Lab Configuration

Your lab is configured in two files in the lab_adaption folder:

• platform_config.yaml — describes the physical resources in your lab (storage, movers, humans, etc.) and their capacities.
• config.py — describes how the orchestrator should start up: which worker to use, which processes to load, which scheduling algorithm to run, and how to connect to the database.

platform_config.yaml

This file lists every device/resource in your lab, grouped by category, and tells the scheduler how many things each resource can handle at once.

description:
    - "An exemplary robotic lab description. Add devices at will"
sila_servers:
    storage:
        Hotel1:
            capacity: 20
        Hotel2:
            capacity: 20
        Hotel3:
            capacity: 20
    movers:
        GenericArm:
            capacity: 1
    humans:
        Human:
            process_capacity: 99 # can do multitasking?
            capacity: 2  # has two hands
    greeters:
        Greeter:
            capacity: 1
            process_capacity: 1

pythonlab_translation:
    storage: LabwareStorageResource
    movers: MoverServiceResource
    humans: HumanServiceResource
    greeters: GreeterServiceResource

sila_servers

Each entry under sila_servers is a category (e.g. storage, movers, humans, greeters) containing one or more named devices. Add, remove, or rename devices here to match the hardware and staff available in your lab. Each device takes:

Field	Meaning
capacity	How many labware items/slots the device can hold or handle at the same time (e.g. a storage hotel with capacity: 20 has 20 slots; a robot arm with capacity: 1 can carry one item).
process_capacity (optional)	How many processes the device/person can do in parallel. Defaults to matching capacity if omitted.

You can add as many categories and devices as you need — this list should reflect everything the scheduler is allowed to plan work around.

pythonlab_translation

This section maps each category above to the PythonLab resource class used in your process descriptions (see the PythonLab api reference). The keys here must match the categories used under sila_servers, and the values must match the resource classes your workflows expect. If you add a new category to sila_servers, add a matching line here so the orchestrator knows how to interpret it in your processes.

config.py

This file controls how the orchestrator starts up. It's a plain Python file — uncomment or edit the lines you need; anything left out falls back to a sensible default.

Setting	What it does
db_client	Which database to use for tracking labware. Set to None to run without a database.
worker_type	Which worker drives execution. Leave as-is to just run a simulation; point it at your own worker implementation to control real hardware.
lab_config_file	Path to your platform_config.yaml. Change this if you rename or move the file.
default_scheduling_time	How many seconds the scheduler is given to compute a new schedule by default. Can also be changed later from the orchestrator GUI.
scheduling_algorithm	Which scheduling algorithm to use (e.g. BottleneckPD).
process_module	Which Python module contains your process descriptions (see pythonLab introduction).
arm_open	Whether the robotic arm view is shown by default in the orchestrator GUI.
db_open	Whether the labware database view is shown by default in the orchestrator GUI.
browser_open	Whether the SiLA browser view is shown by default in the orchestrator GUI.

After changing platform_config.yaml, remember to rerun the database setup script so the new resources are picked up (see the quickstart).