@Marvin Why do i randomly get this message when running scheduled flow in Kubernetes

Flow could not be retrieved from deployment.
Traceback (most recent call last):
  File "/usr/local/lib/python3.9/site-packages/prefect/engine.py", line 246, in retrieve_flow_then_begin_flow_run
    flow = await load_flow_from_flow_run(flow_run, client=client)
  File "/usr/local/lib/python3.9/site-packages/prefect/client.py", line 105, in with_injected_client
    return await fn(*args, **kwargs)
  File "/usr/local/lib/python3.9/site-packages/prefect/deployments.py", line 50, in load_flow_from_flow_run
    basepath = deployment.path or Path(deployment.manifest_path).parent
  File "/usr/local/lib/python3.9/pathlib.py", line 1082, in __new__
    self = cls._from_parts(args, init=False)
  File "/usr/local/lib/python3.9/pathlib.py", line 707, in _from_parts
    drv, root, parts = self._parse_args(args)
  File "/usr/local/lib/python3.9/pathlib.py", line 691, in _parse_args
    a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType

The error you're encountering, "`Flow could not be retrieved from deployment`," typically suggests there's an issue in retrieving the flow's path or manifest during execution. It's possible that this is a result of a missing or improperly defined path in your deployment configuration, causing the TypeError when the code attempts to access a path attribute that is not set (i.e., it's

None

). This traceback suggests that the issue might originate from the configuration of your deployment. Here are a few steps you can take to troubleshoot and resolve this issue: 1. Check Deployment Configuration: Ensure that your deployment configuration specifies the correct paths. Specifically, confirm that the

path

or

manifest_path

attributes in your deployment are correctly defined. 2. Prefect Version Consideration: Since you're running Prefect on Kubernetes and dealing with deployment issues, make sure to use configuration settings that are compatible with the Prefect version you are using. Prefect 2.x and 3.x can have different ways of defining deployments. 3. GitHub Issues Check: Unfortunately, there seems to be no directly related open issue in the Prefect GitHub repository about this specific error. However, if the problem persists, consider searching for or opening an issue there to get additional community or official support. Since the documentation search didn't provide further insight, if you need a detailed explanation of how to configure a deployment in Prefect, I recommend checking the official Prefect documentation for your specific version and confirm any environmental configurations are correctly set up: - Prefect 2.x: https://docs.prefect.io/2.x/ - Prefect 3.x: https://docs.prefect.io/3.x/ If you have information about your Prefect version or specific deployment snippets, I could further tailor the assistance to solve the problem.

@Marvin can you provide an example deployment yaml file

Here's an example of a deployment YAML file for a Prefect flow. This file configures additional settings needed to create a deployment on the server. This example assumes you're using Prefect 2.x documentation references:

yaml
name: catfact
description: A deployment for Cat Facts flow
version: c0fc95308d8137c50d2da51af138aa23

# Specifies the work queue that will handle this deployment's runs
work_queue_name: test

tags: []
parameters: {}
schedule: null
infra_overrides: {}

infrastructure:
  type: process
  env: {}
  labels: {}
  name: null
  command:
    - python
    - -m
    - prefect.engine
  stream_output: true

# DO NOT EDIT BELOW THIS LINE
flow_name: Cat Facts
manifest_path: null
storage: null
path: /Users/terry/test/testflows/catfact
entrypoint: catfact.py:catfacts_flow

parameter_openapi_schema:
  title: Parameters
  type: object
  properties:
    url:
      title: url
      type: string
  required:
    - url

### Key Components: - name: The name of the deployment. - work_queue_name: The work queue for handling the deployment's runs. - infrastructure: Configuration for how the flow will be run, such as the command to execute it. - flow_name: The name of the flow associated with this deployment. - path: The local path or directory where the flow script is located. - entrypoint: Specifies the flow's Python script and function. This file can be customized further based on your specific settings, such as adding schedules or parameters. You can learn more about configuring deployment YAML files in the Prefect documentation here.

@Marvin Provide a Prefect 3 example

Here's an example of a deployment YAML file for Prefect 3.x, which supports multiple deployment declarations within a single

prefect.yaml

file. This allows you to manage configurations for different deployments within the same file:

yaml
# Define deployments with YAML
build:
  - prefect_docker.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.3.1
      id: build-image
      dockerfile: Dockerfile
      image_name: my-docker-image
      tag: latest

deployments:
  - name: deployment-1
    entrypoint: flows/hello.py:my_flow
    parameters:
      number: 42
      message: "Don't panic!"
    work_pool:
      name: my-process-work-pool
      work_queue_name: primary-queue

  - name: deployment-2
    entrypoint: flows/goodbye.py:goodbye_flow
    parameters:
      farewell_message: "Goodbye!"
    work_pool:
      name: my-docker-work-pool
      work_queue_name: secondary-queue

definitions:
  work_pools:
    my_docker_work_pool: &my_docker_work_pool
      name: my-docker-work-pool
      work_queue_name: default
      job_variables:
        image: "{{ build-image.image }}"

### Key Components: - build: Steps required to build your deployment, such as building a Docker image. - deployments: A list of deployments with unique names. Each deployment specifies its entrypoint, parameters, and work pool settings. - definitions: Contains reusable YAML aliases for configurations like work pools. This Prefect 3.x deployment YAML file demonstrates the flexibility to manage multiple deployments with different settings while sharing configurations through aliases. You can find more details in the Prefect 3.x documentation.

@Marvin in your example where are you setting

path

or

manifest_path?

In Prefect 3.x, the

path

or

manifest_path

is not explicitly required in the deployment YAML configuration as it was typically used in previous versions for certain kinds of deployments or when specifying storage options directly. These paths were used to define where the flow's code or manifest file was located. In the Prefect 3.x configuration example I provided, the

entrypoint

field effectively handles the role of pointing to the flow's code location. It tells the deployment where to find the Python script and the specific flow function within that script. If you need to specify a path for storage or other purposes in Prefect 3.x, you generally manage those details through infrastructure blocks or when defining custom storage blocks rather than setting them directly in the deployment YAML. Here's a more detailed explanation of

entrypoint

and paths in Prefect 3.x: - Entry Point: Defines the relative path to your flow script and the function that defines your flow. This is usually sufficient to locate where your flow's logic lives and how it should be executed. If you require more explicit control over the storage of your flows or need to specify additional paths, you can manage that through Prefect's integrations with various storage backends or environment configurations. However, if you have a specific use case or environment setup that requires explicit pathing, additional YAML configurations or blocks might be required. For further details, consider reviewing the Prefect 3.x Deployment Guide where storage and paths are further explained in the context of your specific deployment needs.