Tao Train Dinov3
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The tao-train-dinov3 skill provides a workflow for domain-adapting DINOv3 vision transformer backbones using self-supervised learning on unlabeled image data. It employs teacher-student self-distillation techniques, including DINO, iBOT, and KoLeo objectives, with optional Gram anchoring. The skill supports full training or parameter-efficient fine-tuning via LoRA, and includes utilities to convert, export, and run inference with trained teacher checkpoints.
What it does
- Performs continual self-supervised pre-training on unlabeled image datasets.
- Supports teacher-student self-distillation using DINO, iBOT, and KoLeo objectives.
- Enables parameter-efficient fine-tuning using LoRA adapters.
- Provides tools to convert, export, and perform inference with trained teacher checkpoints.
- Supports AutoML for automated hyperparameter optimization.
- Allows for high-resolution training configurations.
When to use it
- When you need to adapt a pre-trained DINOv3 backbone to a specific domain using unlabeled data.
- When you want to improve representation quality for downstream tasks like classification, detection, or segmentation.
- When you need to perform continual learning on a foundation model.
- When you require a standardized workflow to export or convert DINOv3 models for deployment.
Advantages
- Leverages advanced self-supervised objectives for robust feature learning.
- Supports LoRA for efficient adaptation without full parameter updates.
- Includes built-in support for AutoML to simplify hyperparameter tuning.
- Provides clear separation between training, evaluation, and deployment artifacts.
- Compatible with standard timm-format backbones.
Usage examples
- “Train a DINOv3 backbone on my unlabeled dataset.”
- The agent initiates a training run using the specified configuration, applying DINO/iBOT objectives to adapt the backbone to the provided images.
- “Export my trained DINOv3 teacher checkpoint for deployment.”
- The agent processes the selected teacher checkpoint and converts it into the required format for downstream inference.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-train-dinov3/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-train-dinov3.zip -d .agents/skills
Documentation by NVIDIA Corporation
DINOv3
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first.
Use this skill to continue self-supervised training of a DINOv3 backbone on unlabeled images, then convert, export, or run inference with a trained teacher checkpoint.
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:7.3.0-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[@]}" "$TAO_PYT_IMAGE" \
dinov3 train -e /specs/train.yamlInference:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
dinov3 inference -e /specs/inference.yamlExport:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
dinov3 export -e /specs/export.yamlConvert:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
dinov3 convert -e /specs/convert.yamlEvery 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.
Configuration
Use schemas/<action>.schema.json for supported fields, defaults, ranges, and options. Start from the matching references/spec_template_<action>.yaml.
Training is AutoML-enabled. Read references/skill_info.yaml and resolve an explicit automl_policy or user request before training. Default to automl_policy: on; requests such as "disable AutoML", "no HPO", or "plain training" mean off for that run. When it is on and the train schema and template are packaged, route training through tao-skill-bank:tao-run-automl. Use direct training only when the policy is off or those files are missing, and report the missing-file limitation. Non-train actions remain in this skill.
Treat train_loss as an optimization signal; use downstream evaluation when selecting a checkpoint for deployment.
For method background or tuning ideas, optionally read:
references/dinov3-method.mdreferences/dinov3-recipes.md
Core workflow
Obtain a compatible DINOv3 checkpoint in timm or TAO format.
Point
dataset.train_dataset.images_dirto a directory of unlabeled images.Configure the backbone, training scale, and output location.
Run training and evaluate useful teacher checkpoints on data representative of the downstream task.
Use the selected teacher checkpoint with
convert,export, orinference.
Provide either train.pretrained_model_path for a new run or train.resume_training_checkpoint_path to resume an existing run.
Essential fields
| Purpose | Spec field |
|---|---|
| Training images | dataset.train_dataset.images_dir |
| Initial weights | train.pretrained_model_path |
| Resume checkpoint | train.resume_training_checkpoint_path |
| Backbone | model.backbone.teacher_type, model.backbone.student_type |
| Training scale | dataset.batch_size, train.num_gpus, train.num_nodes |
| Output directory | results_dir |
| Conversion input | convert.checkpoint |
| Export input | export.checkpoint |
| Inference inputs | inference.checkpoint, dataset.test_dataset.images_dir |
Example train settings:
model:
backbone:
teacher_type: vit_b
student_type: vit_b
rope_theta: 100.0
dataset:
train_dataset:
images_dir: /path/to/unlabeled/images
batch_size: 16
train:
pretrained_model_path: /path/to/dinov3/weights
num_epochs: 10
num_gpus: 1Supported image extensions include jpg, jpeg, png, ppm, bmp, pgm, tif, tiff, and webp. Keep evaluation data separate from training data when measuring downstream quality.
Every dataset.*.images_dir value must be an extracted image folder. Managed
archive-backed sources use runtime: extracted_folder, which unpacks the source
before injecting the directory into the spec. For direct Docker or TAO CLI runs,
extract .tar, .tar.gz, .tgz, or .zip inputs before launch.
Checkpoints
| File | Use |
|---|---|
model_epoch_*_step_*.pth | Resume training, or export its EMA teacher |
teacher_epoch_*_step_*.pth | Convert, export, inference, or downstream evaluation |
student_epoch_*_step_*.pth | Diagnostics |
For export, prefer a selected stripped teacher_epoch_*_step_*.pth. Export also accepts
a selected full model_epoch_*_step_*.pth and deterministically selects and logs
teacher.backbone. Inference requires a stripped teacher checkpoint. Do not
substitute dinov3_model_latest.pth without evaluating that milestone.
Training loss does not directly measure downstream representation quality. When possible, compare candidate teacher checkpoints using metrics relevant to the target task.
For convert, export, and inference, copy the training-time backbone configuration into the action spec, including rope_theta. Stripped checkpoints do not restore the RoPE frequency base, so falling back to the default can change the trained model's behavior.
Key options
model.backbone.teacher_typeandstudent_typesupportvit_s,vit_s_plus,vit_b,vit_l,vit_h_plus, andvit_7b.When
model.distill.enable: false, the teacher and student backbone types must match.model.backbone.rope_thetacontrols the rotary frequency base. It defaults to100.0and is tunable; changing it alters positional encoding, so evaluate the resulting checkpoint for the intended task.model.centering_methodsupportssinkhornandsoftmax.model.gram.*controls optional Gram anchoring.model.lora.*enables parameter-efficient continual pre-training. The default targets the attentionqkvandprojprojections;num_last_blocks: 0adapts every transformer block.model.preservation.*adds CLS-token MSE and cosine losses against the frozen anchor teacher. It is recommended with LoRA when global embedding quality matters.train.log_every_n_stepscontrols step-level loss visibility and defaults to1, so short smoke runs emit component and preservation losses.train.precisiondefaults to16-mixed; choose precision based on hardware and measured quality.train.use_custom_attention: falseuses native attention when custom attention is unavailable.Crop and export dimensions need to be divisible by
model.backbone.patch_size.
Distributed training
For iterative GRIT or multi-task weakness mining from an unlabeled pool followed
by DINOv3 SSL retraining, use the related tao-run-dinov3-ssl-deft application.
Ordinary pretraining and fine-tuning remain in this model skill.
Use train.num_gpus, train.gpu_ids, train.num_nodes, and train.distributed_strategy. Start with auto; use ddp or fsdp when appropriate for the selected backbone, resolution, and available memory.
Common issues
Checkpoint load failure: verify that the checkpoint is DINOv3 and that its backbone matches the configured teacher and student types.
No images found or archive rejected: pass an extracted image folder to
dataset.*.images_dir, not an archive file.Out of memory: reduce batch size or image resolution, or use a sharded distributed strategy.
Custom-attention failure: set
train.use_custom_attention: false.Deterministic-backward failure: xformers memory-efficient attention does not provide a deterministic backward kernel; use
train.cudnn.deterministic: false.Grid-size error: choose crop and export dimensions divisible by the patch size.
Unexpected inference keys: use a stripped
teacher_epoch_*_step_*.pthcheckpoint.Full-checkpoint export: a selected
model_epoch_*_step_*.pthis supported; export selects and logs its EMAteacher.backbone.Resume mismatch: the SSL dataloader is not step-resumable, so resume from an epoch/checkpoint boundary when reproducibility matters.
Files
- ATTRIBUTION.mdAdded by AutoGineer918 B
- BENCHMARK.md7.1 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md8.8 KiB
- config/skillspector-baseline.yaml1.0 KiB
- evals/evals.json2.2 KiB
- references/dinov3-method.md2.3 KiB
- references/dinov3-recipes.md2.9 KiB
- references/skill_info.yaml3.5 KiB
- references/spec_template_convert.yaml2.6 KiB
- references/spec_template_export.yaml2.5 KiB
- references/spec_template_inference.yaml2.3 KiB
- references/spec_template_train.yaml3.9 KiB
- references/spec_template_train_highres.yaml3.3 KiB
- schemas/convert.schema.json56.0 KiB
- schemas/export.schema.json56.9 KiB
- schemas/inference.schema.json56.9 KiB
- schemas/manifest.json19.8 KiB
- schemas/train.schema.json54.1 KiB
- skill-card.md4.7 KiB
- skill.oms.sig7.8 KiB
schemas/train.schema.json
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"num_last_blocks": 0,
"rank": 8,
"target_modules": [
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"proj"
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"cls_mse_weight": 0.05,
"enable": false
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"default": {
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"img_size": 256,
"num_register_tokens": 4,
"patch_size": 16,
"rope_theta": 100.0,
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"teacher_type": "vit_b"
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"description": "Configuration for the DINOv3 backbone",
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"num_register_tokens",
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"properties": {
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"description": "Drop path rate for stochastic depth regularization",
"popular": true,
"title": "drop path rate",
"type": "float"
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"img_size": {
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"description": "Backbone image size. Supported values are 256, 512, and 768.",
"enum": [
256,
512,
768
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"popular": true,
"title": "image size",
"type": "ordered_int"
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"num_register_tokens": {
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"description": "Number of register tokens (DINOv3 ViT-B uses 4)",
"maximum": Infinity,
"minimum": 0,
"popular": true,
"title": "num register tokens",
"type": "int"
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"patch_size": {
"default": 16,
"description": "Size of patches (DINOv3 uses patch 16)",
"enum": [
16
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"popular": true,
"title": "patch size",
"type": "ordered_int"
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"rope_theta": {
"default": 100.0,
"description": "Frequency base for 2D axial RoPE. Must match the timm DINOv3 reference; verified by the step-4 feature-parity smoke test.",
"popular": true,
"title": "RoPE theta",
"type": "float"
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"default": "vit_b",
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"vit_s_plus",
"vit_b",
"vit_l",
"vit_h_plus",
"vit_7b"
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"title": "student backbone",
"type": "categorical"
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"teacher_type": {
"default": "vit_b",
"description": "Teacher backbone name. TAO's DINOv3 supports vit_s, vit_s_plus, vit_b, vit_l, vit_h_plus and vit_7b.",
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"vit_s",
"vit_s_plus",
"vit_b",
"vit_l",
"vit_h_plus",
"vit_7b"
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"title": "teacher backbone",
"type": "categorical"
}
},
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"default": "sinkhorn",
"description": "Teacher-output centering for the DINO/iBOT heads. DINOv3 uses Sinkhorn-Knopp (SwAV); 'softmax' is the DINOv2 fallback. If training shows instability/collapse, try 'softmax'.",
"enum": [
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"softmax"
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"popular": true,
"title": "centering method",
"type": "categorical"
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"automl_enabled": false,
"default": {
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"enable": false,
"pretrained_non_distill_pl_model_path": ""
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"description": "Configuration for distillation (reused from nvdinov2)",
"popular": [
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"disable_masking"
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"properties": {
"disable_masking": {
"default": false,
"description": "Whether to disable masking when distillation",
"popular": true,
"title": "disable_masking",
"type": "bool"
},
"enable": {
"default": false,
"description": "Whether to run distillation",
"popular": true,
"title": "distillation",
"type": "bool"
},
"pretrained_non_distill_pl_model_path": {
"default": "",
"description": "Path to a pre-trained pl model from non-distillation DINOv2 SSL pipe for initializing teacher in distillation.",
"type": "string"
}
},
"type": "collection"
},
"gram": {
"automl_enabled": false,
"default": {
"enable": false,
"refresh_interval": 0,
"start_step": 0,
"teacher_scale": 1.0,
"teacher_source": "pretrained",
"w_gram": 0.0
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"description": "Configuration for DINOv3 Gram anchoring",
"popular": [
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"enable",
"w_gram"
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"properties": {
"enable": {
"default": false,
"description": "Whether to add the Gram-anchoring loss term",
"popular": true,
"title": "enable gram",
"type": "bool"
},
"refresh_interval": {
"default": 0,
"description": "Steps between refreshing the Gram teacher from the EMA teacher (only when teacher_source='ema'). 0 = never refresh (frozen snapshot, Phase 0 behavior).",
"maximum": Infinity,
"minimum": 0,
"title": "gram refresh interval",
"type": "int"
},
"start_step": {
"default": 0,
"description": "Global step at which the Gram term activates",
"maximum": Infinity,
"minimum": 0,
"popular": true,
"title": "gram start step",
"type": "int"
},
"teacher_scale": {
"default": 1.0,
"description": "Resolution multiple at which the Gram teacher runs relative to the student. DINOv3 computes Gram on higher-res teacher features; the teacher grid is average-pooled back to the student grid before the loss. 1.0 = same resolution (default, memory-safe). The paper uses 2.0, but at 768 the teacher then runs at 1536 (96x96=9216 tokens) -> heavy; raise to 1.5/2.0 only if memory allows.",
"title": "gram teacher scale",
"type": "float"
},
"teacher_source": {
"default": "pretrained",
"description": "Source of the frozen Gram teacher weights: 'pretrained' anchors to the loaded DINOv3 weights (Phase 0 default); 'ema' uses an early-EMA snapshot of the current run's teacher, refreshed every 'refresh_interval' steps (Phase 1 / high-res).",
"enum": [
"pretrained",
"ema"
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"title": "gram teacher source",
"type": "categorical"
},
"w_gram": {
"default": 0.0,
"description": "Weight of the Gram-anchoring loss term",
"popular": true,
"title": "gram weight",
"type": "float"
}
},
"type": "collection"
},
"head": {
"automl_enabled": false,
"default": {
"bottleneck_dim": 384,
"hidden_dim": 2048,
"num_layers": 3
},
"description": "Configuration for the DINOv3 head (reused from nvdinov2)",
"popular": [
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"num_layers",
"hidden_dim"
],
"properties": {
"bottleneck_dim": {
"default": 384,
"description": "Dimension of the bottleneck layer in the NVDINOv2 head",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "bottleneck dimension",
"type": "int"
},
"hidden_dim": {
"default": 2048,
"description": "Dimension of the hidden layers in the NVDINOv2 head",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "hidden dimension",
"type": "int"
},
"num_layers": {
"default": 3,
"description": "Number of layers in the NVDINOv2 head",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "number of Layers",
"type": "int"
}
},
"type": "collection"
},
"lora": {
"automl_disabled_parameters": [
"model.lora.target_modules"
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"automl_enabled": false,
"default": {
"alpha": 16.0,
"dropout": 0.05,
"enable": false,
"num_last_blocks": 0,
"rank": 8,
"target_modules": [
"qkv",
"proj"
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},
"description": "Configuration for LoRA parameter-efficient continual pre-training",
"popular": [
"rank",
"num_last_blocks",
"alpha",
"target_modules",
"enable"
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"properties": {
"alpha": {
"default": 16.0,
"description": "LoRA scaling alpha; the applied scale is alpha / rank.",
"popular": true,
"title": "lora alpha",
"type": "float"
},
"dropout": {
"default": 0.05,
"description": "Dropout applied to the LoRA branch input only (the frozen base path is untouched).",
"maximum": 1.0,
"minimum": 0.0,
"title": "lora dropout",
"type": "float"
},
"enable": {
"default": false,
"description": "Whether to apply LoRA adapters to the backbone (parameter-efficient SSL)",
"popular": true,
"title": "enable lora",
"type": "bool"
},
"num_last_blocks": {
"default": 0,
"description": "Adapt only the last N transformer blocks. 0 = all blocks.",
"maximum": Infinity,
"minimum": 0,
"popular": true,
"title": "lora num last blocks",
"type": "int"
},
"rank": {
"default": 8,
"description": "LoRA rank (r). The low-rank update is dW = (alpha/r) * B @ A.",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "lora rank",
"type": "int"
},
"target_modules": {
"automl_enabled": false,
"default": [
"qkv",
"proj"
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"description": "Projections to adapt inside each targeted ViT block. 'qkv' and 'proj' are the attention projections (the v1 default); 'fc1'/'fc2' additionally adapt the FFN. SwiGLU backbones (ViT-S+/H+/7B) expose a fused fc1 and are deferred in v1.",
"popular": true,
"title": "lora target modules",
"type": "list"
}
},
"type": "collection"
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"preservation": {
"automl_enabled": false,
"default": {
"cls_cosine_weight": 0.05,
"cls_mse_weight": 0.05,
"enable": false
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"description": "Configuration for CLS-token preservation against the frozen anchor teacher",
"popular": [
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"cls_cosine_weight",
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"properties": {
"cls_cosine_weight": {
"default": 0.05,
"description": "Weight of the CLS-token cosine preservation term (0 disables just this term).",
"maximum": Infinity,
"minimum": 0.0,
"popular": true,
"title": "cls cosine weight",
"type": "float"
},
"cls_mse_weight": {
"default": 0.05,
"description": "Weight of the CLS-token MSE preservation term (0 disables just this term).",
"maximum": Infinity,
"minimum": 0.0,
"popular": true,
"title": "cls mse weight",
"type": "float"
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"enable": {
"default": false,
"description": "Whether to add CLS-token preservation losses against the frozen anchor teacher. Recommended alongside LoRA for continual pre-training.",
"popular": true,
"title": "enable preservation",
"type": "bool"
}
},
"type": "collection"
}
},
"type": "collection"
},
"model_name": {
"default": "",
"description": "Name of model if invoking task via :code:`model_agnostic`",
"title": "Model name",
"type": "string"
},
"results_dir": {
"default": "",
"description": "\n Path to where all the assets generated from a task are stored.\n ",
"title": "Results directory",
"type": "string"
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"train.cudnn",
"train.checkpointer",
"train.schedulers",
"train.optim"
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"automl_enabled": false,
"default": {
"checkpoint_interval": 1,
"checkpoint_interval_unit": "epoch",
"checkpointer": {
"auto_insert_metric_name": false,
"enable_topk": false,
"filename": "model_best_{epoch:03d}",
"replace_periodic": false,
"save_top_k": 1
},
"clip_grad_norm": 3.0,
"cudnn": {
"benchmark": true,
"deterministic": false
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"distributed_strategy": "auto",
"gpu_ids": [
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"layerwise_decay": 1.0,
"log_every_n_steps": 1,
"num_epochs": 10,
"num_gpus": 1,
"num_nodes": 1,
"num_prototypes": 131072,
"optim": {
"optim": "adamw"
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"precision": "16-mixed",
"results_dir": "",
"resume_training_checkpoint_path": "",
"schedulers": {
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"max_decay_steps": 2500000,
"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
"warm_up_steps": 100000
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"learning_rate": {
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"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
"warm_up_steps": 100000
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"momentum": {
"max_decay_steps": 2500000,
"val_base": 0.994,
"val_final": 1.0,
"val_start": 0.0,
"warm_up_steps": 0
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"teacher_temperature": {
"max_decay_steps": 37500,
"val_base": 0.07,
"val_final": 0.07,
"val_start": 0.04,
"warm_up_steps": 37500
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"weight_decay": {
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"val_base": 0.04,
"val_final": 0.2,
"val_start": 0.0,
"warm_up_steps": 0
}
},
"seed": 1234,
"use_custom_attention": true,
"validation_interval": 1
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"description": "Configurable parameters to construct the trainer for a DINOv3 experiment.",
"popular": [
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"optim",
"gpu_ids",
"validation_interval",
"num_gpus",
"precision",
"num_prototypes",
"schedulers",
"distributed_strategy",
"num_nodes",
"clip_grad_norm",
"num_epochs",
"use_custom_attention"
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"properties": {
"checkpoint_interval": {
"default": 1,
"description": "The interval (in epochs) at which a checkpoint will be saved. Helps resume training.",
"minimum": 1,
"popular": true,
"title": "Checkpoint interval",
"type": "int"
},
"checkpoint_interval_unit": {
"default": "epoch",
"description": "The unit of the checkpoint interval.",
"enum": [
"epoch",
"step"
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"title": "Checkpoint interval unit",
"type": "categorical"
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"checkpointer": {
"automl_enabled": false,
"default": {
"auto_insert_metric_name": false,
"enable_topk": false,
"filename": "model_best_{epoch:03d}",
"replace_periodic": false,
"save_top_k": 1
},
"properties": {
"auto_insert_metric_name": {
"default": false,
"type": "bool"
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"dirpath": {
"description": "Directory for best checkpoints. Defaults to results_dir.",
"type": "string"
},
"enable_topk": {
"default": false,
"description": "Save checkpoint(s) ranked by a monitored metric. This is additive to periodic checkpoints unless replace_periodic is enabled.",
"title": "Enable best-checkpoint saving",
"type": "bool"
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"filename": {
"default": "model_best_{epoch:03d}",
"title": "Best-checkpoint filename pattern",
"type": "string"
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"mode": {
"description": "Leave unset to use the network's default direction.",
"enum": [
"min",
"max"
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"title": "Monitor mode",
"type": "categorical"
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"monitor": {
"description": "Leave unset to use the network's default metric.",
"title": "Monitored metric",
"type": "string"
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"replace_periodic": {
"default": false,
"description": "When best-checkpoint saving is enabled, replace periodic history with one metric-best checkpoint and its latest symlink.",
"title": "Replace periodic checkpoints",
"type": "bool"
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"save_top_k": {
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"minimum": 1,
"title": "Number of best checkpoints",
"type": "int"
}
},
"type": "collection"
},
"clip_grad_norm": {
"default": 3.0,
"description": "Value to clip gradients norm",
"popular": true,
"title": "clip gradient norm",
"type": "float"
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"cudnn": {
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"default": {
"benchmark": true,
"deterministic": false
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"description": "CuDNN settings compatible with DINOv3 custom attention.",
"properties": {
"benchmark": {
"default": true,
"description": "Enable CuDNN benchmarking for DINOv3 training.",
"title": "CuDNN benchmark",
"type": "bool"
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"deterministic": {
"default": false,
"description": "Enable deterministic CuDNN behavior. Keep disabled when using DINOv3 custom attention, which has no deterministic backward implementation.",
"title": "CuDNN deterministic",
"type": "bool"
}
},
"type": "collection"
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"distributed_strategy": {
"default": "auto",
"description": "Lightning distributed strategy. 'auto' keeps the nvdinov2 behaviour (single-device or DDP). 'fsdp' shards params/grads/optimizer (FULL_SHARD) for high-resolution and large-backbone multi-GPU training.",
"enum": [
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"ddp",
"fsdp"
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"popular": true,
"title": "distributed strategy",
"type": "categorical"
},
"gpu_ids": {
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"default": [
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"description": "\n List of GPU IDs to run the training on. The length of this list\n must be equal to the number of gpus in train.num_gpus.",
"popular": true,
"title": "GPU IDs",
"type": "list"
},
"layerwise_decay": {
"default": 1.0,
"description": "Layerwise decay factor",
"popular": true,
"title": "layerwise decay factor",
"type": "float"
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"log_every_n_steps": {
"default": 1,
"description": "Number of training steps between logger updates.",
"minimum": 1,
"title": "logging interval",
"type": "int"
},
"num_epochs": {
"default": 10,
"description": "Number of epochs to run the training.",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "Number of epochs",
"type": "int"
},
"num_gpus": {
"default": 1,
"description": "The number of GPUs to run the train job.",
"minimum": 1,
"popular": true,
"title": "Number of GPUs",
"type": "int"
},
"num_nodes": {
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"description": "Number of nodes to run the training on. If > 1, then multi-node is enabled.",
"minimum": 1,
"popular": true,
"title": "Number of nodes",
"type": "int"
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"num_prototypes": {
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"description": "Number of prototypes",
"popular": true,
"title": "number of prototypes",
"type": "int"
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"optim": {
"automl_enabled": false,
"default": {
"optim": "adamw"
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"description": "Optimizer configuration for NVDINOv2",
"popular": [
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"properties": {
"optim": {
"default": "adamw",
"description": "Optimizer type",
"enum": [
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""
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"popular": true,
"title": "optimizer",
"type": "categorical"
}
},
"type": "collection"
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"precision": {
"default": "16-mixed",
"description": "Precision",
"popular": true,
"title": "precision",
"type": "string"
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"pretrained_model_path": {
"description": "Path to DINOv3 pretrained weights matching the configured backbone. Accepts a timm-format directory or file, or a stripped TAO DINOv3 backbone checkpoint. DINOv2/NVDINOv2 checkpoints are not supported.",
"type": "string"
},
"results_dir": {
"default": "",
"description": "\n Path to where all the assets generated from a task are stored.\n ",
"title": "Results directory",
"type": "string"
},
"resume_training_checkpoint_path": {
"default": "",
"description": "Path to the checkpoint to resume training from.",
"title": "Resume checkpoint path",
"type": "string"
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"automl_enabled": false,
"default": {
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"max_decay_steps": 2500000,
"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
"warm_up_steps": 100000
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"learning_rate": {
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"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
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"momentum": {
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"val_base": 0.994,
"val_final": 1.0,
"val_start": 0.0,
"warm_up_steps": 0
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"teacher_temperature": {
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"val_base": 0.07,
"val_final": 0.07,
"val_start": 0.04,
"warm_up_steps": 37500
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"weight_decay": {
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"val_base": 0.04,
"val_final": 0.2,
"val_start": 0.0,
"warm_up_steps": 0
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},
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"popular": [
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"properties": {
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"automl_enabled": false,
"default": {
"freeze_steps": 1250,
"max_decay_steps": 2500000,
"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
"warm_up_steps": 100000
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"description": "Last layer learning rate scheduler configuration",
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"properties": {
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"popular": true,
"title": "freeze steps",
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"max_decay_steps": {
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"description": "Maximum decay steps",
"popular": true,
"title": "max decay steps",
"type": "int"
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"val_base": {
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"description": "The value after warm-up for scheduler.",
"popular": true,
"title": "base value",
"type": "float"
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"val_final": {
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"description": "Final value for scheduler",
"popular": true,
"title": "final value",
"type": "float"
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"val_start": {
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"description": "Starting value for scheduler",
"popular": true,
"title": "starting value",
"type": "float"
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"warm_up_steps": {
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"description": "Number of warm-up steps",
"popular": true,
"title": "warm-up steps",
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},
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"default": {
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"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
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"popular": true,
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"val_base": {
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"description": "The value after warm-up for scheduler",
"popular": true,
"title": "base value",
"type": "float"
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"val_final": {
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"description": "Final value for scheduler",
"popular": true,
"title": "final value",
"type": "float"
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"val_start": {
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"description": "Starting value for scheduler",
"popular": true,
"title": "starting value",
"type": "float"
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"warm_up_steps": {
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"description": "Number of warm-up steps",
"popular": true,
"title": "warm-up steps",
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"teacher_temperature": {
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"type": "int"
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"validation_interval": {
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"minimum": 1,
"popular": true,
"title": "Validation interval",
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},
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},
"wandb": {
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"automl_enabled": false,
"default": {
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"entity": "",
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"project": "TAO Toolkit",
"reinit": false,
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"save_code": false,
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"tags": [
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"tags": {
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},
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}
},
"type": "object",
"x_tao_schema": {
"action": "train",
"core_module": "dinov3",
"model": "tao-train-dinov3",
"network_arch": "dinov3",
"schema_action": "train",
"schema_version": 1,
"source": "tao-core dataclass config"
}
}
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs a command-line tool
The author says: Requires docker + nvidia-container-toolkit.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-train-dinov3 · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-train-dinov3.zip