@Marvin I'd like to surface a clear worker identifier on the flow run UI. Whats the best way to achieve this? Can I add tags to flow runs based on which worker claimed the flow?

To achieve displaying a clear worker identifier on the flow run UI and adding tags to flow runs based on the worker claiming the flow, you can consider using the worker context and tags in Prefect. In both Prefect 2.x and 3.x, you can identify which worker executes a flow run by using the worker's information in your flow logic. Here's a consolidated strategy based on my research: ### Steps to Surface the Worker Identifier: 1. Access Worker Information: You can get contextual details by accessing flow run metadata during the execution. Prefect's runtime context can provide you with the execution environment details, including the worker information. 2. Tagging Flow Runs: - You can manually tag flow runs with worker identifiers. This involves assigning tags when a worker starts executing the flow. This tag can then appear in the UI, helping distinguish which worker handled the flow run. Unfortunately, Prefect documentation does not explicitly cover auto-tagging flows based on the worker. However, you can programmatically tag your flow runs by leveraging Prefect's Python API to assign or update tags. ### Resources to Consider: - For Worker and Flow Execution Contexts: Runtime Context in Prefect 3.x - For Worker Types and Configurations: Workers and Work Pools in Prefect 3.x By inspecting the specific flow run context or using scripting within the deployment setup, you might script the tagging operation to accomplish automated tagging. Ensure that your workers and flow execution plans are well-configured to reflect these identifiers correctly.

@Marvin Can you show me an example of assigning tags in the flow runtime

Here's an example on how you can assign tags to tasks during the flow runtime in Prefect. This example applies to versions 2.x and 3.x, given the similarity in how tags are handled across these versions. ### Example in Prefect 3.x In Prefect, you can use the

tags

context manager to add tags dynamically during the flow execution. Here's a sample code:

python
from prefect import flow, task
from prefect import tags

@task
def my_task():
    print("Hello, I'm a task")

@flow
def my_flow():
    # Assign a tag to the task run at runtime
    with tags("runtime-tag", "another-tag"):
        my_task()

if __name__ == "__main__":
    my_flow()

### Explanation -

tags

Context Manager: This method allows you to assign one or more tags temporarily during the execution of a specific block of code. The tags you provide will apply to all tasks executed within the context block. - Static vs. Dynamic Tagging: Tags can be statically assigned when defining tasks using the task decorator. However, using the

tags

context manager allows for dynamic assignment during runtime, suitable for scenarios where tags depend on runtime conditions or configurations, like identifying which worker processed the flow run. These tags can then be used in Prefect's UI to filter, monitor, and analyze task runs more effectively. For further details, you can look at the specific documentation for Prefect 3.x Task Writing which provides insights into using tags and task configurations.

@Marvin Can we tag the active flow run, rather than the tasks?

To tag the active flow run rather than the tasks within the Prefect pipeline, you can utilize runtime context and Prefect's client API to update the flow run's tags dynamically. ### Example to Tag the Active Flow Run Here's a basic example of how you can achieve this:

python
from prefect import flow
from prefect.client import get_client
from prefect.runtime import flow_run

@flow
async def my_flow():
    # Access the current flow run ID
    flow_run_id = flow_run.get_id()
    
    # Instantiate a client to communicate with the Prefect API
    async with get_client() as client:
        # Retrieve the current flow run's details
        my_flow_run = await client.read_flow_run(flow_run_id)
        
        # Add new tags to the existing ones
        current_tags = set(my_flow_run['tags'])
        new_tags = {"runtime-tag", "specific-worker"}
        
        # Update flow run tags
        await client.update_flow_run(flow_run_id, tags=list(current_tags.union(new_tags)))

if __name__ == "__main__":
    import asyncio
    asyncio.run(my_flow())

### Explanation - Accessing Flow Run Context: Utilize

prefect.runtime.flow_run

to acquire details about the current flow run, including its ID, which is essential for making updates. - Using Prefect Client: Use

get_client

to instantiate a client for interacting with Prefect's API. This client allows you to read and update the flow run. - Updating Flow Run Tags: With

client.update_flow_run

, you can append or modify the tags for the active flow run based on runtime conditions or logic. This approach allows dynamic tagging of flow runs based on execution context, such as associating a specific worker identifier or runtime condition with the flow run. This can be particularly useful for debugging, monitoring, or implementing custom execution logic.

With some slight tweaks the approach above works pretty well.