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<@ULVA73B9P> How do I run Prefect within a Docker ...
# ask-marvin
s
@Marvin How do I run Prefect within a Docker container that is running FastAPI? I have a post and get. How do I deploy this with prefect locally? Should I have a python file that is pointing to the docker image and have flows in it, and deploy the said Docker file? Please give detailed examples of implementation if possible.
m
To run Prefect within a Docker container that also runs FastAPI, you can follow these general steps. This approach assumes you're using Prefect 3.x and involves integrating Prefect tasks with FastAPI endpoints. ### Step-by-Step Guide 1. Create a Prefect Task: Define a Prefect task that you want to trigger via FastAPI.
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python
   from prefect import task

   @task(log_prints=True)
   def my_fastapi_task(name: str):
       print(f"Hello, {name}!")
2. Set Up FastAPI: Create a FastAPI app with POST and GET endpoints.
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python
   from fastapi import FastAPI
   from prefect import flow

   app = FastAPI()

   @app.post("/run-task/")
   async def run_task(name: str):
       my_fastapi_task.submit(name)
       return {"message": f"Task for {name} submitted"}

   @app.get("/status/")
   async def status():
       return {"status": "Running"}
3. Dockerize Your Application: Create a Dockerfile to containerize your FastAPI and Prefect setup.
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dockerfile
   FROM python:3.9

   WORKDIR /app

   COPY . /app

   RUN pip install fastapi uvicorn prefect

   CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
4. Build and Run the Docker Container: Use Docker commands to build the image and run the container.
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bash
   docker build -t my-fastapi-prefect-app .
   docker run -p 8000:8000 my-fastapi-prefect-app
5. Access Your API: You can now access your API at 
<http://localhost:8000>
. Use the POST endpoint to trigger Prefect tasks. - POST to 
/run-task/
with a JSON body like 
{"name": "World"}
to submit a task. - GET from 
/status/
to check the status of your FastAPI service. ### Additional Resources - Prefect in Docker: Configure and manage Prefect with Docker. - Prefect and FastAPI Integration Example: A GitHub repository with examples of integrating Prefect tasks with FastAPI. This setup allows you to deploy Prefect flows locally within a Docker container that serves FastAPI endpoints, aligning with your preference for an imperative programming style.
s
Thank you. @Marvin How would I go about deploying this and in prefect and can't I just create a flow or task within the Fast API setup like before the async def run_task in your example?
m
Certainly, you can define and deploy a Prefect flow directly within your FastAPI setup. This approach ensures that your task orchestration is tightly integrated with your API logic. Here's how you can do it: ### Define a Flow within FastAPI You can define a Prefect flow inside your FastAPI application, just before the endpoint definitions. This allows you to orchestrate multiple tasks or add more complex logic if needed.
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python
from fastapi import FastAPI
from prefect import flow, task

app = FastAPI()

# Define a task
@task(log_prints=True)
def my_fastapi_task(name: str):
    print(f"Hello, {name}!")

# Define a flow that includes the task
@flow
def my_flow(name: str):
    my_fastapi_task(name)

# FastAPI endpoint to trigger the flow
@app.post("/run-task/")
async def run_task(name: str):
    my_flow(name)
    return {"message": f"Flow for {name} executed"}

@app.get("/status/")
async def status():
    return {"status": "Running"}
### Deploying Locally 1. Run Locally: You can run this setup locally without Docker to test it out.
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bash
   uvicorn main:app --reload
2. Dockerize and Deploy: If you want to deploy this within a Docker container, use the Dockerfile setup from the previous message. Build and run your Docker container as before. 3. Access the API: With your API running, you can POST to 
/run-task/
with a JSON body like 
{"name": "World"}
to execute the flow. ### Deployment with Prefect For more advanced deployment options, consider using Prefect's orchestration capabilities: - Prefect Cloud or Server: Deploy your flows to Prefect Cloud or a self-hosted Prefect Server for better management and monitoring. - Work Pools and Agents: Use Prefect's work pools and agents to manage execution environments. This approach allows you to maintain a clean separation between your API logic and task orchestration while still keeping everything in one place for simplicity. You can explore more about Prefect's deployment options in their documentation.
s
How would I deploy this in prefect I mean?
@Marvin How would I deploy this in prefect I mean?
m
To deploy your Prefect flow, especially when integrating it with FastAPI, you can follow these steps. This method leverages Prefect's deployment capabilities to manage and execute your flows efficiently. ### Steps to Deploy a Prefect Flow 1. Create a 
prefect.yaml
File: This file defines your deployment configuration. Place it in the root directory of your project.
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yaml
   version: 1
   deployments:
     - name: my-fastapi-flow
       flow: main.py:my_flow  # Path to your flow function
       schedule: null  # Add a schedule if needed
       tags: []
       infra_overrides: {}  # Override infrastructure settings if necessary
2. Deploy the Flow: Use the Prefect CLI to deploy your flow.
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bash
   prefect deploy
This command will read the 
prefect.yaml
file, build the deployment, and register it with Prefect. 3. Set Up a Work Pool: Prefect uses work pools to manage execution environments. You can set up a work pool that suits your infrastructure needs. - Static Infrastructure: For environments where infrastructure remains constant. - Dynamic Infrastructure: For environments that scale horizontally. More details on work pools can be found here. 4. Start a Worker: If your deployment type requires it, start a Prefect worker to execute the flows.
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bash
   prefect worker start --pool my-work-pool
Workers are responsible for checking scheduled flow runs and executing them. 5. Monitor and Manage: Use the Prefect UI or CLI to monitor and manage your flow runs. Prefect Cloud or a self-hosted Prefect Server can provide more advanced monitoring capabilities. ### Additional Resources - Prefect Deployment Guide: Comprehensive guide on deploying flows with Prefect. - Prefect YAML Configuration: Details on configuring your deployment using 
prefect.yaml
. This setup allows you to deploy and manage your Prefect flows effectively, whether they're running locally or on remote infrastructure.
n
hi @Shamly Mackey - you don't necessary need to deploy your prefect flows unless you need separate infra for them for example, this app (@Marvin) its just a fastapi app with a 
POST /chat
endpoint that 
asyncio.create_task(handle_app_mention)
where 
handle_app_mention
is a prefect flow i use 
asyncio
to background since slack needs a response in less than 3 seconds but it might take the LLM longer to call tools / make a response https://github.com/PrefectHQ/marvin/tree/main/cookbook/slackbot
s
Thank you very much, @Nate! @Marvin I don't plan on using LLMs, but what I am trying to accomplish is a pretty simple flow and task which runs within a docker container. However, I am unsure on the exact implementation process itself. From my understanding, I can have a flow and task decorators above the def functions on FastAPI, and then might need to run a deployment by specifying the app.py in prefect? Sorry, not entirely sure if my question makes sense, but open to any suggestions and/or comments! Thank you for your response!
n
that's ok, it doesn't matter if your python code wrapped in 
@flow
or 
@task
is doing LLM stuff or not, you can use the same pattern the big decision I'm suggesting you need to make is whether or not you want your flows/tasks to run on independent infra (e.g. a separate docker container from your fastapi app) if not, you can just do something like this
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@flow
def my_prefect_flow(...): ...

@app.post('/whatever')
def handler(...):
   my_prefect_flow(...)

   # or if you want avoid blocking the endpoint until the flow ends

   asyncio.create_task(my_prefect_flow(...))
if you do, then you can replace either of those lines with 
run_deployment()
to instead trigger some remote deployment from your endpoint (instead of running it in the same process)
šŸ™ 1
šŸ™Œ 1
s
Oh that makes sense! I appreciate you taking time to explain things! Have been stumped on this for a while. I will try to implement as such. I was thinking of not using a separate infra, but is there a standard practice in the industry that you suggest or recommend? Would you happen to have an example repo that has a demo of said example? Thank you again, very much, @Nate!
n
I was thinking of not using a separate infra, but is there a standard practice in the industry that you suggest or recommend?
i would say there's no one size fits all way to think about this, but if your flows have serious resource requirements (ie lots of memory or cpu) then it may make sense to define independent deployments for them so you can tweak 
job_variables
to allocate extra resources to that work independent of your web app
but in the case of @Marvin the background work is not very heavy so in that case it totally works out to just background the work in the same process
m
If your background work is lightweight and can be handled within the same process, you can simplify your setup by running Prefect tasks directly within your FastAPI application without the need for a separate deployment or worker process. This approach keeps everything in a single process and is suitable for less resource-intensive tasks. Here's how you can achieve this: ### Simplified Setup for Lightweight Background Tasks 1. Define Tasks and Flow: Define your tasks and flow within the FastAPI application.
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python
   from fastapi import FastAPI, BackgroundTasks
   from prefect import flow, task

   app = FastAPI()

   @task(log_prints=True)
   def my_fastapi_task(name: str):
       print(f"Hello, {name}!")

   @flow
   def my_flow(name: str):
       my_fastapi_task(name)

   @app.post("/run-task/")
   async def run_task(name: str, background_tasks: BackgroundTasks):
       background_tasks.add_task(my_flow, name)
       return {"message": f"Flow for {name} is running in the background"}

   @app.get("/status/")
   async def status():
       return {"status": "Running"}
2. Run Locally: Simply run your FastAPI application locally.
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bash
   uvicorn main:app --reload
3. Access the API: Use the POST endpoint to trigger tasks and flows in the background. - POST to 
/run-task/
with a JSON body like 
{"name": "World"}
. ### Explanation - BackgroundTasks: FastAPI's 
BackgroundTasks
is used to run tasks in the background without blocking the main thread. This is ideal for lightweight tasks that don't require heavy computation or long-running processes. - Single Process: Everything runs within a single FastAPI process, reducing complexity and overhead. This approach is perfect for scenarios where the task is not resource-intensive and can run alongside your API server without affecting performance.
n
or yea i forgot you can use starlette 
BackgroundTasks
instead of 
asyncio
if you want, but its mostly the same thing šŸ™‚
s
Thank you very very much! So I have a prefect worker called 
docker-pool
as docker type, set up on my machine. Would I need to run a script similar to the code block below for the worker to detect my script?
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from prefect import flow
from prefect.docker import DockerImage


@flow(log_prints=True)
def buy():
    print("Buying securities")


if __name__ == "__main__":
    buy.deploy(
        name="my-custom-dockerfile-deployment",
        work_pool_name="my-docker-pool",
        image=DockerImage(
            name="my_image",
            tag="deploy-guide",
            dockerfile="Dockerfile"
    ),
    push=False
)
Sorry, I *am very new to orchestration tools and Prefect is the first tool I am learning and trying to implement for a MLOps pipeline that I am trying to implement šŸ˜….
n
no worries! i would check out ā€¢ this starter yt playlist ā€¢ this template repo you can start from
s
Thank you Nate!
n
but essentially the pattern is ā€¢ write your code ā€¢ create a work pool ā€¢ deploy your code (via 
.deploy()
or 
prefect deploy
) which assigns a work pool (infra config) to your code ā€¢ start a worker: worker submits scheduled runs according to the work pool
šŸ™ 1
s
Thank you! I will use the info you provided to continue. Hope you have a great day!
catjam 1
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