Tao Run Dinov3 Ssl Deft

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

1. Resolve the DINOv3 model using the model resolver. Choose grit-score for unlabeled target ranking; its scorer is built in. Offer multi-task-round-robin only when the user already has a score adapter that meets the task scoring contract: DS ships no multi-task scorer, and validate rejects the recipe's placeholder. Read architecture.md for the scientific meaning, task weighting, and stopping policy.

2. Read tao-container-integration.md. Check release readiness before offering a launch: the resolved images.tao_toolkit.data_services image 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 at validate, fail in round 1 with Missing configured inputs: [... 'score_implementation_0'], and exit 0 after a cancel. The release/7.3.0 cherry-pick of this skill must bump images.tao_toolkit.data_services in versions.yaml to that RC. The executable profile is one allocated DS container; no external runner ships with this application. For release QA, the repository script scripts/deft_container_handoff.py runs 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/main diffs each release from its merge base. It is a QA tool, not a launcher.

3. Follow the selected platform's launch gate. For Docker, use the concrete allocation, preflight, and docker exec lifecycle 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 with init, fill paths/resources, then run validate <config>, preflight <config> --gpu in the launch runtime, and plan <config>. Read adapter-contracts.md when declaring a held-out benchmark (isolation), or configuring custom scoring/evaluation, indexed retrieval, or resource policies.

4. Inspect the plan and present the approval contract below. Read references/skill_info.yaml:path_contract as the input-mount checklist: every applicable path must be visible in the allocated container, and output.run_dir must 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.

5. 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 its implementation_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 (or multi-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.