@Marvin I'm using Prefect 3. Is it possible to create a temporary deployment of a flow? The use case would be to run flows using Prefect on my local machine, without necessarily creating a workpool and a definite deployment. I know I can serve flows or deploy on a process work pool, but the problem is that if a colleague of mine is running a local worker on the same process workpool (or if they are serving the same flow), I risk starting jobs on their computers if I run a flow on my machine, which is not what I want. If I start a flow on my machine, I'd like for all subflows to run on my machine only.

In Prefect 3.x, it seems you are looking for a way to execute flows locally without interfering with others' setups or using shared resources like a work pool. Here's a concise way to achieve your goal: 1. Local Execution: You can run your flows locally by directly calling the flow function in your script. This will execute the flow and its tasks on your local machine without creating a deployment or using any work pools. 2. Isolation: To ensure isolation, avoid using shared work pools or workers. By running flows directly, you bypass the need for any external infrastructure, ensuring that all tasks execute on your local machine. 3. Hybrid Model: Prefect's hybrid execution model allows you to run workflows in environments best suited for them. For purely local execution, you can simply execute the flow function in your Python environment. Here's a simple example of how you might run a flow locally:

python
from prefect import flow

@flow
def my_flow():
    print("Running locally!")

# Execute the flow
my_flow()

This approach ensures that the execution is confined to your local environment without affecting others. For more details, you can explore Prefect's local execution documentation. If you ever need to retrieve flows from remote storage, you can use the

from_source

method, but for purely local execution, this is not necessary. If you have more specific requirements, feel free to share them!

@Marvin The problem is that each flow of our infrastructure is a separate Python project, so I cannot directly call the flow function from Python scripts. I need to rely on Prefect to run flows and subflows.

Given your requirement to run flows and subflows within separate Python projects without creating shared deployments or work pools, you can use Prefect's

serve

method to execute flows locally on the machine that triggers the orchestration job. This approach ensures that the flow runs in isolation on your local machine without affecting others. Here's a concise way to achieve this: 1. Use

flow.serve()

: This method allows you to deploy your flow on the same machine that triggers it. It doesn't require a work pool, ensuring that the work is executed locally. This is particularly useful when you want to test or run flows in isolation without affecting shared infrastructure. 2. Local Execution: By using

flow.serve()

, you ensure that the flow runs on your local machine. This method is designed to facilitate local testing and execution without the complexities of managing shared work pools. Here's an example of how you might set this up in your flow script:

python
from prefect import flow

@flow
def my_flow():
    print("Running in isolation on my local machine!")

# Serve the flow locally
my_flow.serve()

This approach keeps the execution isolated and confined to your local environment. For more information, you can refer to the Prefect documentation on local execution. If you have more questions or specific scenarios, feel free to ask!

@Marvin What happens if I serve a flow on my machine, and another person serves the same flow on their machine?

When you use

flow.serve()

on your machine, it creates a local deployment that listens for scheduled runs and executes them on your machine. If another person serves the same flow on their machine, they will have their own separate local deployment. Here’s what happens: 1. Independent Execution: Each instance of

flow.serve()

operates independently. This means that the flow runs scheduled or triggered on your machine will execute locally on your machine, and similarly for the other person's machine. 2. No Interference: Since each

flow.serve()

call creates a local deployment, there is no risk of your flow runs being executed on someone else's machine, or vice versa. Each person's environment is isolated. 3. No Shared State: The flows are completely independent, with no shared state or resources unless explicitly configured to do so (e.g., accessing a shared database or storage). This setup is ideal for development and testing scenarios where you want to ensure that your flow runs are confined to your local environment without affecting others. If you need to collaborate or share results, consider using shared storage or databases to exchange data between flows. For more details, you can refer to the Prefect documentation.

@Marvin but if two people are serving the same flow on different machine, and I call the function

run_deployment

to run the served flow, isn't it possible for the flow to run on the other machine the flow is being served on? How can Prefect know which of the two running worker is local to my machine?

When you use

run_deployment

to execute a served flow, Prefect will attempt to trigger the flow run on any available worker that is listening for that deployment. If multiple machines are serving the same deployment, there is indeed a possibility that the flow could run on any of those machines. Prefect doesn't inherently distinguish between which worker is on your local machine versus another. To ensure that the flow runs on your local machine, you can take the following steps: 1. Unique Deployment Names: Ensure that the deployment name is unique to your machine. This way, when you call

run_deployment

, it specifically targets the deployment instance served on your machine. 2. Local-only Execution: Instead of relying on

run_deployment

, you can directly execute the flow using the flow function in your local script, as previously discussed. This ensures that all executions occur locally. 3. Environment Variables or Configurations: Use environment variables or configuration files to differentiate deployments based on the machine. This can help in setting up unique identifiers for each deployment instance. 4. Work Pool Configuration: If you decide to use work pools, configure them to be unique to your machine. This way, the worker on your machine will only pick up tasks from the local work pool. In essence, the key is to ensure that the deployment or work pool configurations are unique to your machine to avoid cross-execution on other machines. For more details, you can explore the Prefect deployment documentation.

@Marvin how can I configure a work pool to be unique to my machine?

To configure a work pool that is unique to your machine, you can follow these steps: 1. Create a Unique Work Pool: Use a distinct name for your work pool that identifies it as specific to your machine. This helps ensure that only your local worker will interact with it. 2. Start a Worker for Your Work Pool: Ensure that the worker you start is associated with your unique work pool. This can be done by specifying the work pool name when starting the worker. 3. Configure Deployment to Use the Work Pool: When setting up your flow deployment, ensure it is configured to use your unique work pool. This way, the deployment's runs will only be picked up by workers associated with that pool. Here's a basic example of how you might set this up:

bash
# Create a unique work pool
prefect work-pool create --type process my-unique-work-pool

# Start a worker for your work pool
prefect worker start --pool my-unique-work-pool

By doing this, you ensure that only your machine's worker will pick up and execute tasks from your deployment. This setup isolates your execution environment, preventing interference from other machines. For more details, you can explore Prefect's work pools documentation.

@Marvin this way I would end up with a ton of different work pool, one for each person testing flow locally on their machine

Yes, creating separate work pools for each individual can indeed lead to a proliferation of work pools, which might not be ideal for every scenario. Here are a few strategies to manage this: 1. Naming Convention: Use a consistent naming convention that includes the user's machine name or username, making it easier to identify and manage these work pools. For example,

work-pool-<username>

. 2. Automated Cleanup: Implement a script or process to periodically clean up unused or old work pools that are no longer needed. This can help manage the number of work pools over time. 3. Shared Configuration: If feasible, consider using environment variables or configuration files to dynamically assign work pools based on the user's environment, reducing the need for manual creation. 4. Local Execution Without Work Pools: If the goal is purely local testing, consider executing flows directly as Python scripts without creating deployments or work pools. This can be done by calling the flow function directly in a local script, as previously mentioned. While having many work pools might seem cumbersome, these strategies can help manage them effectively, ensuring that local testing remains isolated without cluttering your infrastructure. If you have specific constraints or further questions, feel free to share!