Tao Train Nvdinov2
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-nvdinov2 skill provides a framework for training vision transformers via self-distillation without labels. It supports the full lifecycle of NVDINOv2 models, including training, exporting, and inference. The skill is integrated with TAO AutoML and includes specific requirements for dataset formatting and hardware configurations, such as the use of multi-GPU setups for memory-intensive SSL training.
What it does
- Trains vision transformers using self-supervised teacher-student distillation.
- Generates general-purpose visual features from image data.
- Supports AutoML for automated hyperparameter optimization during training.
- Exports trained models to ONNX format for downstream deployment.
- Provides inference capabilities for validating trained checkpoints.
- Includes templates for generating TensorRT engines via TAO Deploy.
When to use it
- When you need to perform self-supervised pretraining on vision transformer backbones.
- When you want to leverage TAO AutoML to optimize training parameters.
- When you need to prepare visual features for downstream computer vision tasks.
- When you are ready to export a trained model for deployment in a TensorRT environment.
Advantages
- Enables label-free visual representation learning.
- Automates hyperparameter tuning through integrated AutoML support.
- Provides a consistent workflow from training to deployment.
- Includes clear guidelines for handling memory-intensive ViT training.
Usage examples
- “Train an NVDINOv2 model with AutoML enabled.”
- The agent initiates a training job using the NVDINOv2 backbone, applying AutoML policies to optimize the training process based on the provided dataset.
- “Export my trained NVDINOv2 checkpoint to ONNX.”
- The agent uses the specified training checkpoint to generate an ONNX model file suitable for deployment.
- “Run inference on a test dataset using my NVDINOv2 model.”
- The agent loads the specified student checkpoint and runs inference on the provided test images directory.
- “Generate a TensorRT engine for my exported NVDINOv2 model.”
- The agent utilizes the TAO Deploy workflow to convert the ONNX model into a TensorRT engine.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-train-nvdinov2/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-train-nvdinov2.zip -d .agents/skills
Documentation by NVIDIA Corporation
NVDINOv2
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill 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_lossemitted by the train action and minimize it. This model has no packaged evaluate action, so do not invent an evaluation KPI; validate the selectedstudent_epoch_*checkpoint through inference.
Per-Action Dataset Requirements
| Action | Spec Key | Source | Source artifact → runtime value | List? |
|---|---|---|---|---|
| inference | dataset.test_dataset.images_dir | inference_dataset | images_test.tar.gz → extracted image folder | No |
| train | dataset.train_dataset.images_dir | train_datasets | images_train.tar.gz → extracted image folder | No |
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).
| 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_batchnormis always enabled — critical for SSL training with teacher-student frameworkMulti-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
No images found / archive path rejected: dataset.*.images_dir must be an
extracted directory containing images, not an archive. Extract .tar,
.tar.gz, .tgz, or .zip inputs first. Managed archive-backed sources must
use the directory produced by runtime: extracted_folder.
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.
Inference checkpoint has unexpected Lightning keys: For downstream
inference, pass the selected AutoML run's student_epoch_*.pth checkpoint,
not nvdinov2_model_latest.pth. The latest file is a training checkpoint and
the inference loader reports unexpected keys such as state_dict, optimizer
state, and scheduler state.
Inference fails with persistent_workers option needs num_workers > 0:
Set dataset.workers to a positive value (use 2 for a minimal local run).
The NVDINOv2 prediction DataLoader enables persistent workers and cannot run
with the generic zero-worker smoke-test override.
Export checkpoint has unexpected Lightning keys: Export also consumes the
selected student_epoch_*.pth checkpoint. Use the full model_epoch_*.pth
checkpoint only for resume/retrain via train.resume_training_checkpoint_path.
TensorRT engine passed to PyT inference: The packaged PyT nvdinov2 inference
implementation only loads .pth or .tlt model paths. TAO Deploy
gen_trt_engine builds a TensorRT engine for downstream consumers, but the PyT
inference action does not run on that engine.
Separate distill action not available: The current TAO PyT CLI exposes
export, inference, train, and default_specs for NvDINOv2. Do not launch
or advertise a standalone nvdinov2 distill action.
AutoML metric not found: TAO's status KPI reports the final training scalar
as train_loss. Use train_loss with minimize direction for AutoML selection.
Some Lightning progress lines also render the same scalar as
train_loss_epoch; treat that as a fallback alias only, not the primary
monitoring metric.
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.
Model-specific handoff mappings:
| Action | Spec Field | Inference Function | Meaning |
|---|---|---|---|
| export | encryption_key | key | encryption key |
| export | export.checkpoint | parent_model | selected student_epoch_*.pth checkpoint 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 | selected student_epoch_*.pth checkpoint 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 | selected full model_epoch_*.pth training checkpoint 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.
Deployment
Files
- ATTRIBUTION.mdAdded by AutoGineer924 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md12.1 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json796 B
- references/skill_info.yaml2.9 KiB
- references/spec_template_deploy_gen_trt_engine.yaml514 B
- references/spec_template_distill.yaml2.1 KiB
- references/spec_template_export.yaml2.3 KiB
- references/spec_template_inference.yaml2.2 KiB
- references/spec_template_train.yaml2.1 KiB
- references/tao-deploy-nvdinov2.md3.9 KiB
- references/tao-deploy-nvdinov2.skill_info.yaml1.6 KiB
- schemas/distill.schema.json40.0 KiB
- schemas/export.schema.json42.7 KiB
- schemas/inference.schema.json42.8 KiB
- schemas/manifest.json12.3 KiB
- schemas/train.schema.json40.0 KiB
- skill-card.md4.1 KiB
- skill.oms.sig7.9 KiB
schemas/inference.schema.json
{
"automl_default_parameters": [
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"dataset.batch_size"
],
"automl_disabled_parameters": [
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"model.backbone",
"dataset.train_dataset",
"dataset.transform.local_crops_scale",
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"gen_trt_engine",
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"model.distill",
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"default": {
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"test_dataset": {
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},
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},
"transform": {
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],
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"n_global_crops": 2,
"n_local_crops": 8
},
"workers": 8
},
"encryption_key": "",
"inference": {
"batch_size": 8,
"checkpoint": "???",
"gpu_ids": [
0
],
"num_gpus": 1,
"num_nodes": 1,
"results_dir": "",
"trt_engine": "",
"vis_after_n_batches": 1
},
"model": {
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"img_size": 518,
"num_register_tokens": 0,
"patch_size": 14,
"student_type": "vit_l",
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},
"distill": {
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"enable": false,
"pretrained_non_distill_pl_model_path": ""
},
"head": {
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"hidden_dim": 2048,
"num_layers": 3
}
},
"model_name": "",
"results_dir": "",
"train": {
"checkpoint_interval": 1,
"checkpoint_interval_unit": "epoch",
"clip_grad_norm": 3.0,
"cudnn": {
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"deterministic": true
},
"gpu_ids": [
0
],
"layerwise_decay": 1.0,
"num_epochs": 10,
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"num_nodes": 1,
"num_prototypes": 131072,
"optim": {
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},
"precision": "16-mixed",
"pretrained_model_path": "",
"results_dir": "",
"resume_training_checkpoint_path": "",
"schedulers": {
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"freeze_steps": 1250,
"max_decay_steps": 2500000,
"val_base": 7.07e-06,
"val_final": 1e-06,
"val_start": 0.0,
"warm_up_steps": 100000
},
"learning_rate": {
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"val_base": 7.07e-06,
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"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,
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"teacher_temperature": {
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"val_base": 0.07,
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},
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"val_base": 0.04,
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}
},
"seed": 1234,
"use_custom_attention": true,
"validation_interval": 1
},
"wandb": {
"enable": true,
"entity": "",
"group": "",
"name": "TAO Toolkit Training",
"project": "TAO Toolkit",
"reinit": false,
"run_id": "",
"save_code": false,
"sync_tensorboard": false,
"tags": [
"tao-toolkit"
]
}
},
"popular": {
"dataset": {
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"pin_memory": true,
"transform": {
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"local_crops_scale": [
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"local_crops_size": 98,
"n_global_crops": 2,
"n_local_crops": 8
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"workers": 8
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"gen_trt_engine": {
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"tensorrt": {
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"model": {
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"distill": {
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"head": {
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},
"train": {
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"gpu_ids": [
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"optim": {
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"precision": "16-mixed",
"schedulers": {
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}
},
"use_custom_attention": true,
"validation_interval": 1
}
},
"properties": {
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"dataset",
"train",
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],
"dataset": {
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],
"automl_disabled_parameters": [
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],
"automl_enabled": false,
"default": {
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"test_dataset": {
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},
"train_dataset": {
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},
"transform": {
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],
"local_crops_size": 98,
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"workers": 8
},
"description": "Configurable parameters to construct the dataset for a NVDINOv2 experiment.",
"popular": [
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"properties": {
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"popular": true,
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"type": "int"
},
"pin_memory": {
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"description": "Flag to enable the dataloader to allocated pagelocked memory for faster of data between the CPU and GPU.",
"popular": true,
"title": "pin_memory",
"type": "bool"
},
"test_dataset": {
"automl_enabled": false,
"default": {
"images_dir": ""
},
"description": "Configuration for the testing dataset path",
"properties": {
"images_dir": {
"default": "",
"description": "Path to an extracted images directory for the dataset. Archive files such as .tar, .tar.gz, .tgz, and .zip are not supported.",
"title": "image directory",
"type": "string"
}
},
"title": "Testing Dataset",
"type": "collection"
},
"train_dataset": {
"automl_enabled": false,
"default": {
"images_dir": ""
},
"description": "Configuration for the training dataset path",
"properties": {
"images_dir": {
"default": "",
"description": "Path to an extracted images directory for the dataset. Archive files such as .tar, .tar.gz, .tgz, and .zip are not supported.",
"title": "image directory",
"type": "string"
}
},
"title": "Training Dataset",
"type": "collection"
},
"transform": {
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"dataset.transform.local_crops_scale"
],
"automl_enabled": false,
"default": {
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0.32,
1.0
],
"global_crops_size": 224,
"local_crops_scale": [
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0.32
],
"local_crops_size": 98,
"n_global_crops": 2,
"n_local_crops": 8
},
"description": "Configuration parameters for data transformation",
"popular": [
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"local_crops_size",
"n_local_crops",
"global_crops_size",
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],
"properties": {
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"default": [
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],
"description": "Scale range for global crops",
"popular": true,
"title": "Global Crops Scale",
"type": "list"
},
"global_crops_size": {
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"description": "Size of global crops",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "Global Crops Size",
"type": "int"
},
"local_crops_scale": {
"automl_enabled": false,
"default": [
0.05,
0.32
],
"description": "Scale range for local crops",
"popular": true,
"title": "Local Crops Scale",
"type": "list"
},
"local_crops_size": {
"default": 98,
"description": "Size of local crops",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "Local Crops Size",
"type": "int"
},
"n_global_crops": {
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"description": "Number of global crops to generate",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "Number of Global Crops",
"type": "int"
},
"n_local_crops": {
"default": 8,
"description": "Number of local crops to generate",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "Number of Local Crops",
"type": "int"
}
},
"title": "transform",
"type": "collection"
},
"workers": {
"automl_enabled": true,
"default": 8,
"description": "The number of parallel workers processing data",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "batch size",
"type": "int"
}
},
"type": "collection"
},
"encryption_key": {
"default": "",
"description": "Key for encrypting model checkpoints",
"title": "Encryption key",
"type": "string"
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},
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"model": "nvdinov2",
"network_arch": "nvdinov2",
"schema_action": "inference",
"schema_version": 1,
"source": "tao-core dataclass config"
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}
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 Python, Needs Node.js
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-nvdinov2 · 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-nvdinov2.zip