@Marvin Hi, I'm deploying a kubernetes version prefect flow in version 3.2.0, currently I got an error

Downloading flow code from storage at None

, I do put my flow code in docker, container path is

/opt/prefect/prefect

, how to set storage path properly?

To resolve the error regarding downloading flow code from storage in your Kubernetes deployment, you need to ensure that your flow code is properly stored and referenced. Since you're using Docker for storing your flow code, here's a concise way to set it up: 1. Docker Image-Based Storage: Prefect supports storing flow code directly in a Docker image. Make sure your Docker image includes your flow code at the specified path (

/opt/prefect/prefect

). 2. Setting the Storage Path: - If you're using a Python script to deploy, you can specify the storage location using the

from_source

method, though this is more common with non-Docker storage. - For Docker, ensure your Dockerfile correctly copies your flow code into the image. Here's a basic example:

dockerfile
     FROM prefecthq/prefect:3.2.0
     COPY /path/to/your/flow /opt/prefect/prefect

3. Deployment Configuration: - If using a

prefect.yaml

, you don't typically need to specify a storage path for Docker images. Instead, ensure your deployment references the correct Docker image. 4. Common Gotchas: - Ensure your Docker image is available to your Kubernetes cluster, either by pushing it to a container registry or by making it accessible locally if running locally. - Remember that in Prefect 3.x, agents have been replaced by workers, and work pools replace infrastructure blocks. For more details, you can refer to the Prefect documentation on storage and Docker deployment examples. If you have further questions or need a more specific setup, feel free to ask!

@Marvin yes, I'm using python script to deploy the flow, this is how I use

mf1 = main_flow.deploy(

name="mainflow", version="1.0", parameters={}, work_pool_name="k8s", enforce_parameter_schema=False, image="registry.agibot.com/ai-platform/hanwen/prefect_test:latest", entrypoint_type="mainflow:main_flow", build=False )

When deploying a flow using a Python script in Prefect 3.x, you need to ensure that the

entrypoint

parameter is correctly specified. This parameter should be a relative reference to your flow function within the Docker image or repository, following Python's standard path-to-object syntax. Here's a refined approach based on your current setup: 1. Entrypoint Specification: - The

entrypoint

should be specified as

<path_to_file>:<flow_function_name>

. If your flow is defined in a file named

mainflow.py

and the flow function is

main_flow

, then your entrypoint should be

"mainflow.py:main_flow"

. 2. Docker Image: - Ensure your Docker image is correctly built with the flow code at the specified path. If the file structure within your Docker image places

mainflow.py

directly in

/opt/prefect/prefect

, your entrypoint should reflect that path. 3. Example Deployment:

python
   mf1 = main_flow.deploy(
       name="mainflow",
       version="1.0",
       parameters={},
       work_pool_name="k8s",
       enforce_parameter_schema=False,
       image="<http://registry.agibot.com/ai-platform/hanwen/prefect_test:latest|registry.agibot.com/ai-platform/hanwen/prefect_test:latest>",
       entrypoint="mainflow.py:main_flow",  # Adjust this according to your file structure
       build=False
   )

4. Check Dockerfile: Ensure your Dockerfile copies the necessary files to the correct location:

dockerfile
   FROM prefecthq/prefect:3.2.0
   COPY ./mainflow.py /opt/prefect/prefect/
   WORKDIR /opt/prefect/prefect

For more details, refer to the Prefect documentation on deploying via Python. If you need further assistance or clarification, feel free to ask!

@Marvin in prefect 3.x, there is no keyword entrypoint in flow.deploy, there is entrypoint_trype instead

It appears there was a misunderstanding regarding the parameters for the

deploy

method in Prefect 3.x. The

deploy

function does not have an

entrypoint_type

parameter. Instead, the function signature includes parameters like

deployments

,

work_pool_name

,

image

,

build

,

push

, and others. Here's a refined approach to deploying your flow using the

deploy

function: 1. Ensure Correct Parameters: The

deploy

function does not have an

entrypoint

or

entrypoint_type

parameter. Instead, you should focus on parameters like

deployments

,

work_pool_name

, and

image

. 2. Check Deployment: Make sure you have defined your deployment correctly as an instance of

RunnerDeployment

or similar, which you can then pass to the

deploy

function. 3. Example Usage:

python
   from prefect.deployments import RunnerDeployment

   # Assuming 'main_flow' is your flow function
   deployment = RunnerDeployment(flow=main_flow)
   deployment.deploy(
       work_pool_name="k8s",
       image="<http://registry.agibot.com/ai-platform/hanwen/prefect_test:latest|registry.agibot.com/ai-platform/hanwen/prefect_test:latest>",
       build=False
   )

4. Entrypoint in Docker: Ensure your Docker image has the flow code correctly placed and that your deployment setup aligns with your flow's location and execution requirements. For more detailed guidance, you might want to explore Prefect's documentation further or check out their GitHub repository for examples and community discussions. If you have more questions or need further clarification, feel free to ask!