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

Action	Spec Key	Source	Files	List?
distill	dataset.train_dataset.images_dir	train_datasets	images_train.tar.gz	No
inference	dataset.test_dataset.images_dir	inference_dataset	images_test.tar.gz	No
train	dataset.train_dataset.images_dir	train_datasets	images_train.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

python

S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

python

{
    "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):

python

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

inference (mandatory data sources):

python

{
    "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).

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

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

Action	Spec Field	Inference Function	Meaning
distill	`encryption_key`	`key`	encryption key
distill	`model.distill.pretrained_non_distill_pl_model_path`	`parent_model`	model file inferred from the parent job results folder
distill	`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.trt_engine`	`parent_model`	model file inferred from the parent job results folder
inference	`results_dir`	`output_dir`	current job results directory
train	`encryption_key`	`key`	encryption key
train	`results_dir`	`output_dir`	current job results directory
train	`train.pretrained_model_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`train.resume_training_checkpoint_path`	`resume_model`	model file inferred from the current job results folder

For

parent_model

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.

tao-train-nvdinov2

NPX Install

Tags

SKILL.md Content

NVDINOv2

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference

Deployment