MAL

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.

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 still requires

schemas/train.schema.json

and

references/spec_template_train.yaml

to exist and parse. Use the packaged train 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. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as

automl_policy: off

for this run only; otherwise default to

auto

. When

automl_policy: auto

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

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

python

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

train (mandatory data sources):

python

{
    "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):

python

{
    "dataset.val_img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val_ann_path": f"{S3_EVAL}/annotations.json",
}

inference (mandatory data sources):

python

{
    "inference.img_dir": f"{S3_EVAL}/images.tar.gz",
    "inference.ann_path": f"{S3_EVAL}/annotations.json",
}

Eval Dataset

Optional. Val images and annotations configured alongside train paths.

Important Parameters

model.arch: ViT-MAE backbone variant. Default vit-mae-base/16. Options include vit-mae-large/16 and other ViT-MAE variants.
train.lr: Learning rate. Default 1e-6 (very low — fine-tuning ViT).
model.crop_size: Training crop size. Default 512.
train.warmup_epochs: Warmup epochs before full learning rate.
model.load_mask: Whether to load pre-computed masks.

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 model.crop_size (512 -> 384 -> 256) or use a smaller ViT-MAE variant (base vs large).

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	`results_dir`	`output_dir`	current job results directory

For

parent_model

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.

tao-train-mask-auto-label

NPX Install

Tags

SKILL.md Content

MAL

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference