Tao Run Deft Cr Its Mining

Skill: TAO Run DEFT CR ITS Mining

Prerequisites

Before preflight, follow references/host-prerequisites.md; use its selected DEFT_PYTHON for every bundled helper and stop if the dependency probe fails.

Bundled Resources

Resolve DEFT_SKILL_ROOT to the absolute directory containing this installed SKILL.md. The agent or plugin runtime resolves this path; it is not a user input. Invoke bundled helpers with "$DEFT_PYTHON" "$DEFT_SKILL_ROOT/scripts/<name>.py" .... Never require a skill-bank checkout or change the user's working directory to a repository root.

This workflow invokes tao-finetune-cosmos-embed, tao-analyze-gaps-vlm-bcq, tao-mine-nearest-neighbors, and the selected platform skill by registered skill name. Those skills own their credentials, actions, and bundled assets. Cosmos Reason is workflow-owned: resolve images.tao_toolkit.deft_cosmos_reason, generate TOMLs with this workflow's helpers and configured base templates, and submit the exact train/evaluate commands in references/mining-loop.md through the selected platform. Do not invoke tao-finetune-cosmos-reason for planning, templates, action bundles, commands, or hook resolution.

User Inputs (DEFT Workspace and Workflow Configuration Yaml)

The user should provide a DEFT workspace absolute path that will be used for the entire run. This path should not be /workspace because that conflicts with the Cosmos Reason skills.

The layout of the DEFT workspace should be as follows:

<deft_workspace>/
├── data/
├── hf_cache/
├── model/
├── specs/
└── results/

Each workflow run writes under a run-specific directory inside <deft_workspace>/results/. If run.name is set in workflow.yaml, that exact value is used as the directory name; otherwise the workflow creates a timestamped directory. For example, run.name: deft_cr_its_mining_test writes baseline evaluation under <deft_workspace>/results/deft_cr_its_mining_test/baseline/evaluate.

The run directory layout is:

<deft_workspace>/results/<run_name_or_timestamp>/
├── workflow.yaml
├── deft_state.json
├── loop_log.jsonl
├── bcq_accuracy_report.md
├── bcq_accuracy_summary.json
├── baseline/
│   └── evaluate/
│       └── bcq_accuracy_metrics.json
├── cosmos_embed_output/
│   ├── kpi/
│   └── train/
├── embedding_parquets/
│   ├── kpi/
│   │   └── embeddings.parquet
│   └── train/
│       └── embeddings.parquet
├── iter_1/
│   └── evaluate/
│       └── bcq_accuracy_metrics.json
├── iter_2/
└── ...

workflow.yaml is a copy of the high-level config that produced the run. loop_log.jsonl is the append-only event source for resume, and every stage-log append atomically refreshes deft_state.json as its current snapshot. baseline/evaluate/ stores the initial evaluation of the baseline model. Each completed evaluation also stores bcq_accuracy_metrics.json. The run-level bcq_accuracy_report.md and bcq_accuracy_summary.json compare the baseline with every completed iteration. cosmos_embed_output/kpi/ and cosmos_embed_output/train/ store raw Cosmos Embed preparation and inference outputs. embedding_parquets/kpi/ and embedding_parquets/train/ store the fixed mining-ready parquet artifacts computed once from the KPI and train datasets. Each iter_<N>/ directory stores the artifacts for one DEFT improvement iteration.

Workflow Configuration

There should be a file called <deft_workspace>/specs/workflow.yaml, which will be the high-level workflow config for the full DEFT run:

run:
  name: null
  max_iterations: 1

kpi_dataset:
  annotations_path: /abs/path/to/deft_workspace/data/kpi/annotations.json
  media_dir: /abs/path/to/deft_workspace/data/kpi/media

train_dataset:
  annotations_path: /abs/path/to/deft_workspace/data/train/annotations.json
  media_dir: /abs/path/to/deft_workspace/data/train/media

cosmos_reason:
  baseline_model_path: /abs/path/to/deft_workspace/model/reasoner_checkpoint
  base_evaluate_toml: /abs/path/to/deft_workspace/specs/cr_base_evaluate.toml
  base_train_toml: /abs/path/to/deft_workspace/specs/cr_base_train.toml
  continual_model: false

mining:
  embeddings_spec_template: /abs/path/to/deft_workspace/specs/cosmos_embed_inference_template.yaml
  embeddings_modality: both
  cosmos_embed_checkpoint_path: null
  embedding_parquets:
    kpi: null
    train: null
  mining_spec_template: /abs/path/to/deft_workspace/specs/mining_spec_template.yaml
  mine_unique_only: true

run, kpi_dataset, train_dataset, cosmos_reason, and mining are required; configured paths must be absolute inside <deft_workspace>. cosmos_reason.baseline_model_path must be a Cosmos Reasoner checkpoint. Convert a native Omni checkpoint first with tao-finetune-cosmos-reason. The optional continual_model defaults to false, which starts every iteration from the baseline. embeddings_modality selects KPI targets; train/source embeddings always include text and video. The optional mine_unique_only defaults to true and filters previously mined train/source paths from later iterations.

Write all workflow outputs under <deft_workspace>/results; do not add another output root. When run.name is set, use <deft_workspace>/results/<run.name> and explain that baseline/, cosmos_embed_output/, embedding_parquets/, and iter_<N>/ are nested there. When it is null, create <deft_workspace>/results/run_<YYYYMMDD_HHMMSS> and record that path in deft_state.json so resume never recomputes it.

Before running any workflow stage, validate the config:

"$DEFT_PYTHON" "$DEFT_SKILL_ROOT/scripts/verify_workflow_yaml.py" \
  --workspace "$WORKSPACE" \
  --workflow-yaml "$WORKSPACE/specs/workflow.yaml"

The validator checks required fields, workspace-contained absolute paths, embeddings_modality, path existence, Omni baselines, and compatibility between KPI/train LLaVA annotations and their media_dir. Every annotation needs a unique non-empty id; its video must resolve against that dataset's media directory. The Cosmos Embed template must declare a positive inference.num_gpus, which preflight prints for hardware verification. mining.cosmos_embed_checkpoint_path accepts null, an absolute workspace-local path, or a Hugging Face model id with an optional hf_model:// prefix. Optional mining.embedding_parquets.kpi and .train values must be absolute existing mining-ready Parquet files inside the workspace with filepath, embedding, and modality columns. KPI must contain exactly the selected modalities; train must contain both text and video. The removed text_embeddings and video_embeddings fields are invalid. Each stage still follows its underlying skill, container, or platform runner's mount behavior.

If preflight rejects a Cosmos3 Omni baseline, tell the user that this workflow requires a Reasoner checkpoint, ask them to convert it first with tao-finetune-cosmos-reason, and exit without initializing the workflow.

If the user does not provide custom Cosmos Reason or Cosmos Embed templates, copy $DEFT_SKILL_ROOT/assets/cr_base_evaluate.toml, $DEFT_SKILL_ROOT/assets/cr_base_train.toml, and $DEFT_SKILL_ROOT/assets/default_cosmos_embed_inference.yaml into <deft_workspace>/specs/. Use tao-mine-nearest-neighbors to copy its bundled assets/default_nearest_neighbors.yaml when the user does not provide a mining template. Point workflow.yaml at the workspace copies. The bundled Cosmos Embed template requests 8 GPUs; do not launch until preflight confirms the selected platform can satisfy that request, or the user approves a modified template.

Initialize a run once after validation:

"$DEFT_PYTHON" "$DEFT_SKILL_ROOT/scripts/initialize_workflow.py" \
  --workspace "$WORKSPACE" \
  --workflow-yaml "$WORKSPACE/specs/workflow.yaml"

Use the printed run_dir as RUN_DIR for every later command. If run.name is null, never ask another preparation script to derive the run directory again; pass --run-dir "$RUN_DIR" so all stages write into the initialized run.

Do not use --force for an ordinary resume. It rewrites the state/config snapshot but intentionally leaves loop_log.jsonl and all stage artifacts in place. Use it only to repair the snapshot for the same run; choose a new run.name for a clean restart. Before resuming, run "$DEFT_PYTHON" "$DEFT_SKILL_ROOT/scripts/resume_position.py" --run-dir "$RUN_DIR" and continue from the reported stage.

Baseline Evaluation

Run baseline evaluation once before the DEFT iteration loop. This evaluates cosmos_reason.baseline_model_path on the KPI dataset and produces the first results.json used by gap analysis. Immediately run scripts/compute_bcq_accuracy_metrics.py on that file and store baseline/evaluate/bcq_accuracy_metrics.json. Its parser recognizes yes and no in short or free-form responses, including capitalization and punctuation. It reports false positives, false negatives, accuracy, balanced accuracy, and unparseable predictions. Use references/mining-loop.md for the exact commands, completion check, and logging rule.

Initialize Mining Embeddings

Initialize mining embeddings once before the DEFT iteration loop. The KPI and train datasets are fixed for the run, so their Cosmos Embed outputs are reusable across all iterations.

Use scripts/prepare_cosmos_embed_inference.py to prepare the Cosmos Embed inference specs from workflow.yaml. The KPI dataset requires the modality or modalities selected by mining.embeddings_modality; the train dataset always requires text and video. If the user provides a complete dataset Parquet under mining.embedding_parquets.kpi or .train, the preparation script stages it at $RUN_DIR/embedding_parquets/<dataset>/embeddings.parquet and generates no inference specs for that dataset. While staging, it remaps text embedding identifiers from the source run's question files to the current run's lookup by question content; embeddings and video identifiers are preserved. If the dataset value is null or omitted, the script generates every required modality spec for that dataset. Partial per-modality reuse is not supported. If mining.cosmos_embed_checkpoint_path is an absolute local path, the script uses it for generated specs. If it is a remote Hugging Face model id, with or without the hf_model:// prefix, the script downloads or reuses it under <deft_workspace>/hf_cache and writes the local downloaded checkpoint path into the generated inference specs. If the field is null, the script uses nvidia/Cosmos-Embed1-224p.

The script downloads remote HF checkpoints before Cosmos Embed runs because Cosmos Embed startup can race when inference.num_gpus > 1 and multiple workers try to download the same model at once. If both combined dataset Parquets are provided, the preparation script does not need the checkpoint and does not download it.

Use scripts/prepare_cosmos_embed_inference.py once. It prepares both the KPI dataset and the train dataset:

"$DEFT_PYTHON" "$DEFT_SKILL_ROOT/scripts/prepare_cosmos_embed_inference.py" \
  --workspace "$WORKSPACE" \
  --workflow-yaml "$WORKSPACE/specs/workflow.yaml" \
  --run-dir "$RUN_DIR"

The preparation script writes only the missing embedding specs under the initialized RUN_DIR. Initialization owns the $RUN_DIR/workflow.yaml snapshot; this stage does not derive a new run or overwrite that snapshot.

For each dataset without a supplied combined Parquet, the preparation script writes all required modality-specific inference specs under cosmos_embed_output/<dataset>/specs/, creates raw Cosmos Embed result directories, stages text questions as files for stable joins, and writes lookup.parquet. After inference, the conversion script gathers those separate modality outputs into one mining-facing file per dataset. Each row contains filepath, embedding, and modality:

$RUN_DIR/embedding_parquets/kpi/embeddings.parquet
$RUN_DIR/embedding_parquets/train/embeddings.parquet

The KPI parquet contains only the selected modality or modalities. The train parquet always contains both text and video rows.