nvidia-tao/tao-skill-bank/tao-data-io

tao-data-io

Get data to and from the compute container. Decide the storage tier first — under strategy A (pre-positioned mount) no bytes move at all — and when a fetch is needed, move it host-side with aws/s5cmd/boto3/huggingface-cli/ngc directly — no nvidia-tao-sdk, no in-container runtime. Other platform skills call this skill to stage inputs before launch and sync outputs after. It never launches a container itself. The chosen tier is stamped into the job-record at submit.

When NOT to invoke this skill: if the inputs are already readable from the compute frame (a local path on the execution host, an existing Lustre/PVC/bind mount), that IS tier A — record it and skip this skill entirely; there is nothing to move. Air-gapped hosts: tier A is the only tier — never attempt an S3/HF/NGC fetch; anything missing (datasets, checkpoints, and the container images themselves) must be pre-positioned by the operator, and the preflight's readability check is the only data step that runs.

Credentials (env vars; values never written to disk by this skill)

S3 credentials use the officially documented AWS env vars, read from the session environment: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and (for S3-compatible stores) AWS_ENDPOINT_URL, AWS_DEFAULT_REGION. The aws CLI and boto3 pick the variables up natively — never run aws configure and never write ~/.aws/credentials:

set -a; source /path/to/.env; set +a   # omit if already exported
aws s3 ls "s3://$S3_BUCKET_NAME/..."   # reads AWS_* from the environment

If a session provides only the legacy TAO names (ACCESS_KEY, SECRET_KEY, S3_ENDPOINT_URL, CLOUD_REGION), map them once, scoped to the command: AWS_ACCESS_KEY_ID="$ACCESS_KEY" AWS_SECRET_ACCESS_KEY="$SECRET_KEY" aws s3 ...

HF_TOKEN / NGC_KEY pass through unchanged for PTM pulls. Never pass a credential as a CLI argument (-p, --token, -e KEY=value); use --password-stdin or -e VAR (no value).

Storage strategy (agent decides; the gate verifies)

Pick per backend from what the cluster/daemon actually offers:

• A — pre-positioned mount (PVC / NFS / Lustre / bind mount already holding the data): mount it, author the mount paths into the spec. No S3 fetch. Also the air-gap answer.
• B — volume populated from S3: an initContainer / stage step fills a durable volume; compute mounts it; a final step drains it back to S3 if needed.
• C — ephemeral + in-compute fetch: no persistent mount — fetch into the compute container and upload out at the end (today's K8s default).

Verify-before-launch gate (the one invariant)

The path the spec references is readable from the compute frame (not the

launcher's), and the output destination persists after the container exits.

Probe in the compute's frame of reference (in-container aws s3 cp/touch on the resolved results_dir, or a kubectl run/srun probe) — a green aws s3 ls on the launcher is not proof the pod can read the data (managed backends inject different creds into the compute container).

Input staging

• Bulk folder: s5cmd cp 's3://.../*' <stage> or aws s3 sync.
• Single file: aws s3 cp.
• Annotation-selective (download only files referenced by an annotation): use references/selective_download.py (below).
• Archive: tar -xzf X -C <dir> --strip-components=1 guarded by a .extracted marker (idempotent).
• PTM (`ngc://` / `hf://`): huggingface-cli download / ngc registry model download-version, then author the local path.

After staging, author the spec with local paths and run the verify gate.

Output routing (3-way) + upload

• TAO_RESULTS_ROOT set → write to that mount, no upload.
• else S3_BUCKET_NAME set → upload to s3://$S3_BUCKET_NAME/results/$TAO_JOB_ID/.
• else → loud ephemeral warning.

SLURM: never set S3_BUCKET_NAME (Lustre-only); run any upload on the login node, not inside the GPU allocation. Upload with excludes: aws s3 sync <local>/ s3://... --exclude '.tao/*' <upload_excludes...>.

Quick Start — annotation-selective staging

Download only the files an annotation references (e.g. the video column), preserving relative paths, into a local staging dir:

set -a; source /path/to/.env; set +a   # omit if already exported
python references/selective_download.py \
  --annotation /path/to/annotation.parquet \
  --key video \
  --bucket "$S3_BUCKET_NAME" --src-prefix datasets/clips \
  --dest /data/stage/clips

--key is repeatable or comma-separated; --format overrides extension inference (parquet/jsonl/json/csv).

Helpers

• references/selective_download.py — annotation-driven selective download (boto3 + pandas). Unit tests live in references/tests/; run with python -m pytest.