Tao Train Action Recognition

Action Recognition

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips.

Set model.pretrained_model_path for pretrained backbone weights.

Quick Start (docker run)

Docker-native launch — no TAO SDK and no Python on the host. Use the local Docker/platform skill instead when it gives a stricter environment-specific command (non-root UID mapping, cache redirects, remote daemons).

TAO_PYT_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.2.0-pyt  # versions-key: images.tao_toolkit.pyt
TAO_PYT_IMAGE="${TAO_PYT_IMAGE:-$TAO_PYT_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
DOCKER_COMMON=(
  --rm --gpus all --shm-size=8g
  --shm-size=8g
  --ulimit memlock=-1
  --ulimit stack=67108864
  -v "$RUN_ROOT/data:/data:ro"
  -v "$RUN_ROOT/specs:/specs:ro"
  -v "$RUN_ROOT/results:/results"
)

Train:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  action_recognition train -e /specs/train.yaml

Evaluate:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  action_recognition evaluate -e /specs/evaluate.yaml

Inference:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  action_recognition inference -e /specs/inference.yaml

Export:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  action_recognition export -e /specs/export.yaml

Every action takes its spec with -e; results_dir is set in the spec or overridden on the command line. Mount any pretrained-weights directory the spec references, and keep every in-container path consistent across actions.

Dataclass Schemas

Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML for an action requires schemas/<action>.schema.json and references/spec_template_<action>.yaml to exist and parse. Use the packaged selected-action schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.

Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_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 / 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/train.schema.json and references/spec_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/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-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.

Training Requirements

• Dataset type: action_recognition
• Formats: default
• Training monitoring metrics: val_loss, val_acc
• Evaluate task metrics: accuracy, m_accuracy
• AutoML metric contract: for the required evaluation-backed baseline and final comparison, use accuracy with maximize direction and run evaluate through eval_fn for every recommendation. val_loss is suitable only for an explicitly accepted training-proxy run because evaluate does not emit it. Scratch runs with no starting checkpoint require a minimal default train job followed by evaluation of its exact epoch/step checkpoint for the baseline.

Per-Action Dataset Requirements

Typical Spec Overrides

Data 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.

LOCAL_DATA = "/workspace/data/extracted"

If the source dataset is provided as the TAO sample archives train.tar.gz, test.tar.gz, or test/smile.tar.gz, download and extract them before launching the TAO container. The action-recognition entrypoints expect directory paths and fail with NotADirectoryError when these spec keys point at .tar.gz files.

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.label_map": {
        "catch": 0,
        "smile": 1
    },
    "dataset.batch_size": 2,
    "dataset.train_dataset_dir": f"{LOCAL_DATA}/train",
    "dataset.val_dataset_dir": f"{LOCAL_DATA}/test",
}

evaluate (mandatory data sources):

{
    "dataset.label_map": {
        "catch": 0,
        "smile": 1
    },
    "evaluate.test_dataset_dir": f"{LOCAL_DATA}/test",
}

inference (mandatory data sources):

{
    "dataset.label_map": {
        "catch": 0,
        "smile": 1
    },
    "inference.inference_dataset_dir": f"{LOCAL_DATA}/smile_infer/smile",
}

export (mandatory checkpoint + output path):

{
    "export.checkpoint": "<selected train checkpoint>",
    "export.onnx_file": "<results_dir>/action_recognition.onnx",
}

For direct local-docker chaining without the SDK resolver, select the concrete checkpoint produced by training, for example model_epoch_000_step_00005.pth, and pass that exact file to evaluate, inference, and export. Do not use the ar_model_latest.pth symlink unless the user explicitly requests latest-checkpoint behavior. For resume training, set train.resume_training_checkpoint_path to the exact epoch/step checkpoint being resumed.