[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-tao-train-action-recognition":3,"mdc-8y6kny-key":34,"related-repo-nvidia-tao-train-action-recognition":1789,"related-org-nvidia-tao-train-action-recognition":1893},{"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-action-recognition","train action recognition models","Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips. Use when training, evaluating, exporting, or running inference on a TAO action-recognition model. Trigger phrases include \"train action recognition\", \"video action classification\", \"RGB + optical flow action model\", \"TAO ActionRecognition\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,17,20],{"name":13,"slug":14,"type":15},"Deep Learning","deep-learning","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Computer Vision","computer-vision",{"name":21,"slug":22,"type":15},"Video","video",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:29:29.475837","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-action-recognition","---\nname: tao-train-action-recognition\ndescription: Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for\n  classifying temporal actions in video clips. Use when training, evaluating, exporting, or running inference on a TAO\n  action-recognition model. Trigger phrases include \"train action recognition\", \"video action classification\", \"RGB +\n  optical flow action model\", \"TAO ActionRecognition\".\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- action\n- recognition\n---\n\n# Action Recognition\n\nAction recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips.\n\nSet model.pretrained_model_path for pretrained backbone weights.\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:** action_recognition\n- **Formats:** default\n- **Monitoring metric:** val_loss\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| evaluate | evaluate.test_dataset_dir | train_datasets | test\u002F extracted from test.tar.gz | No |\n| inference | inference.inference_dataset_dir | train_datasets | test\u002Fsmile\u002F extracted from test\u002Fsmile.tar.gz | No |\n| train | dataset.train_dataset_dir | train_datasets | train\u002F extracted from train.tar.gz | No |\n| train | dataset.val_dataset_dir | train_datasets | test\u002F extracted from test.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\nLOCAL_DATA = \"\u002Fworkspace\u002Fdata\u002Fextracted\"\n```\n\nIf the source dataset is provided as the TAO sample archives\n`train.tar.gz`, `test.tar.gz`, or `test\u002Fsmile.tar.gz`, download and extract\nthem before launching the TAO container. The action-recognition entrypoints\nexpect directory paths and fail with `NotADirectoryError` when these spec keys\npoint at `.tar.gz` files.\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.label_map\": {\n        \"catch\": 0,\n        \"smile\": 1\n    },\n    \"dataset.batch_size\": 2,\n    \"dataset.train_dataset_dir\": f\"{LOCAL_DATA}\u002Ftrain\",\n    \"dataset.val_dataset_dir\": f\"{LOCAL_DATA}\u002Ftest\",\n}\n```\n\n**evaluate (mandatory data sources):**\n```python\n{\n    \"dataset.label_map\": {\n        \"catch\": 0,\n        \"smile\": 1\n    },\n    \"evaluate.test_dataset_dir\": f\"{LOCAL_DATA}\u002Ftest\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"dataset.label_map\": {\n        \"catch\": 0,\n        \"smile\": 1\n    },\n    \"inference.inference_dataset_dir\": f\"{LOCAL_DATA}\u002Fsmile_infer\u002Fsmile\",\n}\n```\n\n**export (mandatory checkpoint + output path):**\n```python\n{\n    \"export.checkpoint\": \"\u003Cselected train checkpoint>\",\n    \"export.onnx_file\": \"\u003Cresults_dir>\u002Faction_recognition.onnx\",\n}\n```\n\nFor direct local-docker chaining without the SDK resolver, select the concrete\ncheckpoint produced by training, for example\n`model_epoch_000_step_00005.pth`, and pass that exact file to `evaluate`,\n`inference`, and `export`. Do not use the `ar_model_latest.pth` symlink unless\nthe user explicitly requests latest-checkpoint behavior. For resume training,\nset `train.resume_training_checkpoint_path` to the exact epoch\u002Fstep checkpoint\nbeing resumed.\n## Eval Dataset\n\nOptional. Test dataset may be distributed as `test.tar.gz` separate from\ntraining; extract it and point the spec to the extracted `test\u002F` directory.\nTAO training emits `val_loss` as the validation scalar for the packaged sample\ndata; use `val_loss` with minimize direction for AutoML selection unless a\ncustom evaluator supplies an accuracy metric.\n\n## Important Parameters\n\n- **model.model_type**: Input type: rgb, of (optical flow), or joint (multi-stream).\n- **model.backbone**: Default resnet_18. Used as the spatial feature extractor.\n- **dataset.label_map**: Dictionary mapping class names to indices.\n- **model.rgb_seq_length**: Number of frames per clip for RGB input.\n- **model.of_seq_length**: Number of frames for optical flow input.\n- **train.optim.lr**: Learning rate. Default 5e-4.\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 best strategy automatically)\n- No explicit `num_nodes` or `distributed_strategy` config — single-node oriented\n\n## Hardware\n\nMinimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Memory depends on sequence length and input resolution. batch_size=2 is conservative for video data.\n\n## Error Patterns\n\n**Sequence length mismatch**: Ensure video clips have enough frames for the configured rgb_seq_length or of_seq_length.\n\n**Evaluate\u002Finference missing label map**: Downstream actions rebuild the\nActionRecognitionModel before loading the checkpoint, so they need the same\n`dataset.label_map` used during training. Include it with every evaluate or\ninference spec; otherwise model construction fails before the checkpoint can be\nvalidated.\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 `action_recognition.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 | `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| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `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.of_pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `model.rgb_pretrained_model_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",{"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],"action","recognition",{"type":45,"children":46},"root",[47,56,62,67,74,168,174,283,317,323,359,366,511,517,537,558,602,610,733,741,798,806,863,871,908,958,964,999,1005,1068,1074,1091,1164,1201,1207,1212,1218,1228,1245,1251,1280,1293,1749,1783],{"type":48,"tag":49,"props":50,"children":52},"element","h1",{"id":51},"action-recognition",[53],{"type":54,"value":55},"text","Action Recognition",{"type":48,"tag":57,"props":58,"children":59},"p",{},[60],{"type":54,"value":61},"Action recognition from video sequences. 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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},[1945,1946,1947],{"name":1935,"slug":1936,"type":15},{"name":1815,"slug":1816,"type":15},{"name":1948,"slug":1949,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1952,"name":1952,"fn":1953,"description":1954,"org":1955,"tags":1956,"stars":1925,"repoUrl":1926,"updatedAt":1964},"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},[1957,1960,1961],{"name":1958,"slug":1959,"type":15},"Debugging","debugging",{"name":1948,"slug":1949,"type":15},{"name":1962,"slug":1963,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1966,"name":1966,"fn":1967,"description":1968,"org":1969,"tags":1970,"stars":1925,"repoUrl":1926,"updatedAt":1977},"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},[1971,1974],{"name":1972,"slug":1973,"type":15},"Best Practices","best-practices",{"name":1975,"slug":1976,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1979,"name":1979,"fn":1980,"description":1981,"org":1982,"tags":1983,"stars":1925,"repoUrl":1926,"updatedAt":1991},"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},[1984,1987,1990],{"name":1985,"slug":1986,"type":15},"Machine Learning","machine-learning",{"name":1988,"slug":1989,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1993,"name":1993,"fn":1994,"description":1995,"org":1996,"tags":1997,"stars":1925,"repoUrl":1926,"updatedAt":2002},"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},[1998,2001],{"name":1999,"slug":2000,"type":15},"QA","qa",{"name":1845,"slug":1846,"type":15},"2026-07-14T05:25:53.673039",{"slug":2004,"name":2004,"fn":2005,"description":2006,"org":2007,"tags":2008,"stars":1925,"repoUrl":1926,"updatedAt":2011},"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},[2009,2010],{"name":1815,"slug":1816,"type":15},{"name":1818,"slug":1819,"type":15},"2026-07-14T05:25:49.362534",{"slug":2013,"name":2013,"fn":2014,"description":2015,"org":2016,"tags":2017,"stars":1925,"repoUrl":1926,"updatedAt":2025},"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},[2018,2021,2022],{"name":2019,"slug":2020,"type":15},"Code Review","code-review",{"name":1948,"slug":1949,"type":15},{"name":2023,"slug":2024,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2027,"name":2027,"fn":2028,"description":2029,"org":2030,"tags":2031,"stars":1925,"repoUrl":1926,"updatedAt":2034},"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},[2032,2033],{"name":1999,"slug":2000,"type":15},{"name":1845,"slug":1846,"type":15},"2026-07-14T05:25:54.928983",{"slug":2036,"name":2036,"fn":2037,"description":2038,"org":2039,"tags":2040,"stars":1925,"repoUrl":1926,"updatedAt":2043},"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},[2041,2042],{"name":1855,"slug":1856,"type":15},{"name":1935,"slug":1936,"type":15},"2026-07-30T05:29:03.275638",496]