PointPillars

PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via pillar-based representation, then applies 2D detection. Used in autonomous driving / robotics.

Typically trained from scratch. Provide train.resume_training_checkpoint_path to resume.

For TAO Deploy TensorRT actions (

gen_trt_engine

, TensorRT

evaluate

, and TensorRT

inference

), read

references/tao-deploy-pointpillars.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 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: pointpillars
Formats: default
Monitoring metric: loss

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
dataset_convert	dataset.data_path	id		No
evaluate	dataset.data_path	train_datasets		No
evaluate	dataset.data_info_path	train_datasets	/results/{dataset_convert_job_id}/data_info/	No
export	dataset.data_path	train_datasets		No
export	dataset.data_info_path	train_datasets	/results/{dataset_convert_job_id}/data_info/	No
inference	dataset.data_path	train_datasets		No
inference	dataset.data_info_path	train_datasets	/results/{dataset_convert_job_id}/data_info/	No
prune	dataset.data_path	train_datasets		No
prune	dataset.data_info_path	train_datasets	/results/{dataset_convert_job_id}/data_info/	No
retrain	dataset.data_path	train_datasets		No
retrain	dataset.data_info_path	train_datasets	/results/{dataset_convert_job_id}/data_info/	No
train	dataset.data_path	train_datasets		No
train	dataset.data_info_path	train_datasets	/results/{dataset_convert_job_id}/data_info/	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"

train (mandatory data sources):

python

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

evaluate (mandatory data sources):

python

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

export (mandatory data sources):

python

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

inference (mandatory data sources):

python

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

prune (mandatory data sources):

python

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

retrain (mandatory data sources):

python

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

Eval Dataset

Optional. Validation data (val.tar.gz) is separate from training. Used for mAP evaluation.

Important Parameters

train.num_epochs: Default 80 (much higher than other TAO models). PointPillars needs more epochs for convergence on 3D detection.
train.lr: Learning rate. Default 0.003 (adam_onecycle scheduler).
dataset.class_names: List of 3D object classes. Default 7 classes (KITTI-style). Modify to match your dataset.
dataset.data_path: Path to point cloud data directory.
dataset.data_info_path: Path to data info files from dataset_convert step.
dataset.point_cloud_range: Spatial extent of the point cloud to consider. Must match your sensor configuration.
model.dense_head.anchor_generator_config: Anchor configurations per class. Must be tuned for your object sizes and the point cloud range.

Multi-GPU / Multi-Node

Launch method:

torchrun

(LIGHTNING_EXCLUDED_NETWORK). Uses PyTorch native

DistributedDataParallel

(NOT Lightning Trainer).

Spec Key	Description	Default
`train.num_gpus`	Number of GPUs per node	1
`train.gpu_ids`	GPU device indices	[0]
`train.num_nodes`	Number of nodes	1

```
CUDA_VISIBLE_DEVICES
```
is explicitly set from
```
TAO_VISIBLE_DEVICES
```
Uses
```
nn.parallel.DistributedDataParallel
```
directly (not Lightning strategy)
```
NODE_RANK
```
is copied to
```
RANK
```
if
```
RANK
```
is unset

Multi-node env vars (set by orchestrator):

Variable	Purpose
`WORLD_SIZE`	Number of nodes
`NODE_RANK`	This node's rank
`MASTER_ADDR`	Rank-0 node IP
`MASTER_PORT`	Rank-0 port (default 29500)
`NUM_GPU_PER_NODE`	GPUs per node

Hardware

Minimum 1 GPU(s), recommended 4 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. PointPillars is relatively efficient for 3D detection. The main bottleneck is data I/O for large point cloud datasets.

Error Patterns

dataset_convert required: Training will fail if data_info_path is not populated from a prior dataset_convert job. Always run convert first.

Point cloud range mismatch: If point_cloud_range does not match the actual sensor data extent, detections will be poor or empty.

Epoch numbering: PointPillars checkpoint epoch numbers may be offset by 1 from status.json reported epochs.

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

pointpillars.config.json

Action	Spec Field	Inference Function	Meaning
dataset_convert	`results_dir`	`output_dir`	current job results directory
evaluate	`evaluate.checkpoint`	`parent_model`	model file inferred from the parent job results folder
evaluate	`key`	`key`	encryption key
evaluate	`results_dir`	`output_dir`	current job results directory
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	`export.save_engine`	`create_engine_file`	output TensorRT engine path
export	`key`	`key`	encryption key
export	`results_dir`	`output_dir`	current job results directory
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.save_engine`	`create_engine_file`	output TensorRT engine path
gen_trt_engine	`key`	`key`	encryption key
gen_trt_engine	`results_dir`	`output_dir`	current job results directory
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	`key`	`key`	encryption key
inference	`results_dir`	`output_dir`	current job results directory
prune	`key`	`key`	encryption key
prune	`prune.model`	`parent_model`	model file inferred from the parent job results folder
prune	`results_dir`	`output_dir`	current job results directory
retrain	`key`	`key`	encryption key
retrain	`results_dir`	`output_dir`	current job results directory
retrain	`train.pruned_model_path`	`parent_model`	model file inferred from the parent job results folder
train	`key`	`key`	encryption key
train	`model.pretrained_model_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`results_dir`	`output_dir`	current job results directory
train	`train.resume_training_checkpoint_path`	`resume_model`	model file inferred from the current job results folder

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.

Deployment

tao-deploy-pointpillars — PointPillars deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

tao-train-pointpillars

NPX Install

Tags

SKILL.md Content

PointPillars

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

Deployment