CLI Reference¶

Autoware-ML provides a unified command-line interface for all major workflows. Run the commands below either inside the Docker container or from a local pixi shell --environment default / pixi shell --environment dev. Bash completion is installed automatically by the Docker image build and by pixi run --environment <default|dev> setup-project for local installs.

Commands¶

Command	Purpose
`train`	Train models using PyTorch Lightning
`test`	Evaluate models from a checkpoint
`deploy`	Export models to ONNX and TensorRT
`mlflow ui`	Launch the MLflow tracking UI
`mlflow export`	Export one experiment into its own MLflow store
`session start`	Start a managed background task
`session attach`	View live terminal output from a background task
`session detach`	Disconnect raw tmux clients from a managed session
`session ls`	List managed background tasks
`session stop`	Stop a managed background task
`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 <task>/<model>/<config>

# With overrides
autoware-ml train --config-name <task>/<model>/<config> \
    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 <task>/<model>/<config> \
    +checkpoint=mlruns/<task>/<model>/<config>/<run_id>/artifacts/checkpoints/best.ckpt

test¶

Evaluate a trained model from a checkpoint.

autoware-ml test --config-name <config_path> +checkpoint=<path> [hydra_overrides...]

Arguments:

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

Example:

autoware-ml test \
    --config-name <task>/<model>/<config> \
    +checkpoint=mlruns/<task>/<model>/<config>/<run_id>/artifacts/checkpoints/best.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)

mlflow export¶

Export one experiment from the global MLflow store into an isolated store.

autoware-ml mlflow export [--db-path PATH] [--experiment-name NAME | --config-name CONFIG] [--export-dir PATH]

Options:

--db-path: SQLite database path (default: mlruns/mlflow.db)
--experiment-name: Exact MLflow experiment name to export
--config-name: Export the experiment matching this task config path
--export-dir: Directory for the extracted experiment store

session start¶

Start a detached managed session running an autoware-ml command.

autoware-ml session start --name <session_name> [--cwd PATH] [--attach] -- <autoware-ml command...>

Managed sessions use a private tmux server internally, but the public workflow is intentionally narrow: start a background task, view its live output, list running sessions, and stop the task.

Example:

autoware-ml session start --name calibration-status-train --cwd /workspace -- \
    train --config-name calibration_status/calibration_status_classifier/resnet18_t4dataset_j6gen2

Use --raw to run a non-autoware-ml command in the managed session:

autoware-ml session start --name docs --raw --cwd /workspace -- zensical serve

Use --attach with session start to open the live viewer immediately after startup. Use session attach later to view an already running task. In the viewer, Ctrl+C returns to your shell without stopping the task. To terminate the task, use autoware-ml session stop.

session attach¶

Render a live terminal view of an existing managed session.

autoware-ml session attach --name <session_name>

This is a read-only viewer, not a tmux client. Press Ctrl+C to exit the viewer while keeping the task running.

session detach¶

Disconnect raw tmux clients from an existing managed session.

autoware-ml session detach --name <session_name>

Most users do not need this command because autoware-ml session attach does not create a tmux client.

session ls¶

List managed background sessions.

autoware-ml session ls

session stop¶

Stop the tracked task and close its managed session.

autoware-ml session stop --name <session_name>

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