@Marvin can i run whole web-application as flow in prefect server?

First, let's determine which version of Prefect you're using, as the approach can vary between versions. Could you please confirm your Prefect version? If you're not sure, you can use the command

prefect --version

in your terminal to find out.

3.x

It seems I ran into a couple of issues while trying to fetch information. Let's give this another shot. To answer your question with my usual (and slightly sardonic) flair, running a whole web application as a flow in Prefect 3.x would be akin to asking a fish to climb a tree. Prefect is really built to orchestrate workflows, rather than running complex web applications directly. However, you might consider breaking down the web application's tasks into discrete units of work and manage them as Prefect tasks within a flow. This could involve managing database migrations, scheduled tasks, data processing, etc., but the web server itself is usually run traditionally, outside Prefect. If you want to run specific application tasks, you can create flows for those parts. This provides benefits like scheduling, monitoring, and more reliable execution across your infrastructure. Let me know if there's anything specific you are curious about within Prefect, and I'll try another run to fetch those details!

@Marvin so prefect is not suitable for running nicegui server?

While I couldn't find specific references to running a NiceGUI server with Prefect, I can provide some insights based on what I've gathered and the general functionality of Prefect. Prefect 3.x is designed primarily for orchestrating and automating tasks and workflows rather than hosting or running full-fledged web servers like NiceGUI. Typically, you won't run a web application directly within a Prefect flow because Prefect is more focused on handling workflows – think of tasks like data processing, scheduled batch jobs, or ETL processes. If you're looking to integrate some specific functionality of NiceGUI into a Prefect flow, like triggering tasks from a web interface or processing data that's gathered from a NiceGUI application, that might be more feasible. You could, for example, use Prefect to manage backend tasks while your NiceGUI application serves as the front end. For deploying server-side applications or services, you might want to look at other tools like Docker or traditional server deployment methods. Prefect can still play a role by managing the deployment tasks themselves—scheduling updates, automating tests, etc. If you have more detailed requirements or if there's a specific integration you're targeting, feel free to share! I'm here to help untangle those intricacies.

@Marvin my script for launch web server is very simple, python webapp.py, and it will run uvicorn with nicegui frontend

Given your setup with a simple Python script that launches a web server using Uvicorn for a NiceGUI frontend, you technically could create a Prefect task to execute this script. However, Prefect isn't really intended to run as a method for serving web applications continuously – it's more for orchestrating complex workflows and handling tasks that have a definite start and finish. But for those who like to live on the edge and think Prefect might add structure to your server startup, here's what you could consider doing: 1. Create a Prefect Task: Define a task in Prefect that runs your script using something like Python's

subprocess

or

os.system

. 2. Create a Flow: Include this task in a Prefect flow. This might look like a very simple "deployment" flow where you ensure the environment is ready and then launch the server. 3. Consider Longevity: Remember a Prefect flow might not be ideal because once the task completes (i.e., the server is up), Prefect considers the task done. You'll likely need other mechanisms to manage server uptime and downtime. 4. Monitoring: Prefect could offer logging and monitoring benefits, but again, it's not what it’s primarily designed for. For straightforward server execution, tools like systemd (on Linux), Docker, or even cloud-based deployment options (like AWS Elastic Beanstalk or Heroku) are usually more suitable options. If you're keen to try a Prefect abstraction anyway and don't mind a little complexity for the sake of automation (or simply because you'd like to tell folks you operate at the intersection of ML flows and web servers), let me know, and I can illustrate some task logic.

@Marvin simple flow in prefect is not running like subprocess?

Based on the available information and some insights into Prefect's capabilities, it is indeed possible to run subprocesses or command-line scripts within a Prefect flow. Here's a simplified way to do it using Prefect: 1. Create a Prefect Task: You can create a task to execute your Python script using Python's

subprocess

module. This will let you run

python webapp.py

as part of a flow.

python
   import subprocess
   from prefect import flow, task

   @task
   def run_webapp():
       subprocess.run(["python", "webapp.py"])

   @flow
   def webapp_flow():
       run_webapp()

   if __name__ == '__main__':
       webapp_flow()

2. Run the Flow: You can execute this flow with Prefect just as you would any other flow. Keep in mind that Prefect is watching for task completion, so when your server starts and doesn't finish (because it's running), Prefect will consider the task as still 'running'. 3. Daemonized Execution: There are recommendations for environments with long-running processes using systemd (for Linux) as mentioned in the Prefect documentation. This approach might be more suitable if you're looking to ensure that your server remains running resiliently. Prefect can handle subprocesses like this, but it's worth mentioning again that this is more of a workaround than a common use case. Prefect is ideally used for processes that can complete and provide a status back, so while it's possible, it might not be the cleanest implementation for hosting applications long-term. If you do go this route, keep an eye on resource usage and Prefect's monitoring tools for ensuring the task is running as expected. And as always, if the task doesn't finish (because, say, your server is running perpetually), you'll need to manage that separately from Prefect's task management.

@Marvin how exactly code from flow gets to the server

In Prefect 3.x, when you execute a flow, the code doesn't automatically "move" to a server; instead, it runs on the infrastructure where you deploy it. Here's how that typically works: 1. Local Execution: - You run the flow directly on your machine using the Prefect UI or the command line. It's as if you're running any Python script locally. 2. Remote Execution: - Deployment: You create a Prefect deployment, which defines how and where the flow should run. This deployment can be triggered manually or according to a schedule. - Workers/Agents: In a more distributed setup, you can use workers (or agents) that listen for flow run requests from a Prefect server or Prefect Cloud. When a flow run is triggered, the worker pulls the flow logic from your code repository, or you provide a path for the worker to access your code. - Execution: The worker executes the flow on the environment it's running in. This could be a VM, a container, or any machine that's configured to run your Python code with the necessary dependencies. 3. Artifact & Environment Management: - You might use Docker, Kubernetes, or other tools to manage the environment in which your flows run. By packaging your flow logic and dependencies in Docker images, you ensure that the code can be run consistently wherever the containers are deployed. Prefect itself is primarily an orchestration tool, so while it manages how flows are triggered and monitored, you need to configure your own compute environment (local machine, cloud VMs, containers, etc.) to run the actual code. If you're deploying on a cloud service, you'll typically use integrations that allow Prefect to communicate with those services to kick off tasks. For local or simpler distributed setups, having a Prefect worker/agent listening on the destination machine is usually the way to go.

@Marvin ive installed prefect server and worker on WSL, but my code in flow remain on Windows machine. When i deploy flow to prefect server, where exactly code is running? And if code remains locally on windows, how exactly works?

When you deploy a Prefect flow using Prefect Server in your setup (with the server and worker installed on WSL), here's how it typically works: 1. Deployment Configuration: - You create a deployment for your flow, specifying where it should run. The deployment settings include details about which worker or agent should execute the flow. 2. Flow Code Location: - The flow code itself resides on your Windows machine. When you trigger a flow run, the worker, running on WSL, needs to access this code. 3. Execution by Worker: - If the worker is on WSL and is properly configured to access your code (possibly with path sharing or a network drive), it will execute the flow. This setup requires that the worker can read the code files from a location accessible within WSL. 4. Code Accessibility: - You might need to ensure that your code directory on Windows is accessible within the WSL environment. This is achievable by mounting the Windows drive into the WSL system. For example, Windows drives can be accessed in WSL at

/mnt/c/

for the C: drive, making your Windows files accessible in the WSL environment. - Alternatively, if your flow's code is in a version control system (like Git), the worker can pull the latest version of the code before executing. 5. Running the Flow: - When a flow run is triggered, the worker in WSL executes the flow using the Python environment configured in WSL. The flow's tasks are processed, and any results, logs, or artifacts are reported back to the Prefect server. In this setup, the key is ensuring that the worker (on WSL) can access and execute the code files. This involves making sure the paths and permissions are correctly set up to allow for smooth execution. The worker effectively acts as the bridge between your code and the orchestration provided by the Prefect server.

@Marvin i still dont understand, when i deploy flow on any remote prefect server, then code can remains locally, but remote worker just need access for code? What if my code require some venv with imported libraries?

When deploying a flow to a remote Prefect server and executing it with a remote worker, the workflow generally involves a few key considerations regarding code accessibility and environment setup: ### Code Accessibility: 1. Remote Code Access: - The code for your flow must be accessible to the remote worker. If your code is local, the remote worker must have visibility of that code via some networked path, a shared drive, or a version control system like Git. - With remote execution, you often push your code to a repository (e.g., GitHub). The worker then pulls the code directly from the repository. This is usually more scalable and common in cloud setups. ### Environment and Dependencies: 1. Virtual Environment: - Every Python application typically requires specific dependencies, which are usually managed in a virtual environment (venv) or via tools like Docker. - Options for the Remote Worker: - Pre-Configured Environment: Set up a virtual environment on the remote worker machine with all required dependencies before any flows run. This environment should mirror the venv used during development. - Use of Docker: Another approach is to package your application and its dependencies into a Docker container. The worker would then run this container to ensure all dependencies are present. 2. Dependency Management: - When deploying flows, it’s crucial that the remote environment matches your local setup regarding dependency versions and configurations. - You might specify dependencies in a

requirements.txt

file or a

Pipfile

which the remote worker can use to set up the environment. ### Deployment Details: - Environment Configuration: You will need to ensure paths, environment variables, and credentials (if accessing external services) are correctly configured on the remote worker environment. - Dynamic Code Synchronization: If using version control, your deployment process might include scripts to ensure the latest code is pulled and dependencies are up-to-date on the worker, immediately before execution. In summary, deploying to a remote worker does require the code to be accessible to that worker, and the runtime environment (including all dependencies) needs to be prepared on or accessible to the remote setup. This ensures that when the Prefect server triggers a flow run, the worker can seamlessly execute the code as if it were operating on your local environment.

hi @Sergei! you can run your code locally (in another terminal from your

prefect server start

) or if you want to, create a deployment to use a runtime like docker or kubernetes

hi! thanks @Nate for response, i just want to understand, if i have some repo with venv and python files, and remote prefect server. When i deploy some flow from repo, how things are connected?

say I have a prefect server on localhost:4200 (could replace this with

prefect server start

in another terminal)

» docker run -p 4200:4200 -d --rm prefecthq/prefect:3.1.6-python3.12 -- prefect server start --host 0.0.0.0
c7ca9c2087b44abaf4716bf9b7426ab8b261d7e72906676a347540568519cd84

and I have prefect installed so i can set the api url

» uv pip list | rg prefect
prefect                   3.1.6

» prefect config set PREFECT_API_URL=<http://localhost:4200/api>
Set 'PREFECT_API_URL' to '<http://localhost:4200/api>'.
Updated profile 'local'.

» cat ~/.prefect/profiles.toml
active = "local"

[profiles.local]
PREFECT_API_URL = "<http://localhost:4200/api>"

if I have a flow in github, i can fetch it from there and just run it locally

» cat run_flow.py
from prefect import flow


if __name__ == "__main__":
    flow.from_source(
      source="<https://github.com/PrefectHQ/prefect.git>",
      entrypoint="flows/hello_world.py:hello",
    )()

» python run_flow.py
22:41:52.942 | INFO    | prefect.engine - Created flow run 'glistening-crayfish' for flow 'hello'
22:41:52.944 | INFO    | prefect.engine - View at <http://localhost:4200/runs/flow-run/b66020c0-94d2-496a-b992-3633e3e2f465>
22:41:52.986 | INFO    | Flow run 'glistening-crayfish' - Hello world!
22:41:53.018 | INFO    | Flow run 'glistening-crayfish' - Finished in state Completed()

or I can fetch and deploy (as long as a i create a work pool which determines the infra the code should run on)

» cat deploy.py
from prefect import flow

if __name__ == "__main__":
    flow.from_source(
      source="<https://github.com/PrefectHQ/prefect.git>",
      entrypoint="flows/hello_world.py:hello",
    ).deploy(name="hello-world", work_pool_name="local-pool")

» python deploy.py
Looks like you're deploying to a process work pool. If you're creating a deployment for local development, calling `.serve` on
your flow is a great way to get started. See the documentation for more information:
<https://docs.prefect.io/latest/deploy/run-flows-in-local-processes>  Set `ignore_warnings=True` to suppress this message.
Successfully created/updated all deployments!

               Deployments
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ Name              ┃ Status  ┃ Details ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ hello/hello-world │ applied │         │
└───────────────────┴─────────┴─────────┘

To execute flow runs from this deployment, start a worker in a separate terminal that pulls work from the 'local-pool' work pool:

        $ prefect worker start --pool 'local-pool'

To schedule a run for this deployment, use the following command:

        $ prefect deployment run 'hello/hello-world'


You can also run your flow via the Prefect UI: <http://localhost:4200/deployments/deployment/f5f233b8-d36f-472e-ba00-9b44d8270e51>

» prefect deployment run 'hello/hello-world'
Creating flow run for deployment 'hello/hello-world'...
Created flow run 'tangerine-kingfisher'.
└── UUID: d64cbb2c-8916-40a3-af29-cd15dc34e076
└── Parameters: {}
└── Job Variables: {}
└── Scheduled start time: 2024-12-11 22:38:06 CST (now)
└── URL: <http://localhost:4200/runs/flow-run/d64cbb2c-8916-40a3-af29-cd15dc34e076>

now that i scheduled a run, I can tell a worker to

--run-once

for demonstration, but normally you'd omit this to keep the worker on and have it execute all scheduled runs as a subprocess

» prefect worker start -p local-pool --run-once
Discovered type 'process' for work pool 'local'.
Worker 'ProcessWorker 1e8bc82d-f9d8-45ca-90ee-30b2d2b64aaa' started!
22:38:16.341 | INFO    | prefect.flow_runs.worker - Worker 'ProcessWorker 1e8bc82d-f9d8-45ca-90ee-30b2d2b64aaa' submitting flow run 'd64cbb2c-8916-40a3-af29-cd15dc34e076'
22:38:16.449 | INFO    | prefect.flow_runs.worker - Opening process...
22:38:16.480 | INFO    | prefect.flow_runs.worker - Completed submission of flow run 'd64cbb2c-8916-40a3-af29-cd15dc34e076'
22:38:19.576 | INFO    | prefect.deployment - Cloned repository '<https://github.com/PrefectHQ/prefect.git>' into 'prefect'
22:38:19.679 | INFO    | Flow run 'tangerine-kingfisher' - Hello world!
22:38:19.710 | INFO    | Flow run 'tangerine-kingfisher' - Finished in state Completed()
 > Running git_clone step...
22:38:20.138 | INFO    | prefect.flow_runs.worker - Process 91854 exited cleanly.
Worker 'ProcessWorker 1e8bc82d-f9d8-45ca-90ee-30b2d2b64aaa' stopped!
π tmp 3.12.7 ። /tmp

thanks a lot, appreciate it!

yes! your python dependencies must be installed where your flow runs