Runtime Workflow | AODT Documentation

The runtime workflow uses prebuilt Docker images and precompiled binaries for the worker and client. Both the worker and client must be running; the viewer is optional.

The Quickstart section provides end-to-end examples that follow this workflow.

Workflow overview

Step	Action	Example script
1	Generate a scene map	`examples/example_prepare_map.py`
2	Start the worker	`./worker/up.sh`
3	Create or modify a simulation YAML	`examples/example_client_yaml_config.py`, `examples/example_import_yaml_config.py`
4	Run the simulation	`examples/example_full_sim.py`, `examples/example_multi_timesteps.py`
5	Read results from the database	See Database exploration below

Calibration runs independently of this workflow. See Calibration for details.

1. Create a map (GIS pipeline)

Use the GIS pipeline to generate a USD map from OpenStreetMap or CityGML source data:

$ python examples/example_prepare_map.py --s3_endpoint <s3_endpoint>

See the GIS Pipeline guide for full instructions.

2. Start the worker

From the package root on the worker host:

$ cd aodt_1.5.0
$ ./worker/up.sh

This starts the Digital Twin worker along with the Iceberg catalog, S3 bucket (MinIO), and supporting infrastructure.

3. Create a configuration file

Use the configuration builder APIs to author or modify simulation YAML files:

Script	Purpose
`examples/example_client_yaml_config.py`	Create a new YAML file from scratch
`examples/example_import_yaml_config.py`	Load and modify an existing YAML file

See the Config Builder API for parameter details.

4. Run the simulation

The simulation management API supports starting, stopping, pausing, resuming, and querying simulation status.

Script	Mode
`examples/example_full_sim.py`	Batched mode (EM or RAN)
`examples/example_multi_timesteps.py`	Interactive EM mode

Database exploration

After a simulation completes, use these scripts to inspect and extract results:

Script	Purpose
`examples/example_query_tables.py`	Query database tables
`examples/example_drop_database.py`	Drop a database
`examples/extract_CFR_sample.py`	Extract a CFR sample
`examples/extract_CIR_sample.py`	Extract a CIR sample
`examples/plot_PDP_from_CIR.py`	Plot a power delay profile from CIR data
`examples/ran_metric.py`	Read RAN telemetry metrics

Next steps

EM Simulations — step-by-step batched EM quickstart
RAN Simulations — step-by-step batched RAN quickstart
Development Workflow — modify source and rebuild when prebuilt binaries are not sufficient