Tao Train Oneformer
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The tao-train-oneformer skill provides a comprehensive interface for managing the lifecycle of OneFormer models within the NVIDIA TAO ecosystem. It supports universal image segmentation, enabling users to perform panoptic, instance, and semantic segmentation using a single task-conditioned architecture. The skill handles training, evaluation, model export, quantization, and inference, with built-in support for AutoML to optimize training parameters. It includes detailed guidance on dataset requirements, multi-GPU training configurations, and deployment workflows via TensorRT.
What it does
- Trains OneFormer models for universal image segmentation tasks.
- Supports AutoML for automated hyperparameter optimization during training.
- Evaluates model performance using metrics like mIoU.
- Exports trained models to ONNX format for deployment.
- Quantizes models to improve inference efficiency.
- Integrates with TAO Deploy for TensorRT engine generation and optimized inference.
When to use it
- When you need a unified model for panoptic, instance, and semantic segmentation.
- When training a segmentation model within the NVIDIA TAO framework.
- When you need to optimize model performance using AutoML.
- When preparing a model for deployment on NVIDIA hardware using TensorRT.
Advantages
- Unifies multiple segmentation tasks into a single architecture.
- Streamlined integration with TAO AutoML for efficient training.
- Provides clear paths for model export and TensorRT deployment.
- Includes robust error handling and configuration guidance for complex training setups.
Usage examples
- “Train a OneFormer model for universal segmentation.”
- The agent initiates the training process using the specified dataset and default AutoML policy, configuring the model for panoptic, instance, and semantic segmentation.
- “Export my trained OneFormer model to ONNX.”
- The agent generates an ONNX file from the specified checkpoint, ensuring the model is ready for subsequent TensorRT engine generation.
- “Run inference on my OneFormer model.”
- The agent executes the inference pipeline using the provided checkpoint and test dataset, outputting segmentation results in the configured mode.
- “Disable AutoML for my OneFormer training run.”
- The agent proceeds with a manual training configuration, bypassing the AutoML optimization process as requested.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-train-oneformer/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-train-oneformer.zip -d .agents/skills
Documentation by NVIDIA Corporation
OneFormer
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries.
Set train.pretrained_backbone and/or train.pretrained_model.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-oneformer.md first. Deploy spec templates live in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.
Dataclass Schemas
Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML for an action requires schemas/<action>.schema.json and references/spec_template_<action>.yaml to exist and parse. Use the packaged selected-action schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.
Train Action Policy
This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_info.yaml and resolve the run override from either an explicit automl_policy value or the user's workflow request. Use automl_policy: on by default and only expose on / off in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only. When automl_policy: on, automl_enabled: true, and both schemas/train.schema.json and references/spec_template_train.yaml are packaged, route the train action through tao-skill-bank:tao-run-automl by default with this model's skill_dir. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.
Non-train actions such as evaluate, inference, export, and deploy flows stay in this model skill. The per-run automl_policy override does not change model metadata.
Training Requirements
Dataset type: segmentation
Formats: coco_panoptic, coco
AutoML training metric:
mIoU, withdirection=maximizeStandalone evaluation metric:
test_mIoU, withdirection=maximize
Per-Action Dataset Requirements
| Action | Spec Key | Source | Files | List? |
|---|---|---|---|---|
| evaluate | dataset.train.images | train_datasets | images.tar.gz | No |
| evaluate | dataset.label_map | train_datasets | label_map.json | No |
| evaluate | dataset.train.annotations | train_datasets | annotations.json | No |
| evaluate | dataset.train.panoptic | train_datasets | images_panoptic.tar.gz | No |
| evaluate | dataset.val.images | eval_dataset | images.tar.gz | No |
| evaluate | dataset.val.annotations | eval_dataset | annotations.json | No |
| evaluate | dataset.val.panoptic | eval_dataset | images_panoptic.tar.gz | No |
| evaluate | dataset.test.images | eval_dataset | images.tar.gz | No |
| evaluate | dataset.test.annotations | eval_dataset | annotations.json | No |
| evaluate | dataset.test.panoptic | eval_dataset | images_panoptic.tar.gz | No |
| inference | dataset.train.images | train_datasets | images.tar.gz | No |
| inference | dataset.label_map | train_datasets | label_map.json | No |
| inference | dataset.train.annotations | train_datasets | annotations.json | No |
| inference | dataset.train.panoptic | train_datasets | images_panoptic.tar.gz | No |
| inference | dataset.val.images | eval_dataset | images.tar.gz | No |
| inference | dataset.val.annotations | eval_dataset | annotations.json | No |
| inference | dataset.val.panoptic | eval_dataset | images_panoptic.tar.gz | No |
| inference | dataset.test.images | inference_dataset | images.tar.gz | No |
| quantize | dataset.train.images | train_datasets | images.tar.gz | No |
| quantize | dataset.train.annotations | train_datasets | annotations.json | No |
| quantize | dataset.label_map | train_datasets | label_map.json | No |
| quantize | dataset.train.panoptic | train_datasets | images_panoptic.tar.gz | No |
| quantize | dataset.val.images | eval_dataset | images.tar.gz | No |
| quantize | dataset.val.annotations | eval_dataset | annotations.json | No |
| quantize | dataset.val.panoptic | eval_dataset | images_panoptic.tar.gz | No |
| quantize | dataset.test.images | eval_dataset | images.tar.gz | No |
| quantize | dataset.quant_calibration_dataset.images_dir | calibration_dataset | images.tar.gz | No |
| train | dataset.train.images | train_datasets | images.tar.gz | No |
| train | dataset.train.annotations | train_datasets | annotations.json | No |
| train | dataset.label_map | train_datasets | label_map.json | No |
| train | dataset.train.panoptic | train_datasets | images_panoptic.tar.gz | No |
| train | dataset.val.images | eval_dataset | images.tar.gz | No |
| train | dataset.val.annotations | eval_dataset | annotations.json | No |
| train | dataset.val.panoptic | eval_dataset | images_panoptic.tar.gz | No |
| train | dataset.test.images | eval_dataset | images.tar.gz | No |
Typical Spec Overrides
Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.
S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"
S3_INFERENCE = "s3://bucket/data/inference"
S3_CALIBRATION = "s3://bucket/data/calibration"train (mandatory data sources):
{
"train.num_gpus": 1,
"train.num_epochs": 10,
"train.checkpoint_interval": 10,
"train.validation_interval": 10,
"model.sem_seg_head.num_classes": 133,
"dataset.contiguous_id": True,
"train.precision": "32",
"dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
"dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
"dataset.label_map": f"{S3_TRAIN}/label_map.json",
"dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
"dataset.val.images": f"{S3_EVAL}/images.tar.gz",
"dataset.val.annotations": f"{S3_EVAL}/annotations.json",
"dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
"dataset.test.images": f"{S3_EVAL}/images.tar.gz",
}evaluate (mandatory data sources):
{
"evaluate.checkpoint": "<selected train/AutoML checkpoint>",
"model.sem_seg_head.num_classes": 133,
"dataset.contiguous_id": True,
"dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
"dataset.label_map": f"{S3_TRAIN}/label_map.json",
"dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
"dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
"dataset.val.images": f"{S3_EVAL}/images.tar.gz",
"dataset.val.annotations": f"{S3_EVAL}/annotations.json",
"dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
"dataset.test.images": f"{S3_EVAL}/images.tar.gz",
"dataset.test.annotations": f"{S3_EVAL}/annotations.json",
"dataset.test.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
}export:
{
"export.checkpoint": "<selected train/AutoML checkpoint>",
"model.sem_seg_head.num_classes": 133,
"model.export": True,
"export.onnx_file": "/results/oneformer_export_640.onnx",
}inference (mandatory data sources):
{
"inference.checkpoint": "<selected train/AutoML checkpoint>",
"dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
"dataset.label_map": f"{S3_TRAIN}/label_map.json",
"dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
"dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
"dataset.val.images": f"{S3_EVAL}/images.tar.gz",
"dataset.val.annotations": f"{S3_EVAL}/annotations.json",
"dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
"dataset.test.images": f"{S3_INFERENCE}/images.tar.gz",
"inference.images_dir": f"{S3_INFERENCE}/images.tar.gz",
}quantize (mandatory data sources):
{
"quantize.model_path": "<selected train/AutoML checkpoint>",
"dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
"dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
"dataset.label_map": f"{S3_TRAIN}/label_map.json",
"dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
"dataset.val.images": f"{S3_EVAL}/images.tar.gz",
"dataset.val.annotations": f"{S3_EVAL}/annotations.json",
"dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
"dataset.test.images": f"{S3_EVAL}/images.tar.gz",
"dataset.quant_calibration_dataset.images_dir": f"{S3_CALIBRATION}/images.tar.gz",
}Checkpoint Selection
OneFormer training writes epoch-step checkpoints such as
model_epoch_000_step_00017.pth and may also write a
oneformer_model_latest.pth symlink. For checkpoint-dependent actions, use the
model-skill or SDK parent-model resolver and pass the exact selected checkpoint
path into evaluate.checkpoint, inference.checkpoint, export.checkpoint,
quantize.model_path, or train.resume_training_checkpoint_path. Do not pick
the oneformer_model_latest.pth symlink by name unless the user explicitly asks
for latest checkpoint behavior. If the resolver reports a best checkpoint, use
that best checkpoint for evaluation/export/inference; if the user asks for a
specific epoch or step, use the matching epoch-step checkpoint.
Eval Dataset
Optional. Val data configured alongside train in the dataset config.
Important Parameters
model.sem_seg_head.num_classes: Number of segmentation class indices available to the head. Default 133 for COCO panoptic data when
dataset.contiguous_id: Trueremaps raw category ids through the label map. Do not shrink this to a global workflow class count unless the label map and annotations have actually been reduced to that class set.model.one_former.hidden_dim: Keep at 256 for local smoke runs unless the text encoder width is changed in lock-step. Reducing hidden_dim alone causes a text feature/context dimension mismatch during training.
model.backbone.name: Default D2SwinTransformer (Swin-based). embed_dim=192, depths=[2,2,18,2] by default.
train.num_epochs: Default 50 — significantly higher than most TAO models. OneFormer needs more epochs for convergence.
train.optim.lr: Learning rate. Default 1e-5. Lower than Mask2Former's 2e-4.
model.task_toggling: Enable/disable specific tasks: semantic_on, instance_on, panoptic_on.
export.task: Export task mode. Options: semantic, instance, panoptic. Default semantic. Export input defaults to 640x640.
inference.mode: Inference mode. Options: semantic, instance, panoptic. Default semantic. image_size defaults to [1024, 1024].
evaluate.iou_per_class: Report per-class IoU in evaluation. Default True.
Multi-GPU / Multi-Node
Launch method: Lightning-managed (single python process, Lightning spawns workers).
| Spec Key | Description | Default |
|---|---|---|
train.num_gpus | Number of GPUs | 1 |
train.gpu_ids | GPU device indices | [0] |
train.num_nodes | Number of nodes | 1 |
Uses explicit
DDPStrategywithfind_unused_parameters=True,gradient_as_bucket_view=True,process_group_backend="nccl"sync_batchnormis always enabledNo fsdp support — DDP only
Multi-node env vars (set by orchestrator): WORLD_SIZE, NODE_RANK, MASTER_ADDR, MASTER_PORT, NUM_GPU_PER_NODE.
Hardware
Minimum 2 GPU(s), recommended 4 GPU(s). 24GB+ (A100 recommended) VRAM per GPU. OneFormer is memory-intensive like Mask2Former. batch_size=1 is the default. Multi-GPU needed for reasonable training speed, especially with 50 epochs.
Error Patterns
CUDA out of memory: batch_size is already 1. Reduce image resolution or use a smaller Swin configuration.
Extracted S3 tarball points one level too high: For local Docker runs,
images.tar.gz and images_panoptic.tar.gz may extract wrapper directories
such as images/ and images_panoptic/. Set dataset.*.images,
dataset.*.panoptic, inference.images_dir, and quantization calibration
paths to the actual folder containing image or panoptic files, not the wrapper
directory. A one-level-too-high path fails with FileNotFoundError for the
first annotation image even though recursive file counts look correct.
default_specs missing results_dir: The CLI default_specs subtask ignores
-e experiment specs for results_dir; pass a Hydra-style override instead:
oneformer default_specs results_dir=/path/to/default_specs.
Invalid Lightning precision fp32: Use train.precision: "32" in
train/AutoML/evaluate/inference specs. The current Lightning stack rejects the
legacy fp32 string.
PyTorch 2.6 checkpoint load failure on downstream actions: Current
OneFormer checkpoints include OmegaConf objects. For checkpoints produced by
the same trusted TAO train/AutoML workflow, set
TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 in downstream evaluate, inference, export,
quantize, or resume job env vars so Lightning can load the full checkpoint.
Do not use this env var for untrusted checkpoints.
CUDA device-side assert in matcher/class cost: If training fails in
oneformer/utils/matcher.py while indexing out_prob[:, tgt_ids], compare
the effective target ids with model.sem_seg_head.num_classes. The packaged
COCO panoptic sample has 133 compact classes after dataset.contiguous_id: True remapping, so use model.sem_seg_head.num_classes: 133 even when a
broader validation workflow passes a smaller generic num_classes value.
Only use a smaller class count when the label map and annotations are reduced
to that exact contiguous class set.
Inference returns PASS with no predictions: OneFormer prediction reads
inference.images_dir, not dataset.test.images. Declare and populate
inference.images_dir with the image folder or tarball for every inference
run. dataset.test.images may still be useful for shared dataset context, but
it does not drive the PyTorch predict dataloader.
Export output path pre-created as a directory: Do not declare
export.onnx_file as a file output. The OneFormer exporter asserts that the
ONNX path does not already exist, while the local runner pre-creates declared
output paths. Set export.onnx_file explicitly in the spec to a non-existing
file path under the mounted results tree. Keep the default 640x640 export
shape for smoke validation; very small export shapes can trigger PyTorch ONNX
shape-inference failures.
Quantize cannot find the training label map from an AutoML checkpoint:
OneFormer Lightning checkpoints retain train-time absolute dataset paths in
their saved hparams. When running downstream actions from an AutoML child
checkpoint, keep the parent AutoML job directory accessible at its original
/results/<job_id> path inside the action container in addition to passing the
resolved checkpoint path. Otherwise quantize can fail while loading checkpoint
hparams even when the current spec includes a valid dataset.label_map.
Slow training: 50 default epochs with batch_size=1 is slow on single GPU. Use multi-GPU distributed training.
Spec Param / Parent Model Inference
Model-specific inference mappings belong in this MD file, not in config.json. Generated runners should read this section and apply the mappings with SDK helpers before create_job(). This mirrors the old microservices infer_params.py flow.
Inference mappings from TAO Core oneformer.config.json:
| Action | Spec Field | Inference Function | Meaning |
|---|---|---|---|
| evaluate | encryption_key | key | encryption key |
| evaluate | evaluate.checkpoint | parent_model | model file inferred from the parent job results folder |
| evaluate | evaluate.trt_engine | parent_model | model file inferred from the parent job results folder |
| evaluate | results_dir | output_dir | current job results directory |
| export | encryption_key | key | encryption key |
| export | export.checkpoint | parent_model | model file inferred from the parent job results folder |
| export | export.onnx_file | create_onnx_file | output ONNX path |
| export | results_dir | output_dir | current job results directory |
| gen_trt_engine | encryption_key | key | encryption key |
| gen_trt_engine | gen_trt_engine.onnx_file | parent_model | model file inferred from the parent job results folder |
| gen_trt_engine | gen_trt_engine.trt_engine | create_engine_file | output TensorRT engine path |
| gen_trt_engine | results_dir | output_dir | current job results directory |
| inference | encryption_key | key | encryption key |
| inference | inference.checkpoint | parent_model | model file inferred from the parent job results folder |
| inference | inference.trt_engine | parent_model | model file inferred from the parent job results folder |
| inference | results_dir | output_dir | current job results directory |
| quantize | encryption_key | key | encryption key |
| quantize | quantize.model_path | parent_model | model file inferred from the parent job results folder |
| quantize | results_dir | output_dir | current job results directory |
| train | encryption_key | key | encryption key |
| train | results_dir | output_dir | current job results directory |
| train | train.pretrained_backbone | {'link': 'https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth', 'destination_path': '/ptm/mask2former/swin_tiny_patch4_window7_224_22k/swin_tiny_patch4_window7_224_22k.pth'} | {'link': 'https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth', 'destination_path': '/ptm/mask2former/swin_tiny_patch4_window7_224_22k/swin_tiny_patch4_window7_224_22k.pth'} |
| train | train.pretrained_model | ptm_if_no_resume_model | PTM when no resume checkpoint exists |
| train | train.resume_training_checkpoint_path | resume_model | model file inferred from the current job results folder |
For parent_model or parent_model_folder, pass the upstream train/export/AutoML child job id as parent_job_id. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to config.json and do not patch generated runner scripts to guess checkpoint paths.
Deployment
Files
- ATTRIBUTION.mdAdded by AutoGineer927 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md19.5 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json783 B
- references/skill_info.yaml9.5 KiB
- references/spec_template_deploy_evaluate.yaml521 B
- references/spec_template_deploy_gen_trt_engine.yaml277 B
- references/spec_template_deploy_inference.yaml521 B
- references/spec_template_evaluate.yaml4.6 KiB
- references/spec_template_export.yaml4.7 KiB
- references/spec_template_gen_trt_engine.yaml4.8 KiB
- references/spec_template_inference.yaml4.7 KiB
- references/spec_template_quantize.yaml4.5 KiB
- references/spec_template_train.yaml4.5 KiB
- references/tao-deploy-oneformer.md6.1 KiB
- references/tao-deploy-oneformer.skill_info.yaml3.7 KiB
- schemas/evaluate.schema.json72.9 KiB
- schemas/export.schema.json73.5 KiB
- schemas/gen_trt_engine.schema.json75.4 KiB
- schemas/inference.schema.json73.5 KiB
- schemas/manifest.json18.2 KiB
- schemas/quantize.schema.json70.2 KiB
- schemas/train.schema.json70.1 KiB
- skill-card.md4.5 KiB
- skill.oms.sig9.4 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 docker + nvidia-container-toolkit.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
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-train-oneformer · 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-train-oneformer.zip