@Marvin i'm using this code from the docs

async def cancel_flow_runs(flow_runs):
    async with get_client() as client:
        for idx, flow_run in enumerate(flow_runs):
            print(f"[{idx + 1}] Cancelling flow run '{flow_run.name}' with ID '{flow_run.id}'")
            state_updates = {}
            state_updates.setdefault("name", "Cancelled")
            state_updates.setdefault("type", StateType.CANCELLED)
            state = flow_run.state.copy(update=state_updates)
            await client.set_flow_run_state(flow_run.id, state, force=True)

but it fails with

TypeError: 'MockValSer' object cannot be converted to 'SchemaSerializer'

. why?

The error you're encountering,

TypeError: 'MockValSer' object cannot be converted to 'SchemaSerializer'

, appears to be related to a serialization issue in Prefect. This type of error has been reported in various contexts, particularly when using certain task runners like

DaskTaskRunner

, as seen in this GitHub issue. Here are a few steps you might consider to resolve or work around the issue: 1. Check Prefect Version: Ensure that you are using the latest version of Prefect, as serialization issues might have been addressed in more recent updates. You can check the latest release notes for any fixes related to serialization. 2. Review Task Runner: If you are using a specific task runner, try running your code with a different runner or without a task runner to see if the issue persists. This can help isolate whether the problem is specific to a certain runner. 3. Inspect Serialization: The error suggests a problem with converting objects during serialization. Make sure that any custom objects or complex data structures in your flow are serializable. If necessary, simplify the data being passed around. 4. Consult Documentation: Review the Prefect documentation on cancellation and serializers to ensure that your implementation aligns with recommended practices. If the problem persists, you might want to report it on the Prefect GitHub repository or seek further assistance from the Prefect community.

@Marvin are there any other ways to cancel flows in python

To cancel flow runs in Prefect using Python, you can use the

set_flow_run_state

method from the Prefect client, as you have already attempted. However, there are other ways to cancel flows: 1. Command Line Interface (CLI): You can cancel a flow run using the Prefect CLI by executing the command:

bash
   prefect flow-run cancel <flow-run-id>

This requires the flow run ID and can be executed from your terminal. 2. Prefect UI: You can navigate to the Prefect UI, find the flow run you wish to cancel, and use the interface to cancel it directly. 3. Python Script: If you prefer a more programmatic approach, you can use the Prefect API to interact with flow runs. This involves using the Prefect client to change the state of the flow run to "Cancelled". For more detailed guidance, you can refer to the Prefect documentation on cancellation.

@Marvin how could i change my original python function so that it works?

To address the issue you're facing with the original function, you can try using the

PrefectClient

class directly to manage the flow run state. Here's a revised version of your function:

python
from prefect.client.orchestration import PrefectClient
from prefect.client.schemas import StateType

async def cancel_flow_runs(flow_runs):
    async with PrefectClient() as client:
        for idx, flow_run in enumerate(flow_runs):
            print(f"[{idx + 1}] Cancelling flow run '{flow_run.name}' with ID '{flow_run.id}'")
            state_updates = {
                "name": "Cancelled",
                "type": StateType.CANCELLED
            }
            state = flow_run.state.copy(update=state_updates)
            await client.set_flow_run_state(flow_run.id, state, force=True)

Make sure that

PrefectClient

and

StateType

are correctly imported from the Prefect library. This code should help manage the cancellation of flow runs by setting their state to "Cancelled". If the issue persists, double-check the flow run object and its state to ensure they are compatible with the Prefect client's expectations.

@Marvin and what type should

flow_runs

be

The

flow_runs

parameter in your function should be a list of

FlowRun

objects. The

FlowRun

class is part of the

prefect.client.schemas.objects

module. Each

FlowRun

object should have properties like

id

,

name

, and

state

, which are used in your function to update and manage the flow run's state. Ensure that you are creating or retrieving these

FlowRun

objects correctly before passing them to your

cancel_flow_runs

function.

@Marvin i'm using version 3.1.4. Will that work with the code you've given me

The code I provided should work with Prefect version 3.1.4, as it mainly relies on the

PrefectClient

and

StateType

classes, which are core parts of the Prefect library. However, if you encounter any issues, consider updating to the latest version to benefit from recent bug fixes and enhancements. You can review the full changelog here for more details on recent changes.

@Marvin i get

Traceback (most recent call last):
  File "/Users/arthurhussey/orbital-git/orb/libs/chem/prefect_script.py", line 59, in <module>
    asyncio.run(cancel_flow_runs(flows_with_pending_subflows))
  File "/Users/arthurhussey/miniconda3/envs/core/lib/python3.11/asyncio/runners.py", line 190, in run
    return runner.run(main)
           ^^^^^^^^^^^^^^^^
  File "/Users/arthurhussey/miniconda3/envs/core/lib/python3.11/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/arthurhussey/miniconda3/envs/core/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete
    return future.result()
           ^^^^^^^^^^^^^^^
  File "/Users/arthurhussey/orbital-git/orb/libs/chem/prefect_script.py", line 51, in cancel_flow_runs
    await client.set_flow_run_state(flow_run.id, state, force=True)
  File "/Users/arthurhussey/miniconda3/envs/core/lib/python3.11/site-packages/prefect/client/orchestration.py", line 2180, in set_flow_run_state
    state=state_create.model_dump(mode="json", serialize_as_any=True),
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/arthurhussey/miniconda3/envs/core/lib/python3.11/site-packages/pydantic/main.py", line 426, in model_dump
    return self.__pydantic_serializer__.to_python(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: 'MockValSer' object cannot be converted to 'SchemaSerializer'

Your message was too long by 108 tokens...

@Marvin i get

, in set_flow_run_state
    state=state_create.model_dump(mode="json", serialize_as_any=True),
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/arthurhussey/miniconda3/envs/core/lib/python3.11/site-packages/pydantic/main.py", line 426, in model_dump
    return self.__pydantic_serializer__.to_python(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: 'MockValSer' object cannot be converted to 'SchemaSerializer'

The error you're encountering,

'MockValSer' object cannot be converted to 'SchemaSerializer'

, is likely related to the serialization changes in Prefect 3.x, which uses Pydantic 2. Prefect 3.x introduced significant changes in how data is serialized, and existing data from Prefect 2.x might not be compatible. Here are some steps to address this issue: 1. Ensure Compatibility: Double-check that all objects being passed to the

set_flow_run_state

method are compatible with the current Prefect 3.x serialization requirements. This might involve updating how these objects are created or manipulated. 2. Update Prefect: Consider updating to the latest version of Prefect, as newer versions might have addressed serialization issues or provided better error handling. 3. Debug Serialization: If the issue persists, try to identify which specific object or state is causing the serialization failure. You might need to manually inspect or log the state before passing it to

set_flow_run_state

. 4. Consult Prefect Documentation: Look for any additional guidance or updates in the Prefect documentation regarding serialization and state management. If these steps do not resolve the issue, you may need to reach out to the Prefect community for more specific guidance, especially if this is a recurring issue with certain data structures.

@Marvin what type should state be to be passed to set_flow_run_state

The

state

that you pass to the

set_flow_run_state

method should be an instance of the

State

class from the

prefect.client.schemas.objects

module. This class is designed to represent the state of a flow run, and you'll need to ensure that it is correctly instantiated and populated with the necessary attributes before passing it to the method. Make sure that any updates or manipulations to the state object comply with the serialization requirements of Prefect 3.x. If you continue to face issues, it might be beneficial to create a new

State

object rather than copying and updating an existing one.

@Marvin what version of pydantic do i need

Prefect 3.x uses Pydantic 2. Ensure that your environment is set up with Pydantic version 2.x to maintain compatibility with Prefect's serialization requirements. You can install or update Pydantic using pip:

bash
pip install pydantic==2.*

This should help in aligning the serialization processes with Prefect's expectations.

@Marvin if i do

print(type(state))
            print(state.to_state_create().model_dump(mode="json", serialize_as_any=True))

the ouptout is

<class 'prefect.client.schemas.objects.State'>

and then the failure message. Why is this wrong?

The

State

object you are working with should be converted to a

StateCreate

object before performing operations like

model_dump

. The

StateCreate

class is part of the

prefect.client.schemas.actions

module and is designed for creating new state representations. Here's a potential adjustment you can make: 1. Ensure that you have a

StateCreate

instance. If you are converting a

State

object, make sure you are using the correct method to obtain a

StateCreate

instance. 2. Use the

StateCreate

class directly, if needed, to ensure proper serialization. If the

to_state_create()

method is supposed to convert your

State

object to a

StateCreate

object but fails, verify that the conversion logic is correct and aligns with Prefect's expectations for state transitions.

@Marvin i can see in the code that you do that def set_flow_run_state( self, flow_run_id: UUID, state: "prefect.states.State[T]", force: bool = False, ) -> OrchestrationResult[T]: """ Set the state of a flow run. Args: flow_run_id: the id of the flow run state: the state to set force: if True, disregard orchestration logic when setting the state, forcing the Prefect API to accept the state Returns: an OrchestrationResult model representation of state orchestration output """ state_create = state.to_state_create() state_create.state_details.flow_run_id = flow_run_id state_create.state_details.transition_id = uuid4() try: response = self._client.post( f"/flow_runs/{flow_run_id}/set_state", json=dict( state=state_create.model_dump(mode="json", serialize_as_any=True), force=force, ), ) except httpx.HTTPStatusError as e: if e.response.status_code == status.HTTP_404_NOT_FOUND: raise prefect.exceptions.ObjectNotFound(http_exc=e) from e else: raise result: OrchestrationResult[T] = OrchestrationResult.model_validate( response.json() ) return result

Your message was too long by 7 tokens...

@Marvin this is the func def set_flow_run_state( self, flow_run_id: UUID, state: "prefect.states.State[T]", force: bool = False, ) -> OrchestrationResult[T]: """ Set the state of a flow run. Args: flow_run_id: the id of the flow run state: the state to set force: if True, disregard orchestration logic when setting the state, forcing the Prefect API to accept the state Returns: an OrchestrationResult model representation of state orchestration output """ state_create = state.to_state_create() state_create.state_details.flow_run_id = flow_run_id state_create.state_details.transition_id = uuid4() try: response = self._client.post( f"/flow_runs/{flow_run_id}/set_state", json=dict( state=state_create.model_dump(mode="json", serialize_as_any=True), force=force, ), ) except httpx.HTTPStatusError as e: if e.response.status_code == status.HTTP_404_NOT_FOUND: raise prefect.exceptions.ObjectNotFound(http_exc=e) from e else: raise result: OrchestrationResult[T] = OrchestrationResult.model_validate( response.json() ) return result

Your message was too long by 1 tokens...

@Marvin the func: def set_flow_run_state( self, flow_run_id: UUID, state: "prefect.states.State[T]", force: bool = False, ) -> OrchestrationResult[T]: """ Set the state of a flow run. Args: flow_run_id: the id of the flow run state: the state to set force: if True, disregard orchestration logic when setting the state, forcing the Prefect API to accept the state Returns: an OrchestrationResult model representation of state orchestration output """ state_create = state.to_state_create() state_create.state_details.flow_run_id = flow_run_id state_create.state_details.transition_id = uuid4() try: response = self._client.post( f"/flow_runs/{flow_run_id}/set_state", json=dict( state=state_create.model_dump(mode="json", serialize_as_any=True), force=force, ), ) except httpx.HTTPStatusError as e: if e.response.status_code == status.HTTP_404_NOT_FOUND: raise prefect.exceptions.ObjectNotFound(http_exc=e) from e else: raise result: OrchestrationResult[T] = OrchestrationResult.model_validate( response.json() ) return result

The function

set_flow_run_state

you've provided is designed to set the state of a flow run using a

State

object. The key part of this function is the conversion of the

State

object to a

StateCreate

object using

state.to_state_create()

. This conversion is necessary because the API expects a

StateCreate

representation when setting the state. If you are encountering an error during this conversion, it might be due to: 1. Incorrect State Object: Ensure that the

State

object you are using is correctly initialized and contains all necessary information before conversion. 2. Pydantic Serialization: The error might stem from Pydantic's serialization process. Make sure your environment is using Pydantic 2.x, as required by Prefect 3.x. 3. State Details: Check that the

state_details

attribute within your

State

object is properly populated, especially

flow_run_id

and

transition_id

. Given the function's reliance on

state.to_state_create()

, double-check that this method is implemented correctly in your

State

class, and ensure that all necessary fields are being set before calling

model_dump

. If the problem persists, it may be beneficial to debug by logging the state object before and after conversion to identify any discrepancies in expected attributes or data types.