Generated banner for Tao Generate Image Embeddings
Generated logo for Tao Generate Image Embeddings

Tao Generate Image Embeddings

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Run TAO Data Services image embedding to turn a parquet of image filepaths into an embedding parquet using CLIP, SigLIP, or a TAO checkpoint. Use when a workflow needs embeddings before nearest-neighbor or unique-neighbor mining, or when the user asks to "embed images", "compute image embeddings", or "generate SigLIP embeddings".
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a standardized workflow for generating image embeddings using NVIDIA TAO Data Services. It processes a parquet file containing image paths and outputs a new parquet file enriched with embedding vectors. The skill supports multiple encoder models, including CLIP and SigLIP, and is designed to prepare data for downstream tasks such as nearest-neighbor or unique-neighbor mining. Maintaining consistency in the encoder model and configuration across all datasets is critical for ensuring the comparability of the resulting embeddings.

What it does

  • Converts image file paths from a parquet file into high-dimensional embedding vectors.
  • Supports multiple encoder architectures including CLIP, SigLIP, and custom TAO checkpoints.
  • Preserves existing metadata columns from the input parquet in the output file.
  • Provides a validation script to ensure configuration specs are correct before execution.
  • Allows for batch size adjustments to optimize GPU memory usage during processing.

When to use it

  • When you need to prepare image datasets for similarity search or neighbor mining.
  • When you need to generate consistent embeddings for multiple datasets to be compared later.
  • When you are working within a TAO-based pipeline and require image feature extraction.
  • When you need to switch between different pre-trained models like CLIP or SigLIP for image analysis.

Advantages

  • Ensures compatibility with downstream TAO mining tools.
  • Supports flexible model selection including standard HuggingFace models and custom TAO checkpoints.
  • Maintains data integrity by carrying through original metadata columns.
  • Includes validation tools to prevent configuration errors before starting resource-intensive GPU tasks.

Usage examples

“Generate embeddings for my images listed in images.parquet using the default SigLIP model.”
The agent will configure the embedding spec with SigLIP, validate it, and run the TAO Data Services container to produce an output parquet containing the image embeddings.
“I need to re-embed my dataset using CLIP instead of SigLIP for a new mining run.”
The agent will update the model and model_path fields in your experiment spec to the CLIP configuration, validate the changes, and execute the embedding process.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-generate-image-embeddings/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-generate-image-embeddings.zip -d .agents/skills

Documentation by NVIDIA Corporation

TAO Generate Image Embeddings

Use this skill to run TAO Data Services image embedding. The skill consumes a parquet of image filepaths and writes a parquet with an embedding column. Downstream mining skills (tao-mine-od-images, tao-mine-nearest-neighbors) consume its output.

The container entrypoint is:

embedding image_embeddings -e /absolute/path/to/image_embeddings.yaml

Inputs

The user can provide either an existing spec or the fields needed to generate one.

Required spec fields:

FieldMeaning
input_parquetAbsolute path to a parquet containing image filepaths.
output_parquetAbsolute path where the embedding parquet is written.
modelCLIP or SigLIP.
model_pathHuggingFace model id, local HF snapshot directory, or a TAO .pth/.ckpt checkpoint. Must match model. SigLIP: google/siglip-base-patch16-224 (768-dim, the template default). CLIP: openai/clip-vit-base-patch32 (512-dim). The validator rejects a recognizable model/path mismatch before launch.

Common optional fields:

FieldDefaultMeaning
model_config_path""TAO experiment spec path. Required only when model_path is a TAO checkpoint.
batch_size64Number of images processed in parallel. Lower it if the GPU runs out of memory.

The input parquet must contain a filepath column. Any additional columns are carried through to the output verbatim, so metadata such as label survives into the embedding parquet.

filepath values are transitive file inputs. When another workflow emits this spec, its platform action request must declare and mount every image root those paths reference; mounting only input_parquet is insufficient. Keep these mount-only folder inputs outside the native embedding YAML, because the Data Services config has no image_root field. If the producing workflow freezes absolute paths, each embedding container must remount the corresponding source at the same compute-visible root.

The default template is assets/default_image_embeddings.yaml.

Encoder Consistency

When embeddings feed a mining step, every parquet compared against another must be produced with the same model and model_path. Embedding dimensionality follows the encoder — 768 for the SigLIP default, 512 for CLIP ViT-B/32 — and nothing in the output parquet records which encoder wrote it. Embeddings from different encoders are not comparable, and mismatched encoders are the most common cause of mining output that looks unrelated to the targets. Reuse one spec across every parquet in a mining run and override only input_parquet / output_parquet.

Quick Start

Run from the tao-skill-bank repo root.

Write the spec beside the output parquet. The run does not retain it, so the embeddings otherwise carry no record of the encoder that produced them. That matters here more than elsewhere: every parquet compared against another in a mining step must come from the same model and model_path, and mismatched encoders are the usual cause of mining output that looks unrelated to its targets.

OUT_DIR=/absolute/path/for/this/run              # where output_parquet is written
SPEC="$OUT_DIR/image_embeddings.yaml"            # spec lives beside its output
RUN_ROOT=/absolute/path/that/contains/parquets/images/and/results
GPU_COUNT=1

python3 skills/data/tao-generate-image-embeddings/scripts/verify_image_embeddings_spec.py \
  --spec "$SPEC"

DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"

RUNTIME_HOME="$RUN_ROOT/.tao-runtime/home"
HF_CACHE="$HOME/.cache/huggingface"
mkdir -p "$RUNTIME_HOME/.cache" "$HF_CACHE"
IDENTITY_ARGS=(--user "$(id -u):$(id -g)"
  -e USER="$(id -un)" -e LOGNAME="$(id -un)"
  -e HOME="$RUNTIME_HOME" -e XDG_CACHE_HOME="$RUNTIME_HOME/.cache"
  -e HF_HOME="$HF_CACHE" -v "$HF_CACHE:$HF_CACHE")

docker run --rm --gpus "$GPU_COUNT" --shm-size=8g --network=host \
  "${IDENTITY_ARGS[@]}" \
  -v "$RUN_ROOT:$RUN_ROOT" \
  -w "$RUN_ROOT" \
  "$DS_IMAGE" \
  embedding image_embeddings -e "$SPEC"

Keep --user and the identity env block above. The image imports transformers at startup, which calls getpass.getuser(); without USER/LOGNAME it fails with KeyError: 'getpwuid(): uid not found: <uid>' because the UID has no /etc/passwd entry. Do not work around it by running the container as root; that leaves root-owned outputs. This follows the launch contract in tao-run-on-docker.

To embed several parquets with one encoder, reuse the same spec and override the two paths per run:

docker run --rm --gpus "$GPU_COUNT" --shm-size=8g --network=host "${IDENTITY_ARGS[@]}" \
  -v "$RUN_ROOT:$RUN_ROOT" -w "$RUN_ROOT" "$DS_IMAGE" \
  embedding image_embeddings -e "$SPEC" \
  input_parquet=/abs/path/other_input.parquet \
  output_parquet=/abs/path/other_output.parquet

Generate A Spec

If the user provides parquet paths and an encoder instead of a ready spec, copy the template and fill in the nulls. Every tuning value it already carries is the one this stage wants — change one only deliberately.

cp skills/data/tao-generate-image-embeddings/assets/default_image_embeddings.yaml "$SPEC"

Fill input_parquet, output_parquet and — if not using the default encoder — model and model_path, all as absolute paths, then validate:

python3 skills/data/tao-generate-image-embeddings/scripts/verify_image_embeddings_spec.py --spec "$SPEC"
input_parquet: /absolute/path/filepaths.parquet
output_parquet: /absolute/path/results/embeddings.parquet
model: SigLIP
model_path: google/siglip-base-patch16-224
model_config_path: ""          # required only when model_path is a TAO .pth/.ckpt
batch_size: 64

The template is the only place a default value lives, so nothing can disagree with it. verify reports the encoder, since embeddings are only comparable to others produced by the same model and model_path.

Keep the spec, input parquet, image files, and output directory under RUN_ROOT so the same paths resolve inside the container.

Preflight

  1. Verify Docker and GPU access:

docker info > /dev/null
nvidia-smi -L
  1. Resolve and pull the data-services image if needed:

DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"
  1. Validate the spec:

python3 skills/data/tao-generate-image-embeddings/scripts/verify_image_embeddings_spec.py \
  --spec "$SPEC"
  1. Confirm RUN_ROOT contains the spec, the input parquet, the image files its filepath column points at, and the output directory. Mount RUN_ROOT to the same absolute path inside Docker.

Outputs

ArtifactLocation
embedding parquetoutput_parquet

The output parquet contains filepath, an embedding column of list-like vectors, and every extra column carried through from the input. Print its row count and column list after the run so the caller can confirm the embedding column exists.

Troubleshooting

The subtask image_embeddings requires -e/--experiment_spec_file: rerun with embedding image_embeddings -e "$SPEC".

Input parquet or images not found inside Docker: the filepath values are read verbatim. Use a RUN_ROOT mount where host and container paths are identical, and confirm the images themselves are under that mount — not just the parquet.

Model loading error with a .pth / .ckpt model_path: TAO checkpoints need model_config_path set to the training spec so the architecture can be rebuilt. HuggingFace ids and snapshot directories do not.

CUDA out of memory: lower batch_size (try 32 or 16).

Mined results look unrelated downstream: the parquets compared during mining were embedded with different encoders. Re-embed them with one shared spec — see ## Encoder Consistency.

No GPU available: embedding requires at least one CUDA GPU. Check nvidia-smi -L, the Docker --gpus flag, and the NVIDIA container toolkit installation.

Files

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 a command-line tool, Needs Python

The author says: Requires docker, nvidia-container-toolkit, one or more CUDA GPUs, and the TAO data-services container pinned in versions.yaml.

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-generate-image-embeddings · 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-generate-image-embeddings.zip