tao-train-reid

Re-Identification

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.

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: re_identification
• Formats: default
• Monitoring metric: cmc_rank_1

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	evaluate.test_dataset	train_datasets	sample_test.tar.gz	No
evaluate	evaluate.query_dataset	train_datasets	sample_query.tar.gz	No
inference	inference.test_dataset	train_datasets	sample_test.tar.gz	No
inference	inference.query_dataset	train_datasets	sample_query.tar.gz	No
train	dataset.train_dataset_dir	train_datasets	sample_train.tar.gz	No
train	dataset.test_dataset_dir	train_datasets	sample_test.tar.gz	No
train	dataset.query_dataset_dir	train_datasets	sample_query.tar.gz	No

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": 100,
    "num_workers": 4,
    "batch_size": 16,
    "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):

{
    "evaluate.test_dataset": f"{S3_TRAIN}/sample_test.tar.gz",
    "evaluate.query_dataset": f"{S3_TRAIN}/sample_query.tar.gz",
}

inference (mandatory data sources):

{
    "inference.test_dataset": f"{S3_TRAIN}/sample_test.tar.gz",
    "inference.query_dataset": f"{S3_TRAIN}/sample_query.tar.gz",
}

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).

Spec Key	Description	Default
train.num_gpus	Number of GPUs	1
train.gpu_ids	GPU device indices	[0]

• 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

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Re-ID models are relatively lightweight but benefit from large batch sizes for metric learning.

Error Patterns

num_classes mismatch: Ensure dataset.num_classes equals the number of unique identity folders in the training set.

Query/gallery mismatch: Query and test (gallery) datasets must share the same identity namespace.

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 re_identification.config.json:

Action	Spec Field	Inference Function	Meaning
evaluate	encryption_key	key	encryption key
evaluate	evaluate.checkpoint	parent_model	model file inferred from the parent job results folder
evaluate	evaluate.output_cmc_curve_plot	create_evaluate_cmc_plot_reid	ReID CMC plot path
evaluate	evaluate.output_sampled_matches_plot	create_evaluate_matches_plot_reid	ReID sampled matches plot path
evaluate	results_dir	output_dir	current job results directory
export	encryption_key	key	encryption key
export	export.checkpoint	parent_model	model file inferred from the parent job results folder
export	export.onnx_file	create_onnx_file	output ONNX path
export	results_dir	output_dir	current job results directory
inference	encryption_key	key	encryption key
inference	inference.checkpoint	parent_model	model file inferred from the parent job results folder
inference	inference.output_file	create_inference_result_file_reid	ReID inference JSON path
inference	results_dir	output_dir	current job results directory
train	encryption_key	key	encryption key
train	model.pretrained_model_path	ptm_if_no_resume_model	PTM when no resume checkpoint exists
train	results_dir	output_dir	current job results directory
train	train.resume_training_checkpoint_path	resume_model	model file inferred from the current job results folder

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.