Tao Data Io
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Workflow
- Automation and integrations
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
tao-data-io is a support skill designed to handle data movement for TAO jobs by abstracting storage strategies and staging processes. It determines the optimal storage tier—ranging from pre-positioned mounts to ephemeral in-compute fetches—and manages the transfer of datasets, annotations, and pre-trained models. The skill ensures that compute containers have the necessary data access before execution and handles the routing of results back to storage, all while maintaining strict credential security and verifying data readability from the compute environment.
What it does
- Determines the storage strategy based on cluster capabilities (pre-positioned, volume-based, or ephemeral).
- Stages input data including bulk files, selective annotations, archives, and pre-trained models.
- Maps environment-based credentials for secure access to S3, Hugging Face, and NGC.
- Verifies that the compute environment can successfully read the staged data before job launch.
- Routes output results to designated storage locations or persistent mounts.
- Performs selective downloads based on annotation references to optimize data transfer.
When to use it
- When you need to stage datasets or pre-trained models for a TAO job.
- When you need to route job outputs to S3 or a persistent mount.
- When you need to verify data accessibility within a compute container before starting a job.
- When you want to perform selective data downloads based on specific annotation files.
Advantages
- Reduces data movement overhead by supporting pre-positioned storage strategies.
- Enhances security by managing credentials via environment variables without writing to disk.
- Provides a unified interface for various storage backends and data sources.
- Ensures job reliability through pre-launch readability verification.
- Optimizes bandwidth usage with annotation-selective download capabilities.
Usage examples
- “stage inputs for my TAO job”
- The agent evaluates the storage tier, fetches the required datasets or models from the configured source, and prepares the local paths for the compute container.
- “verify the container can read the data”
- The agent probes the compute environment to ensure the specified paths are accessible and that the container has the necessary permissions to read the input data.
- “upload TAO results”
- The agent identifies the output directory, applies necessary upload excludes, and synchronizes the results to the configured S3 bucket.
- “download only referenced files from the annotation”
- The agent executes the selective download script to fetch only the specific files listed in the provided annotation file, saving time and storage space.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-data-io/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-data-io.zip -d .agents/skills
Documentation by NVIDIA Corporation
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://$S3_BUCKET_NAME/..." # reads AWS_* from the environmentIf a session provides only the legacy TAO names (ACCESS_KEY, SECRET_KEY,
S3_ENDPOINT_URL, CLOUD_REGION), map them once, scoped to the command:
AWS_ACCESS_KEY_ID="$ACCESS_KEY" AWS_SECRET_ACCESS_KEY="$SECRET_KEY" aws s3 ...
HF_TOKEN / NGC_KEY pass through unchanged for PTM pulls. Never pass a
credential as a CLI argument (-p, --token, -e KEY=value); use
--password-stdin or -e VAR (no value).
Storage strategy (agent decides; the gate verifies)
Pick per backend from what the cluster/daemon actually offers:
A — pre-positioned mount (PVC / NFS / Lustre / bind mount already holding the data): mount it, author the mount paths into the spec. No S3 fetch. Also the air-gap answer.
B — volume populated from S3: an initContainer / stage step fills a durable volume; compute mounts it; a final step drains it back to S3 if needed.
C — ephemeral + in-compute fetch: no persistent mount — fetch into the compute container and upload out at the end (today's K8s default).
Verify-before-launch gate (the one invariant)
The path the spec references is readable from the compute frame (not the launcher's), and the output destination persists after the container exits.
Probe in the compute's frame of reference (in-container aws s3 cp/touch on
the resolved results_dir, or a kubectl run/srun probe) — a green aws s3 ls on the launcher is not proof the pod can read the data (managed
backends inject different creds into the compute container).
Input staging
Bulk folder:
s5cmd cp 's3://.../*' <stage>oraws s3 sync.Single file:
aws s3 cp.Annotation-selective (download only files referenced by an annotation): use
references/selective_download.py(below).Archive:
tar -xzf X -C <dir> --strip-components=1guarded by a.extractedmarker (idempotent).PTM (
ngc:///hf://):huggingface-cli download/ngc registry model download-version, then author the local path.
After staging, author the spec with local paths and run the verify gate.
Output routing (3-way) + upload
TAO_RESULTS_ROOTset → write to that mount, no upload.else
S3_BUCKET_NAMEset → upload tos3://$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 inreferences/tests/; run withpython -m pytest.
Files
- ATTRIBUTION.mdAdded by AutoGineer903 B
- BENCHMARK.md6.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.9 KiB
- config/skillspector-baseline.yaml1.6 KiB
- evals/evals.json826 B
- references/selective_download.pyScript7.5 KiB
- references/tests/test_selective_download.pyScript6.8 KiB
- skill-card.md4.1 KiB
- skill.oms.sig5.2 KiB
LICENSE
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
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 aws CLI or s5cmd on the staging host, plus Python 3.10+ with boto3 and pandas/pyarrow for annotation-selective download. No nvidia-tao-sdk, no fsspec/s3fs. Credentials are read from the process environment, whether exported in the user's shell or sourced from a user-approved env file.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- References to credential files
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
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-data-io · 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.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-data-io.zip