[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-tao-train-centerpose":3,"mdc--w694zn-key":34,"related-repo-nvidia-tao-train-centerpose":2064,"related-org-nvidia-tao-train-centerpose":2168},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"tao-train-centerpose","train CenterPose models for pose estimation","CenterPose for keypoint \u002F pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger phrases include \"train CenterPose\", \"6-DoF object pose\", \"keypoint estimation\", \"object pose regression\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Deep Learning","deep-learning","tag",{"name":17,"slug":18,"type":15},"Machine Learning","machine-learning",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Computer Vision","computer-vision",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:27:20.507306","Apache-2.0",281,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Ftao-train-centerpose","---\nname: tao-train-centerpose\ndescription: CenterPose for keypoint \u002F pose estimation. Detects object centers and regresses keypoint locations for 6-DoF\n  object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger\n  phrases include \"train CenterPose\", \"6-DoF object pose\", \"keypoint estimation\", \"object pose regression\".\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit.\nmetadata:\n  version: \"0.1.0\"\n  author: NVIDIA Corporation\nallowed-tools: Read Bash\ntags:\n- pose\n- estimation\n---\n\n# CenterPose\n\nCenterPose for keypoint \u002F pose estimation. Detects object centers and regresses keypoint locations. Used for 6-DoF object pose estimation.\n\nSet model.backbone.pretrained_backbone_path.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), use the deploy spec templates packaged in this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml` prefix.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `evaluate`, `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Training Requirements\n\n- **Dataset type:** centerpose\n- **Formats:** default\n- **Monitoring metric:** val_3DIoU\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| evaluate | dataset.test_data | eval_dataset | test.tar.gz | No |\n| gen_trt_engine | gen_trt_engine.tensorrt.calibration.cal_image_dir | calibration_dataset | train.tar.gz | Yes |\n| inference | dataset.inference_data | inference_dataset | val.tar.gz | No |\n| train | dataset.train_data | train_datasets | train.tar.gz | No |\n| train | dataset.val_data | eval_dataset | val.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for every action** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nTRAIN_DIR = \"\u002Fpath\u002Fto\u002Fextracted\u002Ftrain\"\nVAL_DIR = \"\u002Fpath\u002Fto\u002Fextracted\u002Fval\"\nTEST_DIR = \"\u002Fpath\u002Fto\u002Fextracted\u002Ftest\"\nINFER_DIR = VAL_DIR\nCAL_IMAGE_DIRS = [\"\u002Fpath\u002Fto\u002Fextracted\u002Ftrain\u002F\u003Csequence_or_image_dir>\"]\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_epochs\": 30,\n    \"train.checkpoint_interval\": 10,\n    \"train.validation_interval\": 10,\n    \"train.num_gpus\": 1,\n    \"dataset.category\": \"bike\",\n    \"dataset.batch_size\": 4,\n    \"dataset.train_data\": TRAIN_DIR,\n    \"dataset.val_data\": VAL_DIR,\n}\n```\n\n**evaluate (mandatory data sources):**\n```python\n{\n    \"dataset.category\": \"bike\",\n    \"dataset.test_data\": TEST_DIR,\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"dataset.category\": \"bike\",\n    \"dataset.inference_data\": INFER_DIR,\n}\n```\n\n**gen_trt_engine (mandatory data sources):**\n```python\n{\n    \"gen_trt_engine.tensorrt.calibration.cal_image_dir\": CAL_IMAGE_DIRS,\n}\n```\n## Eval Dataset\n\nOptional. Val and test datasets are provided as separate tarballs.\n\n## Important Parameters\n\n- **dataset.num_classes**: Number of object categories. Default 1.\n- **dataset.num_joints**: Number of keypoints per object. Fixed at 8 (bbox keypoints). Valid range: exactly 8.\n- **dataset.input_res**: Input resolution. Fixed at 512. Output resolution fixed at 128.\n- **dataset.category**: Object category name. Default \"cereal_box\".\n- **model.backbone.model_type**: Default fan_small. Backbone options limited in schema.\n- **train.optim.lr**: Learning rate. Default 6e-5. MultiStep scheduler with lr_steps=[90, 120], lr_decay=0.1.\n- **train.loss_config**: Rich loss config with toggles: mse_loss, obj_scale, obj_scale_uncertainty, hps_uncertainty, reg_bbox, hm_hp. Weights: wh_weight=0.1, off_weight=1, hp_weight=1.\n- **inference.use_pnp**: Use PnP for 6-DoF pose. Default True. Requires camera intrinsics (focal_length_x\u002Fy, principle_point_x\u002Fy).\n- **export.input_width**: Export input size. Fixed at 512x512. opset_version=16.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n\n- Strategy: `auto` (Lightning picks the best strategy automatically)\n- No explicit `num_nodes` or `distributed_strategy` config — single-node only\n- No `sync_batchnorm`\n\n## Export \u002F TRT Defaults\n\n- Export input: 512x512 (fixed), opset 16\n- TRT data types: FP32, FP16, INT8\n- TRT opt_batch_size: 4, max_batch_size: 8\n\n## Hardware\n\nMinimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. CenterPose is moderately memory-intensive depending on input resolution and number of keypoints.\n\n## Error Patterns\n\n**num_joints mismatch**: Ensure dataset.num_joints matches the keypoint count in your annotations.\n\n**Extract S3 tarballs for local Docker**: The starter-kit S3 data is packaged as\n`train.tar.gz`, `val.tar.gz`, and `test.tar.gz`, but the CenterPose TAO actions\nconsume extracted folders. Extract each archive and set `dataset.train_data`,\n`dataset.val_data`, `dataset.test_data`, and `dataset.inference_data` to the\nextracted split directories.\n\n**Checkpoint handoff**: CenterPose training writes concrete checkpoints such as\n`model_epoch_000_step_00008.pth` and a `centerpose_model_latest.pth` symlink.\nUse the SDK\u002Fmodel checkpoint resolver or the exact epoch\u002Fstep checkpoint for\nevaluate, inference, export, and resume. Use the symlink only when the user\nexplicitly asks for latest.\n\n**TAO Deploy postprocessor compatibility**: Use the deploy image resolved from\n`versions.yaml` or the selected platform. A successful `gen_trt_engine` run does\nnot prove deploy `evaluate` or `inference` works; inspect those action exit codes\nand logs separately, especially for CenterPose postprocessor errors such as\n`TypeError: only 0-dimensional arrays can be converted to Python scalars`.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nInference mappings from TAO Core `centerpose.config.json`:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| evaluate | `encryption_key` | `key` | encryption key |\n| evaluate | `evaluate.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `evaluate.trt_engine` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `results_dir` | `output_dir` | current job results directory |\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| gen_trt_engine | `encryption_key` | `key` | encryption key |\n| gen_trt_engine | `gen_trt_engine.onnx_file` | `parent_model` | model file inferred from the parent job results folder |\n| gen_trt_engine | `gen_trt_engine.tensorrt.calibration.cal_cache_file` | `create_cal_cache` | calibration cache path |\n| gen_trt_engine | `gen_trt_engine.trt_engine` | `create_engine_file` | output TensorRT engine path |\n| gen_trt_engine | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `inference.trt_engine` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `model.backbone.pretrained_backbone_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | model file inferred from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n\n## Deployment\n\n- [tao-deploy-centerpose](references\u002Ftao-deploy-centerpose.md)\n",{"data":35,"body":44},{"name":4,"description":6,"license":26,"compatibility":36,"metadata":37,"allowed-tools":40,"tags":41},"Requires docker + nvidia-container-toolkit.",{"version":38,"author":39},"0.1.0","NVIDIA Corporation","Read Bash",[42,43],"pose","estimation",{"type":45,"children":46},"root",[47,56,62,67,113,120,213,219,328,360,366,402,409,582,588,608,666,674,766,774,810,818,854,862,891,897,902,908,1008,1014,1031,1104,1152,1158,1176,1182,1187,1193,1203,1259,1285,1330,1336,1365,1378,2005,2039,2045,2058],{"type":48,"tag":49,"props":50,"children":52},"element","h1",{"id":51},"centerpose",[53],{"type":54,"value":55},"text","CenterPose",{"type":48,"tag":57,"props":58,"children":59},"p",{},[60],{"type":54,"value":61},"CenterPose for keypoint \u002F pose estimation. Detects object centers and regresses keypoint locations. Used for 6-DoF object pose estimation.",{"type":48,"tag":57,"props":63,"children":64},{},[65],{"type":54,"value":66},"Set model.backbone.pretrained_backbone_path.",{"type":48,"tag":57,"props":68,"children":69},{},[70,72,79,81,87,89,95,97,103,105,111],{"type":54,"value":71},"For TAO Deploy TensorRT actions (",{"type":48,"tag":73,"props":74,"children":76},"code",{"className":75},[],[77],{"type":54,"value":78},"gen_trt_engine",{"type":54,"value":80},", TensorRT ",{"type":48,"tag":73,"props":82,"children":84},{"className":83},[],[85],{"type":54,"value":86},"evaluate",{"type":54,"value":88},", and TensorRT ",{"type":48,"tag":73,"props":90,"children":92},{"className":91},[],[93],{"type":54,"value":94},"inference",{"type":54,"value":96},"), use the deploy spec templates packaged in this skill's ",{"type":48,"tag":73,"props":98,"children":100},{"className":99},[],[101],{"type":54,"value":102},"references\u002F",{"type":54,"value":104}," folder with the ",{"type":48,"tag":73,"props":106,"children":108},{"className":107},[],[109],{"type":54,"value":110},"spec_template_deploy_*.yaml",{"type":54,"value":112}," prefix.",{"type":48,"tag":114,"props":115,"children":117},"h2",{"id":116},"dataclass-schemas",[118],{"type":54,"value":119},"Dataclass Schemas",{"type":48,"tag":57,"props":121,"children":122},{},[123,125,131,133,139,141,147,149,155,157,163,165,171,173,179,181,187,189,195,197,203,205,211],{"type":54,"value":124},"Generated TAO Core schemas are packaged in ",{"type":48,"tag":73,"props":126,"children":128},{"className":127},[],[129],{"type":54,"value":130},"schemas\u002F\u003Caction>.schema.json",{"type":54,"value":132},", with ",{"type":48,"tag":73,"props":134,"children":136},{"className":135},[],[137],{"type":54,"value":138},"schemas\u002Fmanifest.json",{"type":54,"value":140}," listing available actions. Each generated schema also emits ",{"type":48,"tag":73,"props":142,"children":144},{"className":143},[],[145],{"type":54,"value":146},"references\u002Fspec_template_\u003Caction>.yaml",{"type":54,"value":148}," from the schema top-level ",{"type":48,"tag":73,"props":150,"children":152},{"className":151},[],[153],{"type":54,"value":154},"default",{"type":54,"value":156}," field. AutoML enablement is declared at the model layer in ",{"type":48,"tag":73,"props":158,"children":160},{"className":159},[],[161],{"type":54,"value":162},"references\u002Fskill_info.yaml",{"type":54,"value":164}," via ",{"type":48,"tag":73,"props":166,"children":168},{"className":167},[],[169],{"type":54,"value":170},"automl_enabled",{"type":54,"value":172},". Runnable AutoML still requires ",{"type":48,"tag":73,"props":174,"children":176},{"className":175},[],[177],{"type":54,"value":178},"schemas\u002Ftrain.schema.json",{"type":54,"value":180}," and ",{"type":48,"tag":73,"props":182,"children":184},{"className":183},[],[185],{"type":54,"value":186},"references\u002Fspec_template_train.yaml",{"type":54,"value":188}," to exist and parse. Use the packaged train schema for ",{"type":48,"tag":73,"props":190,"children":192},{"className":191},[],[193],{"type":54,"value":194},"automl_default_parameters",{"type":54,"value":196},", ",{"type":48,"tag":73,"props":198,"children":200},{"className":199},[],[201],{"type":54,"value":202},"automl_disabled_parameters",{"type":54,"value":204},", defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ",{"type":48,"tag":73,"props":206,"children":208},{"className":207},[],[209],{"type":54,"value":210},"~\u002Ftao-core",{"type":54,"value":212}," at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.",{"type":48,"tag":114,"props":214,"children":216},{"id":215},"train-action-policy",[217],{"type":54,"value":218},"Train Action Policy",{"type":48,"tag":57,"props":220,"children":221},{},[222,224,229,231,237,239,245,247,253,255,261,263,269,271,276,277,283,285,290,291,296,298,304,306,312,314,319,321,326],{"type":54,"value":223},"This model is AutoML-enabled at the model layer. Before handling any train-stage request, read ",{"type":48,"tag":73,"props":225,"children":227},{"className":226},[],[228],{"type":54,"value":162},{"type":54,"value":230}," and resolve the run override from either an explicit ",{"type":48,"tag":73,"props":232,"children":234},{"className":233},[],[235],{"type":54,"value":236},"automl_policy",{"type":54,"value":238}," value or the user's workflow request. Use ",{"type":48,"tag":73,"props":240,"children":242},{"className":241},[],[243],{"type":54,"value":244},"automl_policy: on",{"type":54,"value":246}," by default and only expose ",{"type":48,"tag":73,"props":248,"children":250},{"className":249},[],[251],{"type":54,"value":252},"on",{"type":54,"value":254}," \u002F ",{"type":48,"tag":73,"props":256,"children":258},{"className":257},[],[259],{"type":54,"value":260},"off",{"type":54,"value":262}," in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as ",{"type":48,"tag":73,"props":264,"children":266},{"className":265},[],[267],{"type":54,"value":268},"automl_policy: off",{"type":54,"value":270}," for this run only. When ",{"type":48,"tag":73,"props":272,"children":274},{"className":273},[],[275],{"type":54,"value":244},{"type":54,"value":196},{"type":48,"tag":73,"props":278,"children":280},{"className":279},[],[281],{"type":54,"value":282},"automl_enabled: true",{"type":54,"value":284},", and both ",{"type":48,"tag":73,"props":286,"children":288},{"className":287},[],[289],{"type":54,"value":178},{"type":54,"value":180},{"type":48,"tag":73,"props":292,"children":294},{"className":293},[],[295],{"type":54,"value":186},{"type":54,"value":297}," are packaged, route the train action through ",{"type":48,"tag":73,"props":299,"children":301},{"className":300},[],[302],{"type":54,"value":303},"tao-skill-bank:tao-run-automl",{"type":54,"value":305}," by default with this model's ",{"type":48,"tag":73,"props":307,"children":309},{"className":308},[],[310],{"type":54,"value":311},"skill_dir",{"type":54,"value":313},". Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and ",{"type":48,"tag":73,"props":315,"children":317},{"className":316},[],[318],{"type":54,"value":236},{"type":54,"value":320},". Use direct model training only when ",{"type":48,"tag":73,"props":322,"children":324},{"className":323},[],[325],{"type":54,"value":268},{"type":54,"value":327}," or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.",{"type":48,"tag":57,"props":329,"children":330},{},[331,333,338,339,344,345,351,353,358],{"type":54,"value":332},"Non-train actions such as ",{"type":48,"tag":73,"props":334,"children":336},{"className":335},[],[337],{"type":54,"value":86},{"type":54,"value":196},{"type":48,"tag":73,"props":340,"children":342},{"className":341},[],[343],{"type":54,"value":94},{"type":54,"value":196},{"type":48,"tag":73,"props":346,"children":348},{"className":347},[],[349],{"type":54,"value":350},"export",{"type":54,"value":352},", and deploy flows stay in this model skill. 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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},[2220,2221,2222],{"name":2210,"slug":2211,"type":15},{"name":2044,"slug":2041,"type":15},{"name":2223,"slug":2224,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2227,"name":2227,"fn":2228,"description":2229,"org":2230,"tags":2231,"stars":2200,"repoUrl":2201,"updatedAt":2239},"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},[2232,2235,2236],{"name":2233,"slug":2234,"type":15},"Debugging","debugging",{"name":2223,"slug":2224,"type":15},{"name":2237,"slug":2238,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2241,"name":2241,"fn":2242,"description":2243,"org":2244,"tags":2245,"stars":2200,"repoUrl":2201,"updatedAt":2252},"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},[2246,2249],{"name":2247,"slug":2248,"type":15},"Best Practices","best-practices",{"name":2250,"slug":2251,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2254,"name":2254,"fn":2255,"description":2256,"org":2257,"tags":2258,"stars":2200,"repoUrl":2201,"updatedAt":2264},"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},[2259,2260,2263],{"name":17,"slug":18,"type":15},{"name":2261,"slug":2262,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":2266,"name":2266,"fn":2267,"description":2268,"org":2269,"tags":2270,"stars":2200,"repoUrl":2201,"updatedAt":2275},"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},[2271,2274],{"name":2272,"slug":2273,"type":15},"QA","qa",{"name":2118,"slug":2119,"type":15},"2026-07-14T05:25:53.673039",{"slug":2277,"name":2277,"fn":2278,"description":2279,"org":2280,"tags":2281,"stars":2200,"repoUrl":2201,"updatedAt":2284},"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},[2282,2283],{"name":2044,"slug":2041,"type":15},{"name":2091,"slug":2092,"type":15},"2026-07-14T05:25:49.362534",{"slug":2286,"name":2286,"fn":2287,"description":2288,"org":2289,"tags":2290,"stars":2200,"repoUrl":2201,"updatedAt":2298},"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},[2291,2294,2295],{"name":2292,"slug":2293,"type":15},"Code Review","code-review",{"name":2223,"slug":2224,"type":15},{"name":2296,"slug":2297,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2300,"name":2300,"fn":2301,"description":2302,"org":2303,"tags":2304,"stars":2200,"repoUrl":2201,"updatedAt":2307},"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},[2305,2306],{"name":2272,"slug":2273,"type":15},{"name":2118,"slug":2119,"type":15},"2026-07-14T05:25:54.928983",{"slug":2309,"name":2309,"fn":2310,"description":2311,"org":2312,"tags":2313,"stars":2200,"repoUrl":2201,"updatedAt":2316},"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},[2314,2315],{"name":2128,"slug":2129,"type":15},{"name":2210,"slug":2211,"type":15},"2026-07-30T05:29:03.275638",496]