madsci.experiment_manager 0.5.2

The Modular Autonomous Discovery for Science (MADSci) Experiment Manager.

MADSci Experiment Manager

Manages experimental runs across a MADSci-powered lab, providing experiment design, tracking, and lifecycle management.

Features

• Experiment Designs: Define experimental parameters, conditions, and metadata
• Experiment Runs: Track individual experiment executions with status and results
• Lifecycle Management: Monitor experiment progress from design to completion
• Status Management: Support for pausing, resuming, cancelling, and failing experiments
• Integration: Works with all MADSci managers for comprehensive lab coordination

Installation

See the main README for installation options. This package is available as:

• PyPI: pip install madsci.experiment_manager
• Docker: Included in ghcr.io/ad-sdl/madsci
• Example configuration: See example_lab/managers/example_experiment.manager.yaml

Dependencies: MongoDB database (see example_lab)

Usage

Quick Start

Use the example_lab as a starting point:

# Start with working example
docker compose up  # From repo root
# Experiment Manager available at http://localhost:8002/docs

# Or run standalone
python src/madsci_experiment_manager/madsci/experiment_manager/experiment_server.py

Manager Setup

For custom deployments, see example_experiment.manager.yaml for configuration options.

Environment Variables

The Experiment Manager supports configuration via environment variables with the EXPERIMENT_ prefix:

• EXPERIMENT_SERVER_URL: Experiment Manager server URL (default: http://localhost:8002)
• EXPERIMENT_MANAGER_DEFINITION: Path to manager definition file (default: experiment.manager.yaml)
• EXPERIMENT_DB_URL: Database connection URL (default: mongodb://localhost:27017)

Example:

export EXPERIMENT_SERVER_URL=http://localhost:8002
export EXPERIMENT_DB_URL=mongodb://localhost:27017
export EXPERIMENT_MANAGER_DEFINITION=my_experiment.manager.yaml

Configuration files are also supported: .env, experiments.env, settings.toml, experiments.settings.toml, etc.

Experiment Client

Use ExperimentClient to manage experiments programmatically:

from madsci.client.experiment_client import ExperimentClient
from madsci.common.types.experiment_types import (
    ExperimentDesign,
    ExperimentRegistration,
)

client = ExperimentClient("http://localhost:8002")

# Design an experiment
design = ExperimentDesign(
    experiment_name="Compound Screen Experiment",
    experiment_description="Screen compounds for activity",
    resource_conditions=[]  # Define required resources/conditions
)

# Register and start an experiment
experiment = client.start_experiment(
    experiment_design=design,
    run_name="Screen Run 1",
    run_description="Testing compound A at concentration 10"
)

# Get experiment details
experiment_details = client.get_experiment(experiment.experiment_id)

# Control experiment lifecycle
paused = client.pause_experiment(experiment.experiment_id)
continued = client.continue_experiment(experiment.experiment_id)
ended = client.end_experiment(experiment.experiment_id)

Core Concepts

Experiment Designs

Templates defining experimental parameters and structure:

• Parameter definitions: Specify experiment variables and ranges
• Conditions: Define prerequisites and constraints
• Metadata: Store design rationale and protocols

ExperimentDesign Fields:

• experiment_name (str): The name of the experiment
• experiment_description (Optional[str]): A description of the experiment
• resource_conditions (list[Conditions]): Starting layout of resources required
• ownership_info (OwnershipInfo): Information about users, campaigns, etc. that own this design

ExperimentRegistration Fields:

• experiment_design (ExperimentDesign): The experiment design to execute
• run_name (Optional[str]): Name for this specific experiment run
• run_description (Optional[str]): Description of the experiment run

Experiment Runs

Individual executions of an experiment design:

• Status tracking: Monitor progress from registration to completion
• Results storage: Capture experimental outcomes and data
• Lineage: Link runs to their designs

Experiment States:

• in_progress: Experiment is currently running
• paused: Experiment is not currently running but can be resumed
• completed: Experiment run has finished successfully
• failed: Experiment has failed during execution
• cancelled: Experiment has been cancelled by user or system
• unknown: Experiment status is unknown

Experiment Application

The ExperimentApplication class provides scaffolding for custom experiment logic:

from madsci.experiment_application.experiment_application import ExperimentApplication

class MyExperiment(ExperimentApplication):
    def run_experiment(self, experiment_id: str) -> dict:
        # Custom experimental logic
        # Use other MADSci clients (workcell, data, etc.)
        return {"result": "success"}

app = MyExperiment(experiment_server_url="http://localhost:8002")
app.start()

API Endpoints

The Experiment Manager provides the following REST endpoints:

Experiment Management

• GET /experiment/{experiment_id} - Get an experiment by ID
• GET /experiments?number=10 - Get latest experiments (default: 10)
• POST /experiment - Start a new experiment (body: ExperimentRegistration)

Experiment Lifecycle Control

• POST /experiment/{experiment_id}/end - End an experiment
• POST /experiment/{experiment_id}/continue - Continue a paused experiment
• POST /experiment/{experiment_id}/pause - Pause an experiment
• POST /experiment/{experiment_id}/cancel - Cancel an experiment
• POST /experiment/{experiment_id}/fail - Mark an experiment as failed

Service Management

• GET /definition - Get manager definition and configuration
• GET /health - Get manager health status (includes database connectivity)

Full API documentation is available at http://localhost:8002/docs when the service is running.

Integration with MADSci Ecosystem

The Experiment Manager coordinates with other MADSci components:

• Workcell Manager: Execute workflows as part of experiments
• Data Manager: Store experimental results and files
• Event Manager: Log experimental events and milestones
• Resource Manager: Track samples and consumables used

Example: See example_lab/ for complete integration examples with all managers working together.