Tao Train Mask Auto Label

NVIDIAOfficialCreated by NVIDIA Corporation

Skill
Tool integration
Data and AI
Apache-2.0
Medium risk

From the author

MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations (point or box annotations) using a ViT-MAE backbone. Use when training, evaluating, or running inference for a TAO MAL model. Trigger phrases include "train MAL", "Mask Auto-Label", "weakly-supervised segmentation", "box-prompted segmentation", "minimal-annotation mask prediction".

— the description by NVIDIA Corporation

AutoGineer summary

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

The tao-train-mask-auto-label skill provides an interface for Mask Auto-Label (MAL) workflows, enabling the generation of segmentation masks from minimal annotations such as points or boxes. It utilizes a ViT-MAE backbone and supports training, evaluation, and inference tasks within the NVIDIA TAO Toolkit ecosystem. The skill is designed to handle weakly-supervised segmentation tasks and includes support for AutoML-based hyperparameter optimization.

What it does

Trains segmentation models using minimal point or box annotations.
Performs model evaluation and inference using specified checkpoints.
Supports AutoML for automated hyperparameter tuning during training.
Configures training parameters including learning rate, weight decay, and crop size.
Integrates with Docker-based environments for consistent execution.
Maps data sources and model checkpoints for automated job orchestration.

When to use it

When you need to generate segmentation masks from sparse annotations like bounding boxes.
When performing weakly-supervised segmentation tasks using a ViT-MAE architecture.
When you want to automate hyperparameter optimization for your segmentation model.
When you need to evaluate or run inference on a previously trained MAL model.

Advantages

Reduces manual annotation effort by using weakly-supervised learning.
Provides structured support for AutoML to optimize model performance.
Standardized configuration via YAML spec files for reproducibility.
Consistent containerized execution environment for training and inference.

Usage examples

“Train a MAL model with AutoML enabled.”: The agent initiates a training job using the MAL skill, applying the default AutoML policy to optimize hyperparameters like learning rate and weight decay.
“Run inference on my MAL model using the latest checkpoint.”: The agent executes the inference action, mapping the specified checkpoint and dataset paths to generate segmentation masks.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-train-mask-auto-label/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip tao-train-mask-auto-label.zip -d .agents/skills

Documentation by NVIDIA Corporation

MAL

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations (e.g., point or box annotations). Uses ViT-MAE backbone.

Set train.pretrained_model_path for ViT-MAE pretrained weights.

Quick Start (docker run)

Docker-native launch — no TAO SDK and no Python on the host. Use the local Docker/platform skill instead when it gives a stricter environment-specific command (non-root UID mapping, cache redirects, remote daemons).

TAO_PYT_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt  # versions-key: images.tao_toolkit.pyt
TAO_PYT_IMAGE="${TAO_PYT_IMAGE:-$TAO_PYT_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
DOCKER_COMMON=(
  --rm --gpus all --shm-size=8g
  --shm-size=8g
  --ulimit memlock=-1
  --ulimit stack=67108864
  -v "$RUN_ROOT/data:/data:ro"
  -v "$RUN_ROOT/specs:/specs:ro"
  -v "$RUN_ROOT/results:/results"
)

Train:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  mal train -e /specs/train.yaml

Evaluate:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  mal evaluate -e /specs/evaluate.yaml

Inference:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  mal inference -e /specs/inference.yaml

Every action takes its spec with -e; results_dir is set in the spec or overridden on the command line. Mount any pretrained-weights directory the spec references, and keep every in-container path consistent across actions.

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: default
Monitoring metric: mIoU
AutoML metric contract: Use mIoU emitted by the evaluate action and maximize it. Compare the recorded AutoML objective with the evaluator's emitted mIoU; do not substitute training loss.

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	dataset.val_img_dir	eval_dataset	images.tar.gz	No
evaluate	dataset.val_ann_path	eval_dataset	annotations.json	No
inference	inference.img_dir	inference_dataset	images.tar.gz	No
inference	inference.ann_path	inference_dataset	annotations.json	No
train	dataset.train_img_dir	train_datasets	images.tar.gz	No
train	dataset.train_ann_path	train_datasets	annotations.json	No
train	dataset.val_img_dir	eval_dataset	images.tar.gz	No
train	dataset.val_ann_path	eval_dataset	annotations.json	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. MAL expects COCO-style annotation JSON plus image paths that match the JSON file_name entries after the data source is prepared. Archive-only CSV/image datasets are not compatible unless they are converted to this format first.

S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

{
    "train.num_gpus": 1,
    "train.gpu_ids": [
        0
    ],
    "train.num_epochs": 5,
    "train.checkpoint_interval": 5,
    "train.validation_interval": 5,
    "dataset.train_img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.train_ann_path": f"{S3_TRAIN}/annotations.json",
    "dataset.val_img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val_ann_path": f"{S3_EVAL}/annotations.json",
}

evaluate (mandatory data sources):

{
    "evaluate.checkpoint": "<selected train/AutoML checkpoint>",
    "dataset.val_img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val_ann_path": f"{S3_EVAL}/annotations.json",
}

inference (mandatory data sources):

{
    "inference.checkpoint": "<selected train/AutoML checkpoint>",
    "inference.img_dir": f"{S3_EVAL}/images.tar.gz",
    "inference.ann_path": f"{S3_EVAL}/annotations.json",
}

For checkpoint-dependent actions, use the model resolver declared in references/skill_info.yaml. Select the exact epoch/step checkpoint requested by the user or the best checkpoint when a best-checkpoint action is requested. The mal_model_latest.pth symlink is only appropriate when the user explicitly asks for the latest checkpoint.

Eval Dataset

Optional. Val images and annotations configured alongside train paths.

Important Parameters

model.arch: ViT-MAE backbone variant. Default vit-mae-base/16. Avoid vit-deit-tiny/16; the current runtime rejects tiny ViT variants.
train.lr: Learning rate. Default 1e-6 (very low — fine-tuning ViT).
dataset.crop_size: Training crop size. Default 512. Use this key, not model.crop_size.
train.warmup_epochs: Warmup epochs before full learning rate.
dataset.load_mask: Whether annotations contain pre-computed segmentation masks. Set this to false for COCO annotations that contain boxes but no segmentation fields when performing training or inference without mask ground truth. Keep it true when every annotation contains segmentation data. AutoML validation that selects by mIoU requires segmentation ground truth and dataset.load_mask: true; bbox-only evaluation emits non-finite mIoU and must not be accepted as a valid AutoML objective.

AutoML / HPO Notes

For MAL AutoML launches, keep the default smoke search space narrow and pass automl_hyperparameters=["train.lr", "train.wd"]. Use conservative Bayesian ranges around the ViT-MAE fine-tuning defaults, for example train.lr from 1e-7 to 1e-5 and train.wd from 1e-5 to 1e-2. The packaged train schema marks these two parameters as the default AutoML parameters; pass them explicitly when using a runtime that still derives MAL search metadata from its bundled config module.

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

Multi-GPU strategy: ddp_find_unused_parameters_true
No fsdp support
LR auto-scaling: lr = lr * num_devices * batch_size (learning rate is scaled automatically by device count and batch size)

Multi-node env vars (set by orchestrator): WORLD_SIZE, NODE_RANK, MASTER_ADDR, MASTER_PORT, NUM_GPU_PER_NODE.

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 24GB+ (A100 recommended) VRAM per GPU. ViT-MAE backbone at crop_size=512 needs 24GB+ GPU memory.

Error Patterns

CUDA out of memory: Reduce dataset.crop_size (512 -> 384 -> 256) or use a smaller ViT-MAE variant (base vs large).

Key crop_size not in MALModelConfig: The crop-size override was placed under model.crop_size. Move it to dataset.crop_size.

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 mal.config.json:

Action	Spec Field	Inference Function	Meaning
evaluate	`evaluate.checkpoint`	`parent_model`	model file inferred from the parent job results folder
evaluate	`results_dir`	`output_dir`	current job results directory
inference	`inference.checkpoint`	`parent_model`	model file inferred from the parent job results folder
inference	`inference.label_dump_path`	`create_inference_result_file_mal`	MAL inference JSON path
inference	`results_dir`	`output_dir`	current job results directory
train	`train.pretrained_model_path`	`ptm_if_no_resume_model`	optional pretrained model when not resuming
train	`train.resume_training_checkpoint_path`	`resume_model`	exact checkpoint for resume runs
train	`results_dir`	`output_dir`	current job results directory

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.

Files

ATTRIBUTION.mdAdded by AutoGineer945 B
BENCHMARK.md7.0 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md11.1 KiB
config/skillspector-baseline.yaml1.0 KiB
evals/evals.json821 B
references/skill_info.yaml3.4 KiB
references/spec_template_evaluate.yaml1.8 KiB
references/spec_template_inference.yaml1.8 KiB
references/spec_template_train.yaml1.5 KiB
schemas/evaluate.schema.json20.2 KiB
schemas/inference.schema.json20.1 KiB
schemas/manifest.json3.7 KiB
schemas/train.schema.json17.3 KiB
skill-card.md4.7 KiB
skill.oms.sig6.6 KiB

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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, 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-mask-auto-label · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

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