madsci.experiment_manager 0.6.1

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.

Database Migration Tools

MADSci Experiment Manager includes automated MongoDB migration tools that handle schema changes and version tracking for the experiment management system.

Features

• Version Compatibility Checking: Automatically detects mismatches between MADSci package version and MongoDB schema version
• Automated Backup: Creates MongoDB dumps using mongodump before applying migrations to enable rollback on failure
• Schema Management: Creates collections and indexes based on schema definitions
• Index Management: Ensures required indexes exist for optimal query performance
• Location Independence: Auto-detects schema files or accepts explicit paths
• Safe Migration: All changes are applied transactionally with automatic rollback on failure

Usage

Standard Usage

# Run migration for experiments database (auto-detects schema file)
python -m madsci.common.mongodb_migration_tool --database madsci_experiments

# Migrate with explicit database URL
python -m madsci.common.mongodb_migration_tool --db-url mongodb://localhost:27017 --database madsci_experiments

# Use custom schema file
python -m madsci.common.mongodb_migration_tool --database madsci_experiments --schema-file /path/to/schema.json

# Create backup only
python -m madsci.common.mongodb_migration_tool --database madsci_experiments --backup-only

# Restore from backup
python -m madsci.common.mongodb_migration_tool --database madsci_experiments --restore-from /path/to/backup

# Check version compatibility without migrating
python -m madsci.common.mongodb_migration_tool --database madsci_experiments --check-version

Docker Usage

When running in Docker containers, use docker-compose to execute migration commands:

# Run migration for experiments database in Docker
docker-compose run --rm experiment-manager python -m madsci.common.mongodb_migration_tool --db-url 'mongodb://mongodb:27017' --database 'madsci_experiments' --schema-file '/app/madsci/experiment_manager/schema.json'

# Create backup only in Docker
docker-compose run --rm experiment-manager python -m madsci.common.mongodb_migration_tool --db-url 'mongodb://mongodb:27017' --database 'madsci_experiments' --schema-file '/app/madsci/experiment_manager/schema.json' --backup-only

# Check version compatibility in Docker
docker-compose run --rm experiment-manager python -m madsci.common.mongodb_migration_tool --db-url 'mongodb://mongodb:27017' --database 'madsci_experiments' --schema-file '/app/madsci/experiment_manager/schema.json' --check-version

Server Integration

The Experiment Manager server automatically checks for version compatibility on startup. If a mismatch is detected, the server will refuse to start and display migration instructions:

DATABASE INITIALIZATION REQUIRED! SERVER STARTUP ABORTED!
The database exists but needs version tracking setup.
To resolve this issue, run the migration tool and restart the server.

Schema File Location

The migration tool automatically searches for schema files in:

• madsci/experiment_manager/schema.json

Backup Location

Backups are stored in .madsci/mongodb/backups/ with timestamped filenames:

• Format: madsci_experiments_backup_YYYYMMDD_HHMMSS
• Can be restored using the --restore-from option

Requirements

• MongoDB server running and accessible
• MongoDB tools (mongodump, mongorestore) installed
• Appropriate database permissions for the specified user