CLI Reference¶

Autoware-ML provides a unified command-line interface for all major workflows.

Commands¶

Command	Purpose
`train`	Train models using PyTorch Lightning
`deploy`	Export models to ONNX and TensorRT
`mlflow-ui`	Launch the MLflow tracking UI
`create-dataset`	Generate dataset info files

train¶

Train a model using the specified Hydra configuration.

autoware-ml train --config-name <config_path> [hydra_overrides...]

All arguments after --config-name are passed to Hydra as overrides. See Configuration for details.

Examples:

# Basic training
autoware-ml train --config-name my_task/my_model

# With overrides
autoware-ml train --config-name my_task/my_model \
    trainer.max_epochs=100 \
    model.optimizer.lr=0.0001

deploy¶

Export a trained model to ONNX and TensorRT.

autoware-ml deploy --config-name <config_path> +checkpoint=<path> [options...]

Arguments:

--config-name: Path to config (same as used for training)
+checkpoint: Path to .ckpt checkpoint file

Options:

output_name=<name>: Base name for output files
output_dir=<path>: Output directory

Example:

autoware-ml deploy \
    --config-name my_task/my_model \
    +checkpoint=path/to/checkpoint.ckpt

mlflow-ui¶

Launch the MLflow tracking UI.

autoware-ml mlflow-ui [--port PORT] [--db-path PATH]

Options:

--port, -p: Port for the UI (default: 5000)
--db-path: SQLite database path (default: mlruns/mlflow.db)

create-dataset¶

Generate preprocessed info files for a dataset.

autoware-ml create-dataset \
    --dataset <name> \
    --task <task> \
    --root-path <path> \
    --out-dir <path> \
    [options...]

Arguments:

--dataset: Dataset name
--task: Task name (can be repeated for multiple tasks)
--root-path: Dataset root directory
--out-dir: Output directory for info files

Options:

--version: Dataset version
--max-sweeps: Max LiDAR sweeps to include
--info-prefix: Prefix for output files

Example:

autoware-ml create-dataset \
    --dataset nuscenes \
    --task my_task \
    --root-path /path/to/dataset \
    --out-dir /path/to/output