Tao Run Dinov3 Ssl Deft
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a command-line interface for managing iterative DINOv3 SSL DEFT (Data-Efficient Fine-Tuning) workflows. It enables active-learning-style data mining from an unlabeled pool using GRIT or multi-task weakness scoring. The workflow relies on fixed-encoder retrieval and requires a specific Data Services container environment to handle data selection, retrieval, and retraining loops.
What it does
- Initializes and validates DINOv3 mining recipes.
- Manages iterative data selection and retraining rounds.
- Supports GRIT scoring for target ranking.
- Integrates with fixed-encoder embedding preparation.
- Provides lifecycle commands for planning, running, and monitoring jobs.
- Generates reports and tracks state across training rounds.
When to use it
- When performing active-learning-style data mining for DINOv3 models.
- When you need to select and retrain on data from an unlabeled pool.
- When utilizing GRIT or multi-task weakness scoring strategies.
- When working within an environment that supports the required Data Services container.
Advantages
- Automates the iterative loop for data selection and model retraining.
- Ensures consistency through immutable configuration and data contracts.
- Provides structured monitoring and reporting for mining stages.
- Decouples training logic from the orchestration controller.
Usage examples
- “Initialize a new DINOv3 mining recipe using the grit-score strategy.”
- Generates a run.yaml configuration file ready for path and resource customization.
- “Validate the configuration for a DINOv3 DEFT workflow.”
- Checks the configuration against the workflow schema and verifies container readiness.
- “Start the mining workflow using the configured run directory.”
- Executes the iterative selection and training rounds as defined in the plan.
- “Check the status of an ongoing mining run.”
- Returns the current progress, stage, and round information for the specified run directory.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-run-dinov3-ssl-deft/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-run-dinov3-ssl-deft.zip -d .agents/skills
Documentation by NVIDIA Corporation
DINOv3 SSL DEFT
The DS-packaged Python controller owns the loop and durable state. This skill prepares and approves its inputs; it does not implement orchestration.
Instructions
Resolve the DINOv3 model using the model resolver. Choose
grit-scorefor unlabeled target ranking; its scorer is built in. Offermulti-task-round-robinonly when the user already has a score adapter that meets the task scoring contract: DS ships no multi-task scorer, andvalidaterejects the recipe's placeholder. Read architecture.md for the scientific meaning, task weighting, and stopping policy.Read tao-container-integration.md. Check release readiness before offering a launch: the resolved
images.tao_toolkit.data_servicesimage must be a release build whose installed packages contain the reviewed DEFT modules and pass packaged validation. An older DS build is not sufficient. The placeholder rejection (step 1), the cohort checks and the canceled-run exit code 143 need a DS build that contains tao-data-services#59: the first DS 7.3.0 RC after rc-90. DS rc-90 and older accept the placeholder atvalidate, fail in round 1 withMissing configured inputs: [... 'score_implementation_0'], and exit 0 after a cancel. Therelease/7.3.0cherry-pick of this skill must bumpimages.tao_toolkit.data_servicesinversions.yamlto that RC. The executable profile is one allocated DS container; no external runner ships with this application. For release QA, the repository scriptscripts/deft_container_handoff.pyruns each repository's regression tests (--suite source) and the shipped package checks (--suite packaged) inside the approved images; the packaged suite also rejects a DS image that predates #59. Run it from clean checkouts of the release SHAs; the default--base-ref origin/maindiffs each release from its merge base. It is a QA tool, not a launcher.Follow the selected platform's launch gate. For Docker, use the concrete allocation, preflight, and
docker execlifecycle in the integration reference. Inside that runtime, follow the preparation sequence in adapter-contracts.md: produce source/target embeddings with the same fixed encoder, register the source store with its payload contract, and write the target contract. No additional host/model package installation is needed. Generate a packaged recipe withinit, fill paths/resources, then runvalidate <config>,preflight <config> --gpuin the launch runtime, andplan <config>. Read adapter-contracts.md when declaring a held-out benchmark (isolation), or configuring custom scoring/evaluation, indexed retrieval, or resource policies.Inspect the plan and present the approval contract below. Read
references/skill_info.yaml:path_contractas the input-mount checklist: every applicable path must be visible in the allocated container, andoutput.run_dirmust match the platform's durable results directory. Skill Bank resolves image keys on the host; DS accepts explicit image references only and never reads this repository at runtime.After approval, execute
run <config>inside the allocated container. Monitor to a terminal result using the commands below. Do not modify controller state or locks manually.
Approval contract
Populate these six items from preflight and the plan, not from assumptions:
Loop: chosen strategy, maximum rounds, stops, evaluation scope and patience. For
multi-task-round-robin, the user's score adapter command and itsimplementation_files.Data: immutable target/source versions, verified row/shard counts, parent history, storage capacity and mount locations. For any held-out benchmark, the identity sidecar and its declared columns, even with evaluation disabled, and preflight's
contracts.benchmark_isolation(at least 1; otherwise stop) (held-out benchmark isolation).Sampling: fixed target budget, resolved integer task quotas, neighbors, radius/floor and duplicate thresholds; index, audit, probes/depth for ANN.
Training: passes, resolution, resources/update floor and immutable original checkpoint. Every candidate starts from that checkpoint, never its predecessor. Check the per-round update budget against LR warm-up and last-layer freeze; follow short-round schedule sizing.
Cache/output: read-only images/embeddings, any platform cache, node-local
TAO_LOCAL_SCRATCH, cumulative locator manifests, checkpoint retention, and durable results directory.Monitoring: cadence, stage/round updates, retry/failure handling and final reason.
Do not request launch approval while any of these items is unknown.
Lifecycle and monitoring
All commands below follow python -m nvidia_tao_ds.mining.dinov3.workflow
inside the same allocated DS runtime:
preflight [<config>] [--gpu](with the config, also checks the allocation)init --recipe grit-score --output run.yaml(ormulti-task-round-robin, which needs the user's score adapter; see step 1)validate <config>,plan <config>,run <config>,resume <config>status <run_dir> [--config <config>]logs <run_dir> <client_job_id> [--cursor <cursor>] [--config <config>]cancel <run_dir> [--config <config>]report <run_dir> [--config <config>]adopt-training <continuation_config>only for an explicitly approved continuation into a new run directory.
Read the job ID from status before requesting logs. Resume takes the config
path, not the run directory. Cancellation is terminal for that run: run and
resume then exit with code 143, not 0. Use an explicit new-run continuation
when appropriate.
Keep monitoring attached until the workflow is terminal. Use the user's
cadence, otherwise about five minutes for short stages and 20–30 minutes for
long search/training stages. Report stage/round transitions, mining yield,
metrics, retries, failures, and the final stop reason. Submission is not success.
If the container has exited, use the integration reference's read-only
inspection recipe rather than attempting docker exec against it.
Related Skills
tao-train-dinov3: ordinary DINOv3 training without a mining loop.tao-launch-workflow,tao-run-on-docker,tao-data-io: launch and storage.tao-run-deft-aoi,tao-run-deft-pas: different supervised DEFT workflows.
Files
- ATTRIBUTION.mdAdded by AutoGineer939 B
- BENCHMARK.md8.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md7.3 KiB
- config/skillspector-baseline.yaml1.4 KiB
- evals/evals.json4.6 KiB
- references/adapter-contracts.md26.9 KiB
- references/architecture.md6.8 KiB
- references/metrics.schema.json1.0 KiB
- references/skill_info.yaml2.0 KiB
- references/stage-request.schema.json3.2 KiB
- references/tao-container-integration.md8.1 KiB
- references/workflow.schema.json9.7 KiB
- skill-card.md5.2 KiB
- skill.oms.sig6.4 KiB
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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 the selected platform's native launcher and a TAO Data Services image containing the DINOv3 SSL DEFT modules; no host TAO or Python data-stack installation.
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-run-dinov3-ssl-deft · 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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