@Marvin I’m working on a Prefect-based orchestration use case where we need to dynamically run a set of subflows defined in a configuration file (

flows_config.yaml

). Each subflow resides in its own Python script, and the orchestrator needs to load and execute these subflows dynamically. any sample working code is appiciated

To dynamically run subflows defined in a configuration file using Prefect, you can follow these steps: 1. Read Configuration: Parse your

flows_config.yaml

to get the list of subflows and their corresponding script paths. 2. Load and Execute Subflows: Use Prefect's capabilities to load and execute these subflows dynamically. You can use the

flow.from_source

method to retrieve flows from remote storage or local scripts. This method accepts a repository URL or a local path to the flow script. Here's a simple example to illustrate how you might achieve this:

python
import yaml
from prefect import flow, task
from prefect.deployments import flow_from_source

@task
def load_config(config_path):
    with open(config_path, 'r') as file:
        return yaml.safe_load(file)

@flow
def orchestrate_flows(config_path: str):
    # Load configuration
    config = load_config(config_path)

    # Iterate over subflows
    for flow_config in config['subflows']:
        flow_path = flow_config['path']
        flow_name = flow_config['name']
        
        # Load and run the subflow
        subflow = flow_from_source(flow_path, flow_name)
        subflow.run()

# Example usage
orchestrate_flows('flows_config.yaml')

### Key Points: - Configuration Parsing: Use

yaml.safe_load()

to parse your YAML configuration file. - Dynamic Flow Loading: Use

flow_from_source()

to dynamically load and execute subflows from their respective scripts. - Execution: Iterate through each subflow configuration and execute them. ### References: - Prefect Documentation on Flow Execution - Prefect `flow.from_source` Method This should provide a starting point for your orchestration use case. Let me know if you need further assistance!

@Marvin subflow.run() runs the flows on local or on prefect server ?

The

subflow.run()

method executes the flows locally within the same Python process. It does not interact with Prefect Cloud or a Prefect Server by default. This is useful for testing or running flows in a local development environment. If you want to execute flows on Prefect Cloud or Prefect Server, you would typically use deployments and agents to manage flow execution across distributed environments. This involves creating a deployment for your flow and then using a Prefect agent to execute the flow in the desired environment. For more information on running flows in different environments, you can refer to the Prefect Deployment Documentation.