Tao Train Nvdinov2

NVDINOv2

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).

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 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 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
• AutoML metric contract: Use train_loss emitted by the train action and minimize it. This model has no packaged evaluate action, so do not invent an evaluation KPI; validate the selected student_epoch_* checkpoint through inference.

Per-Action Dataset Requirements

The managed runner may source these datasets as archives because references/skill_info.yaml declares runtime: extracted_folder. It must unpack the archive and place the resulting directory in images_dir. For direct Docker or TAO CLI runs, extract archives before launch; never set an images_dir field to .tar, .tar.gz, .tgz, or .zip.

Typical Spec Overrides

Data source overrides are mandatory for train and inference — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.

TRAIN_IMAGES_DIR = "/workspace/data/extracted/train/images_train"
TEST_IMAGES_DIR = "/workspace/data/extracted/test/images_test"

train (mandatory data sources):

{
    "train.num_gpus": 1,
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "dataset.train_dataset.images_dir": TRAIN_IMAGES_DIR,
}

local AutoML validation / smoke run: Use this shape when the goal is to confirm Bayesian launch, metric selection, best-model choice, and checkpoint persistence on local Docker. It keeps the run representative while avoiding the much slower ViT-Large default.

{
    "wandb.enable": False,
    "model.backbone.teacher_type": "vit_s",
    "model.backbone.student_type": "vit_s",
    "model.backbone.img_size": 224,
    "dataset.batch_size": 8,
    "dataset.workers": 2,
    "train.num_epochs": 1,
    "train.checkpoint_interval": 1,
    "train.num_prototypes": 1024,
    "train.precision": "32-true",
    "train.use_custom_attention": False,
    "train.num_gpus": 1,
    "dataset.train_dataset.images_dir": TRAIN_IMAGES_DIR,
}

export (mandatory checkpoint handoff):

{
    "export.checkpoint": "<selected train/AutoML student_epoch_* checkpoint>",
    "export.onnx_file": "/path/to/results/nvdinov2.onnx",
}

inference (mandatory data sources):

{
    "inference.checkpoint": "<selected train/AutoML student_epoch_* checkpoint>",
    "model.backbone.teacher_type": "<same value used for train>",
    "model.backbone.student_type": "<same value used for train>",
    "model.backbone.img_size": "<same value used for train>",
    "train.use_custom_attention": "<same value used for train>",
    # NVDINOv2 enables persistent_workers in its prediction DataLoader.
    # Keep this positive; dataset.workers=0 makes inference fail before loading data.
    "dataset.workers": 2,
    "dataset.test_dataset.images_dir": TEST_IMAGES_DIR,
}

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.