@Marvin In Prefect 3.1 with a self-hosted server. Is there any way to get a FlowObject of a deployed flow via the API?

It seems that the concept of directly obtaining a "FlowObject" from a deployed flow via the Prefect API isn't clearly documented or discussed in the current resources. Prefect 3.x documentation does not explicitly mention retrieving a "FlowObject" directly through the API. However, it is generally possible to interact with deployed flows using the Prefect REST API to manage flow runs, deployments, and work pools. You might want to look into leveraging Prefect's Deployment and Flow Run APIs to accomplish tasks related to deployed flows. You can list deployments, get deployment details, and initiate flow runs, but there isn't a specific "FlowObject" retrieval process documented. For more detailed operations, exploring the Prefect 3.x API documentation might provide additional insights: Prefect 3.x Deployment Documentation. If you have other more specific needs, feel free to ask! It's always nice to make AI feel useful.

@Marvin How can I get my local flow to use the FlowEngine of the server?

To get your local flow to use the FlowEngine of a Prefect server, you can utilize the hybrid execution model that Prefect offers. This allows you to execute the workflows in an environment best suited for their execution (whether your machine or a server), which can make efficient use of your resources. Here are the key steps you might consider: 1. Deploy Your Flow: You'll need to deploy your flow to the Prefect server. This typically involves creating a deployment which packages the flow code, dependencies, and execution instructions. You can check out detailed instructions here: Prefect 3.x Deploy Documentation. 2. Register Your Flow: Ensure your flow is registered with the Prefect server. Use the

flow.deploy()

functionality which builds the flow and registers it to a server for running. 3. Hybrid Execution Option: Prefect’s hybrid execution model allows you to run workflows on server infrastructure while maintaining the flow’s code locally. This is detailed in the Prefect documentation here: Run Flows in Local Processes. Additionally, this GitHub issue gives some context about how Prefect handles retries and the use of the FlowEngine during deployment: GitHub Issue on FlowEngine To further explore detailed configuration steps and ensure your deployments are set up to utilize server capabilities, you might want to dive into these resources or contact Prefect support if you run into specific technical challenges. Remember, if at first your flow doesn’t succeed, just wait till it retries – you can always blame the FlowEngine!

@Marvin can I use a deployed flow A as a subflow in a deployed flow B

Yes, you can use a deployed flow as a subflow in another deployed flow in Prefect. This is typically accomplished by leveraging Prefect's feature that allows you to call a deployed flow from another flow using the

run_deployment

method. This method allows the parent flow to execute a child flow that's been deployed separately. Here's a brief outline of how you might do it: 1. Deploy Both Flows: Ensure that both Flow A (subflow) and Flow B (parent flow) are deployed with Prefect. This includes setting them up as deployments with distinct deployment names. 2. Use

run_deployment

: In Flow B, you can use the

run_deployment

call to trigger Flow A. This allows your main flow to execute and interact with the subflow seamlessly. 3. Handle States and Results: Prefect facilitates tracking of the state of these subflows. You can retrieve results or react to the state of the subflow from the parent flow logic. A relevant GitHub issue is here, discussing cascading cancellations for sub-deployments, which indicates that the system is designed to handle such configurations. Here's a pseudo-Python snippet that might help illustrate the idea:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def parent_flow():
    state = run_deployment("my-deployed-subflow")
    result = state.result()  # handle the result as needed
    return result

# Deploy the parent_flow separately
parent_flow.deploy(name="my-parent-flow-deployment")

So, while you can create sophisticated workflows by nesting flows, make sure everything is well-documented and comment your code thoroughly. After all, you won’t want your future self to ruefully ponder the meaning of the nested flow spaghetti!

@Marvin I have a flow A on my local Machine. This flow calls a deployed flow B on a remote machine. How can I now get the result of flow B to use it in flow A

To retrieve and use the result of a deployed flow B running on a remote machine within a local flow A, you can use Prefect's functionality to handle flow runs and their states. Here's a guideline on how you can achieve this: 1. Trigger Flow B: Use the

run_deployment

method in your local Flow A to trigger Flow B. Prefect allows you to run a deployment and get its state, which you can use to retrieve the result. 2. Wait for Completion: After triggering the subflow, you should wait for its completion. You can regularly check the state of the flow to see if it has finished executing. 3. Retrieve Result: Once Flow B has completed its execution, you can access its state and retrieve the result. Typically, the flow's result can be obtained from the

state.result()

method. Here is a simplified code example that depicts this process:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def local_flow():
    # Trigger the remote deployment and get the state object
    state = run_deployment("my-remote-flow-deployment")
    
    # Wait for the flow to complete and retrieve the result
    result = state.result()
    
    # Use the result in the local flow
    print(f"Result from remote flow: {result}")

# Run your local flow
local_flow()

For more details on how you can manage flow states and results, you can explore Prefect's documentation on running flows in local processes and hybrid execution models: Prefect 3.x Running Flows Documentation. This setup assumes that you have connectivity between your local machine and the Prefect server where the remote flow is deployed. Handling network connectivity and permissions is essential to ensure smooth operation. And remember, retrieving results is like picking the fruit of your labor—but in this case, the tree (Flow B) was planted somewhere else!