"Code Review","code-review",{"name":4233,"slug":4234,"type":15},{"name":4308,"slug":4309,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":4312,"name":4312,"fn":4313,"description":4314,"org":4315,"tags":4316,"stars":4210,"repoUrl":4211,"updatedAt":4319},"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},[4317,4318],{"name":4284,"slug":4285,"type":15},{"name":18,"slug":19,"type":15},"2026-07-14T05:25:54.928983",{"slug":4321,"name":4321,"fn":4322,"description":4323,"org":4324,"tags":4325,"stars":4210,"repoUrl":4211,"updatedAt":4328},"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},[4326,4327],{"name":4140,"slug":4141,"type":15},{"name":4220,"slug":4221,"type":15},"2026-07-30T05:29:03.275638",496]