Tao Train Reid

Re-Identification

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).

Person re-identification. Learns discriminative embeddings to match the same person across different camera views. Metric learning based.

Set model.pretrained_model_path for pretrained 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" \
  re_identification train -e /specs/train.yaml

Evaluate:

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

Inference:

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

Export:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  re_identification 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.

Supported Actions

The packaged Re-Identification PyT CLI supports train, evaluate, inference, export, and default_specs. This model skill exposes the runnable user actions train, evaluate, inference, and export; resume/retrain is performed through train with train.resume_training_checkpoint_path.

Do not advertise or synthesize dataset_convert, deploy, prune, quantize, gen_trt_engine, or standalone retrain for this model unless the packaged model skill and real CLI add those actions.

Training Requirements

• Dataset type: re_identification
• Formats: default
• AutoML training metric: cmc_rank_1 (maximize). Train status files emit this retrieval KPI for trial ranking.
• Standalone evaluation metric: mAP. The evaluate action writes mAP to its status KPI and prints the CMC ranks in its console table; do not expect standalone evaluate status to contain cmc_rank_1.

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"
CHECKPOINT = "/results/{train_job_id}/results_dir/model_epoch_000_step_00099.pth"

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.num_classes": 100,
    "dataset.num_workers": 4,
    "dataset.batch_size": 16,
    "dataset.num_instances": 4,
    "dataset.train_dataset_dir": f"{S3_TRAIN}/sample_train.tar.gz",
    "dataset.test_dataset_dir": f"{S3_TRAIN}/sample_test.tar.gz",
    "dataset.query_dataset_dir": f"{S3_TRAIN}/sample_query.tar.gz",
}

resume train (mandatory checkpoint):

{
    "train.num_epochs": 31,
    "train.resume_training_checkpoint_path": CHECKPOINT,
    "dataset.num_classes": 100,
    "dataset.batch_size": 16,
    "dataset.num_instances": 4,
    "dataset.train_dataset_dir": f"{S3_TRAIN}/sample_train.tar.gz",
    "dataset.test_dataset_dir": f"{S3_TRAIN}/sample_test.tar.gz",
    "dataset.query_dataset_dir": f"{S3_TRAIN}/sample_query.tar.gz",
}

evaluate (mandatory data sources and checkpoint):

{
    "evaluate.test_dataset": f"{S3_TRAIN}/sample_test.tar.gz",
    "evaluate.query_dataset": f"{S3_TRAIN}/sample_query.tar.gz",
    "evaluate.checkpoint": CHECKPOINT,
    "evaluate.output_cmc_curve_plot": "/results/{evaluate_job_id}/results_dir/cmc_curve.png",
    "evaluate.output_sampled_matches_plot": "/results/{evaluate_job_id}/results_dir/sampled_matches.png",
}

export (mandatory checkpoint and output):

{
    "export.checkpoint": CHECKPOINT,
    "export.onnx_file": "/results/{export_job_id}/results_dir/reid.onnx",
}

inference (mandatory data sources and checkpoint):

{
    "inference.test_dataset": f"{S3_TRAIN}/sample_test.tar.gz",
    "inference.query_dataset": f"{S3_TRAIN}/sample_query.tar.gz",
    "inference.checkpoint": CHECKPOINT,
    "inference.output_file": "/results/{inference_job_id}/results_dir/reid_inference.json",
}

For export and inference, provide explicit file paths for export.onnx_file and inference.output_file. For evaluate, provide explicit file paths for evaluate.output_cmc_curve_plot and evaluate.output_sampled_matches_plot. Keep these as spec values or spec_params mappings; do not declare them as file outputs in skill_info.yaml for local Docker until the runner distinguishes files from folders during output pre-creation.

Eval Dataset

Required. Evaluation requires test and query datasets for retrieval-based metrics (CMC, mAP).

Important Parameters

• dataset.num_classes: Number of identities. Default 751. Must match the number of unique identities in training data.
• model.backbone: Default resnet_50.
• optim.base_lr: Base learning rate. Default 3.5e-4.
• dataset.batch_size: Per-GPU batch size. Default 64. Re-ID benefits from large batches for better triplet/contrastive sampling.
• dataset.num_instances: Number of instances per identity in a batch. Controls sampling strategy for metric learning.

Multi-GPU / Multi-Node

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

• Multi-GPU strategy: ddp_find_unused_parameters_true
• sync_batchnorm is always enabled
• Precision forced to FP16 (16-mixed)
• No explicit num_nodes config — single-node oriented