@Marvin how do i query deployments created from the prefect client?

Here's how you can query deployments using the Prefect client in Prefect 2.x: ### Using the Prefect Client 1. Install Prefect (if you haven't already):

sh
    pip install prefect

2. Create and Query Deployments: You can use the Prefect client to create and query deployments. Here's an example snippet:

python
    from prefect.client import get_client
    
    async def get_deployments():
        async with get_client() as client:
            # Replace "your_flow_name" with the name of your flow
            deployments = await client.read_deployments(flow_name="your_flow_name")
            return deployments
    
    # Example usage
    import asyncio
    deployments = asyncio.run(get_deployments())
    for deployment in deployments:
        print(deployment)

### Documentation Links 1. Deployments and Flows 2. Prefect Deployment Tutorial 3. Prefect Deploy Guide ### Related GitHub Issues Searching through GitHub issues may also offer additional insights and related discussions: - Support subflow cancellation if parent flow is in terminal state - Unable to handle 'Cancelling' events Feel free to delve into the linked resources for a deeper understanding. If you have further questions or issues, don't hesitate to ask!

@Marvin help me update this mock workpool for use in my tests, i need a mock valid job_configuration

async def _create_prefect_workpools():
    async with get_client() as client:
        await client.create_work_pool(
            WorkPoolCreate(
                name=infra.PrefectWorkpool.CPU.value,
                type="kubernetes",
                base_job_template={
                    "job_configuration": {},
                    "variables": {
                        "properties": {
                            "image": constants.DEFAULT_PREFECT_WORKFLOW_IMAGE
                        }
                    },
                },
            )
        )

To help you update your mock work pool with a valid job configuration for use in your tests, here's the modified code. The job configuration includes placeholders that reference values provided in the variables section. The

base_job_template

defines fields that can be edited on a per-deployment basis or for the entire work pool. ### Updated Code

python
from prefect.client import get_client
from prefect.workers import WorkPoolCreate
import constants
import infra

async def _create_prefect_workpools():
    async with get_client() as client:
        await client.create_work_pool(
            WorkPoolCreate(
                name=infra.PrefectWorkpool.CPU.value,
                type="kubernetes",
                base_job_template={
                    "job_configuration": {
                        "container": {
                            "image": "{{ image }}",
                            "resources": {
                                "limits": {
                                    "cpu": "{{ cpu }}",
                                    "memory": "{{ memory }}"
                                }
                            },
                            "env": {
                                "PREFECT_API_URL": "{{ api_url }}",
                                "PREFECT_LOGGING_LEVEL": "{{ logging_level }}"
                            }
                        }
                    },
                    "variables": {
                        "properties": {
                            "image": {
                                "default": constants.DEFAULT_PREFECT_WORKFLOW_IMAGE,
                                "type": "string"
                            },
                            "cpu": {
                                "default": 500,
                                "type": "integer"
                            },
                            "memory": {
                                "default": 1024,
                                "type": "integer"
                            },
                            "api_url": {
                                "default": "<http://api.prefect.io>",
                                "type": "string"
                            },
                            "logging_level": {
                                "default": "INFO",
                                "type": "string"
                            }
                        }
                    }
                },
            )
        )

# Example constants and infra definitions
class constants:
    DEFAULT_PREFECT_WORKFLOW_IMAGE = "prefecthq/prefect:2.0.0"

class infra:
    class PrefectWorkpool:
        CPU = "cpu-workpool"

### Explanation 1. Job Configuration: The

job_configuration

section includes placeholders (

{{ image }}

,

{{ cpu }}

,

{{ memory }}

,

{{ api_url }}

,

{{ logging_level }}

) which are referenced from the

variables

section. This allows you to customize job configurations based on the deployment. 2. Variables: This section defines the default values and types for each configurable variable. These variables can be referenced in the job configuration using double curly braces. ### Documentation Links - Prefect Work Pools Concept - Developing a New Worker Type Feel free to adjust the configuration as needed for your specific testing requirements. If you have any further questions or run into issues, let me know!