@Marvin can I run a thousand of deployments at one time?

So, you want to launch a Prefect deployment army. Let's figure out if you can. But first, which version of Prefect are you using?

@Marvin prefect 3.0.2

While Prefect doesn't explicitly mention a limit on the number of deployments you can run at once, there are some factors to consider, like concurrency limits and slot decay, that might impact this. 1. Concurrency Limits: You can set global concurrency limits with slot decay rates that determine how frequently tasks can run. Managing these will help in scaling your deployments efficiently. 2. Multi-Deployment Capabilities: Prefect does allow creating multiple deployments from the same or different flows, which can be managed independently. To get into specifics and read further: - Global Concurrency Limits - Multiple Deployments Example Keep an eye on managing your work pools and setting appropriate decay rates to handle high-scale deployments effectively.

@Marvin can I keep docker container running to server next flows even after worker made its job already?

It looks like your question ties into how Prefect handles Docker containers once a task is completed. Prefect's standard setup for worker infrastructure in Docker generally doesn't keep containers running after they've finished their tasks—they start up, execute the flow, and then shut down. Here is what we know from the documentation: 1. Docker container work pools are created and managed through Prefect, and are typically meant to start, execute the flow, and stop. 2. The current implementations and examples do not discuss keeping containers alive for subsequent tasks or flows. For more details, you can read: - Running flows in Docker containers For persistent containers, you might need custom handling or orchestration beyond Prefect's default setup, potentially integrating continuous running services or using a different approach.

@Marvin given that I have k8s or docker work pool, is this possible to incorporate worker into the container itself, set it's type to 'process' and keep container running and ready to accept requests even after run_deployment is done? I am going to run thousands of jobs on such containers with AI models and spinning up them again from the scratch would take a lot of time since loading model weights is pretty heavy task

It appears that Prefect does not natively support keeping Docker containers running after the job is complete when using Prefect workers. The typical setup involves the container being terminated after the run is done. However, from the documentation and some related GitHub issues, here are a few key points and potential approaches: 1. Workers Polling Mechanism: Prefect workers are responsible for retrieving scheduled runs from a work pool and executing them. Workers correspond to the execution environment (like Docker or Kubernetes). 2. Modification Potential: While Prefect’s setups for Docker and Kubernetes involve spinning up and down with each task, it might be possible to customize your Docker entrypoint or Kubernetes job definitions to keep the container running and reuse it for subsequent tasks. This would involve significant customization beyond the default setup. For more details: - Running flows in Docker containers - Implementing Workers For a custom solution, your approach might involve: - Running a persistent Prefect worker inside your Docker container. - Configuring that worker to use a process-based execution type to handle jobs without shutting down the container. However, such setups would require you to handle custom logic to maintain the container’s persistence and responsiveness. For any specific examples or if you face issues during custom setups, Prefect's community forums or GitHub might provide further insights or examples from other users facing similar challenges.

@Marvin can you tell me how to serve multiple flows?

To serve multiple flows simultaneously with Prefect, you can use the

serve

utility. Here is a concise guide on how to do it: 1. Define Your Flows: Create your flow functions using the

@flow

decorator. 2. Convert to Deployments: Use the

to_deployment

method to create deployments for your flows. 3. Serve Your Deployments: Utilize the

serve

function to run multiple deployments in the same process. Here's an example to get you started:

python
import time
from prefect import flow, serve

@flow
def slow_flow(sleep: int = 60):
    "Sleepy flow - sleeps the provided amount of time (in seconds)."
    time.sleep(sleep)

@flow
def fast_flow():
    "Fastest flow this side of the Mississippi."
    return

if __name__ == "__main__":
    slow_deploy = slow_flow.to_deployment(name="sleeper", interval=45)
    fast_deploy = fast_flow.to_deployment(name="fast")
    serve(slow_deploy, fast_deploy)

In the example above: -

slow_flow

and

fast_flow

are defined as flows. - Each flow is converted into a deployment using

to_deployment

. - The

serve

function runs these deployments concurrently. For more detailed information, refer to: - Serve multiple flows at once - Working with multiple deployments

@Marvin serve async flow

To serve an asynchronous flow in Prefect, you can follow these steps: 1. Define Your Async Flow: Use the

@flow

decorator along with async functions. 2. Convert to Deployment: Convert your async flow to a deployment. 3. Serve the Async Deployment: Use the

serve

function to run the async deployments concurrently. Here is an example:

python
import asyncio
from prefect import flow, serve, task

@task
async def make_http_request():
    await asyncio.sleep(1)
    print("Making an HTTP request...")

@flow
async def async_flow():
    for _ in range(10):
        await make_http_request.submit()

if __name__ == "__main__":
    async_deploy = async_flow.to_deployment(name="async-flow")
    serve(async_deploy)

In this example: -

make_http_request

is an async task that simulates making an HTTP request. -

async_flow

is an async flow that submits multiple requests. - The

serve

function is used to run the asynchronous deployment. Make sure you run the script in the appropriate environment that supports async execution. Reference: - Handling Asynchronous Tasks and Flows - Serving Multiple Flows

@Marvin is this possible to 'await' running deployment?

Based on the documentation and GitHub issues, it is possible to

await

a running deployment when using Prefect. However, there are some nuances to keep in mind: 1. Standard Setup: Prefect's

run_deployment

function can be awaited to manage asynchronous execution, and you need to ensure that states are handled correctly. 2. Flow and Task Results: By using

flow_run.state.result()

with appropriate options (

fetch=True

,

raise_on_failure=True

), you can ensure that your asynchronous operations handle results and exceptions as expected. Here is an example of how you might asynchronously run a deployment and await its completion:

python
import asyncio
from prefect import flow, task
from prefect.states import State
from prefect.client import get_client

@task
async def async_task():
    await asyncio.sleep(1)
    print("Async task completed.")

@flow
async def parent_flow():
    state = await run_deployment(name="my-deployment")
    result = await state.result(fetch=True, raise_on_failure=True)
    print(result)

if __name__ == "__main__":
    asyncio.run(parent_flow())

1. Task Definition: Define an asynchronous task

async_task

. 2. Flow Definition: Create a parent flow (

parent_flow

), which calls

run_deployment

. 3. Awaiting Deployment: In the parent flow, the deployment is run asynchronously, and the result is awaited. For further details, you can refer to the relevant discussions on GitHub: - Inconsistent `raise_on_failure=True` behaviour for `flow_run.state.result()` when using `run_deployment` By following these steps, you should be able to run and await asynchronous deployments in Prefect effectively.