tao-train-mask-auto-label

--- name: tao-train-mask-auto-label description: 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". license: Apache-2.0 compatibility: Requires docker + nvidia-container-toolkit. metadata: version: "0.1.0" author: NVIDIA Corporation allowed-tools: Read Bash tags: - segmentation --- # 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). ```bash 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 "$RUN_ROOT/results:/results" ) ``` Train: ```bash docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \ mal train -e /specs/train.yaml ``` Evaluate: ```bash docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \ mal evaluate -e /specs/evaluate.yaml ``` Inference: ```bash docker run "${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. ```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 { "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):** ```python { "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.

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