Tao Train Nvdinov2

NVDINOv2

NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels. Produces general-purpose visual features.

Set train.pretrained_model_path for pretrained ViT weights.

For TAO Deploy TensorRT actions (gen_trt_engine), read references/tao-deploy-nvdinov2.md first. Deploy spec templates live in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.

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: image_classification
• Formats: ssl
• Monitoring metric: train_loss

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"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

{
    "train.num_gpus": 1,
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "dataset.train_dataset.images_dir": f"{S3_TRAIN}/images_train.tar.gz",
}

distill (mandatory data sources):

{
    "dataset.train_dataset.images_dir": f"{S3_TRAIN}/images_train.tar.gz",
}

inference (mandatory data sources):

{
    "dataset.test_dataset.images_dir": f"{S3_EVAL}/images_test.tar.gz",
}

Eval Dataset

Optional. SSL training does not use labels. Evaluation is downstream task-specific.

Important Parameters

• model.backbone.teacher_type: Teacher ViT variant. Default vit_l (ViT-Large).
• model.backbone.student_type: Student ViT variant. Default vit_l. Typically matches teacher.
• model.backbone.img_size: Input image size. Default 518. Higher resolution produces better features but costs more memory.
• model.backbone.patch_size: ViT patch size. Default 14.
• dataset.batch_size: Per-GPU batch size. Default 4. SSL training is memory-intensive due to dual (teacher+student) forward passes.
• train.layerwise_decay: Layer-wise learning rate decay. Important for ViT fine-tuning.
• train.clip_grad_norm: Gradient clipping. Important for stable SSL training.

Multi-GPU / Multi-Node

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

• Strategy: auto (Lightning picks best strategy automatically)
• sync_batchnorm is always enabled — critical for SSL training with teacher-student framework
• Multi-GPU strongly recommended (4-8 GPUs) for meaningful SSL training

Multi-node env vars (set by orchestrator): WORLD_SIZE, NODE_RANK, MASTER_ADDR, MASTER_PORT, NUM_GPU_PER_NODE.

Hardware

Minimum 4 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. SSL with ViT-Large teacher+student is very memory-intensive. Requires A100 40GB+ GPUs. Multi-GPU strongly recommended.

Error Patterns

CUDA out of memory: ViT-Large teacher+student with img_size=518 requires 40GB+ GPU memory. Reduce batch_size, img_size, or use smaller ViT variant.

Slow convergence: SSL needs many epochs. Default 10 is for quick testing; production runs typically use 100+ epochs.

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 nvdinov2.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.

Deployment

• tao-deploy-nvdinov2 — NvDINOv2 deploy workflow for TensorRT engine generation using TAO Deploy.