Track Twin Model Performance with Weights & Biases and AWS IoT TwinMaker SDK

                      
                     
from typing import Dict, Any
import os
import wandb
import boto3
from botocore.exceptions import ClientError

# Configuration
class Config:
    def __init__(self):
        self.aws_region = os.getenv('AWS_REGION', 'us-east-1')
        self.wandb_project = os.getenv('WANDB_PROJECT', 'twin_model_performance')
        self.aws_access_key = os.getenv('AWS_ACCESS_KEY')
        self.aws_secret_key = os.getenv('AWS_SECRET_KEY')

# Initialize Weights & Biases
wandb.init(project=Config().wandb_project)

# AWS IoT TwinMaker Client
class TwinMakerClient:
    def __init__(self, config: Config):
        self.client = boto3.client(
            'iot-twinmaker',
            aws_access_key_id=config.aws_access_key,
            aws_secret_access_key=config.aws_secret_key,
            region_name=config.aws_region
        )

    def get_twin_data(self, workspace_id: str, entity_id: str) -> Dict[str, Any]:
        try:
            response = self.client.get_twin(
                workspaceId=workspace_id,
                entityId=entity_id
            )
            return response['twin']
        except ClientError as e:
            print(f'Error getting twin data: {e}')
            return {}

# Main function to track performance
def track_performance(workspace_id: str, entity_id: str) -> None:
    config = Config()
    client = TwinMakerClient(config)
    twin_data = client.get_twin_data(workspace_id, entity_id)
    if twin_data:
        # Log metrics to Weights & Biases
        wandb.log({
            'accuracy': twin_data.get('accuracy', 0),
            'latency': twin_data.get('latency', 0)
        })

if __name__ == '__main__':
    workspace_id = os.getenv('WORKSPACE_ID')
    entity_id = os.getenv('ENTITY_ID')
    track_performance(workspace_id, entity_id)