メニュー
Use when working in examples/embedder_fiftyone, or running / debugging the embedder→FiftyOne datapipe example (DINOv2/DINOv3 image embeddings, UMAP, similarity search).
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when asked to create, scaffold, or build a NEW datapipe pipeline or project (any size — plain ETL/ingest, ML training with metrics, LLM-processing steps, annotation loops, viewers/UI), usually in an empty directory outside the datapipe repo. Also when a user describes a recurring data-processing task ("files keep arriving and I need to...") and no pipeline exists yet.
Use when working in examples/ocr, or running / debugging the OCR→FiftyOne datapipe example (multi-LLM structured OCR on passport/id-doc images).
Use when working in the epoch8 datapipe repo with the examples/* folders, when asked to run or debug any datapipe example pipeline on your own data, or when unsure which example or setup skill applies.
Use when setting up or running examples/detection_tags — a self-contained datapipe detection demo built around tags (per-scenario metrics), split into two parts around a checkpoint (train a baseline, then add a tagged TRAIN batch and retrain, watching the tag metric rise), with a FiftyOne view and injected ground truth (no Label Studio). Also for "add a tagged batch, retrain, watch the tag metric rise" and for rehearsing that retraining demo from a saved checkpoint.
Use when setting up or running examples/e2e_template (image_detection or image_keypoints) — datapipe YOLO/Label Studio pipelines — or debugging their install, stages, training, or FiftyOne output.
Use when working in examples/sam_cvat, or when setting up / running / debugging the datapipe SAM3→CVAT pre-annotation example, on the cats-n-dogs demo data or on your own images.
| name | setup-embedder-fiftyone |
| description | Use when working in examples/embedder_fiftyone, or running / debugging the embedder→FiftyOne datapipe example (DINOv2/DINOv3 image embeddings, UMAP, similarity search). |
This skill = run the DINO→FiftyOne embedder on YOUR images. The FiftyOne-zoo dataset (caltech101) is just a smoke-test; the real goal is your own images — set the knobs below first.
Ask first — don't assume (only the unresolved): demo (zoo) or your own data? which Postgres + which database for DB_URL — never point it at an existing DB or drop in a localhost default without confirming; reuse an existing venv / uv env or create a fresh one? GPU? surface stage logs or run quiet?
How to work: read the setup, then propose a short plan and get a go-ahead before touching anything. Prepare .env and pause for the user to verify it before running. Run each stage with its logs shown and, after each, say what you did and what changed — don't run the pipeline silently. If a stage fails and the cause isn't clear from the normal logs, re-run it with datapipe --debug … run (or --debug-sql for SQL errors); debug is very verbose, so send it to a file and grep it (e.g. datapipe --debug run > /tmp/dp_debug.log 2>&1; grep -nEi "error|traceback" /tmp/dp_debug.log) rather than dumping it inline.
LABELS_JSON keys to the file stem (optional) — a mismatch silently yields an unlabeled
sample (None) / 0 useful results.LOCAL_IMAGES_DIR=/path (.jpg/.jpeg/.png; file stem → image_name).config.py → EMBEDDERS (default dinov2_base + dinov2_large).Stages (labels match app.py): source (local folder or zoo fallback) → embedder → fiftyone
(publish samples) → embeddings (per-image .npy) → fiftyone-brain (UMAP + cosine sim per embedder).
DB_URL (SQLAlchemy URL). Not started by the example; an empty DB is fine —
datapipe db create-all auto-creates tables. .env.example default ...postgres@localhost:5432/postgres.no_matching_samples.uv + Python 3.10–3.12 → uv sync
(pins cu124 torch==2.6.0, transformers, fiftyone).dinov3_vitl16 entry is
commented out — to use it, uncomment, accept the facebook/dinov3-* license, set HF_TOKEN with
gated-repo read access (else the whole embeddings batch fails).Skip if you have data. Leave LOCAL_IMAGES_DIR unset → zoo fallback (ZOO_DATASET=caltech101,
ZOO_NUM_CLASSES=10, keeps the last N classes); run §Run as-is to confirm install/DB/GPU. First run
downloads it, then auto-deletes the source zoo dataset; image_name rewritten /→__ (steps.py:83).
cp .env.example .env # DB_URL, LOCAL_IMAGES_DIR or ZOO_*, FIFTYONE_DATASET_NAME (+ FIFTYONE_DATABASE_DIR, see traps)
uv sync && source .venv/bin/activate # else prefix each command with `uv run`
datapipe db create-all && datapipe run
# one stage: datapipe step --labels=stage=source run (source|embedder|fiftyone|embeddings|fiftyone-brain)
Embeddings cache as .npy in EMBEDDINGS_DIR/{embedder_id}/ (default ./data/embeddings); delete to recompute.
fiftyone app launch --remote --address 0.0.0.0 --port 5151 --wait -1 # venv active; SSH-forward 5151
Open FIFTYONE_DATASET_NAME. Embeddings panel → key <embedder_id>_umap (single _), color by
ground_truth. Similarity → <embedder_id>__sim (double _) — don't transpose.
brain_status.status = 'ok' per embedder — trust the table, not the exit code: datapipe swallows
step errors and still exits 0. Plus: Embeddings panel shows clusters, similarity returns neighbors.
brain_status empty / 0 images though run exited 0 → FiftyOne mongod failed to start (old
~/.fiftyone datadir: FCV mismatch → Wrong mongod version / failed to bind to port). Use a fresh
dir: FIFTYONE_DATABASE_DIR=/tmp/fo_db.no_matching_samples → no sample image_name matched an embedding key: check the fiftyone
stage published samples, pipeline + FiftyOne on the same machine, and (zoo) the __-rewrite.LocalEntryNotFoundError → needs a license-accepted gated HF_TOKEN (see Prerequisites).