Tao Train Pose Classification

Pose Classification

Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose keypoint data.

Typically trained from scratch on skeleton data.

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 still requires schemas/train.schema.json and references/spec_template_train.yaml to exist and parse. Use the packaged train 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. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only; otherwise default to auto. When automl_policy: auto, 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: pose_classification
• Formats: default
• Monitoring metric: val_acc

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.

S3_TRAIN = "s3://bucket/data/train"

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "num_classes": 6,
    "graph_layout": "nvidia",
    "dataset.train_dataset.data_path": f"{S3_TRAIN}",
    "dataset.train_dataset.label_path": f"{S3_TRAIN}",
    "dataset.val_dataset.data_path": f"{S3_TRAIN}",
    "dataset.val_dataset.label_path": f"{S3_TRAIN}",
}

evaluate (mandatory data sources):

{
    "evaluate.test_dataset.data_path": f"{S3_TRAIN}",
    "evaluate.test_dataset.label_path": f"{S3_TRAIN}",
}

inference (mandatory data sources):

{
    "inference.test_dataset.data_path": f"{S3_TRAIN}",
}

Eval Dataset

Optional. Validation data is provided alongside training as val_data.npy / val_label.pkl.

Important Parameters

• dataset.num_classes: Number of pose action classes. Default 6.
• model.graph_layout: Skeleton graph layout. Options: nvidia, openpose. Determines joint connectivity.
• model.graph_strategy: Graph partitioning strategy for GCN.
• train.optim.lr: Learning rate. Default 0.1 (SGD). Higher than vision models due to graph convolution properties.
• model.dropout: Dropout rate for regularization.

Multi-GPU / Multi-Node

Launch method: Lightning-managed (single python process, Lightning spawns workers).

• Strategy: auto (Lightning picks best strategy automatically)
• No explicit num_nodes or distributed_strategy config — single-node only
• Lightweight model, single GPU typically sufficient

Hardware

Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. Pose classification is very lightweight — skeleton data is small. Single GPU is sufficient.

Error Patterns

Graph layout mismatch: Ensure model.graph_layout matches the skeleton format in your .npy data files.

Label shape mismatch: train_label.pkl class indices must be in range [0, num_classes).

Spec Param / Parent Model Inference

Model-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.

Inference mappings from TAO Core pose_classification.config.json:

For parent_model or parent_model_folder, pass the upstream train/export/AutoML 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.