Metaflow DeployedFlow Trigger Issue In V2.19.7+

Dec 4, 2025 by Alex Johnson 48 views

Introduction

This article addresses a critical issue encountered in Metaflow versions 2.19.7 and above, specifically concerning the triggering of deployed flows using deploy-time configurations. This bug manifests when a flow employing a deploy-time Config attempts to trigger another flow via DeployedFlow. The error, which did not exist in version 2.19.6 and earlier, results in a flow failure accompanied by an "Options were not properly set" message. This article provides a detailed explanation of the issue, steps to reproduce it, and potential workarounds. Understanding the nuances of Metaflow, especially concerning configurations and flow triggering, is crucial for data scientists and engineers who rely on this framework for building and managing complex workflows. The improper setting of options, as indicated by the error message, can stem from various factors, including how configurations are defined, accessed, and passed between flows. Therefore, a thorough understanding of Metaflow's configuration mechanisms is essential for effective troubleshooting. In subsequent sections, we will delve deeper into the specifics of the error, examining the provided code snippets and dissecting the steps to replicate the problem. By doing so, we aim to provide clarity and practical guidance to those facing this challenge in their Metaflow projects.

Problem Description

The core problem lies in how Metaflow handles configurations when one flow triggers another using DeployedFlow, particularly when deploy-time configurations are involved. The error message, "Options were not properly set -- this is an internal error," suggests an issue with how the configurations are being passed or accessed during the triggering process. This issue specifically arises in Metaflow versions 2.19.7 and later, indicating a potential regression introduced in these versions. The use of DeployedFlow is a common pattern in Metaflow for orchestrating complex workflows where individual flows are deployed and triggered independently. This pattern is particularly useful for modularizing workflows and enabling reuse of flow components. However, this issue highlights the importance of careful consideration when dealing with configurations in such scenarios. When flows are triggered, it's crucial to ensure that the necessary configurations are correctly propagated to the triggered flow. The deploy-time Config feature in Metaflow allows setting configurations at the time of deployment, which can be beneficial for managing environment-specific settings or other deployment-related parameters. However, as this issue demonstrates, there can be challenges in ensuring these configurations are correctly handled during flow triggering. In the following sections, we will examine the code provided to reproduce this issue, paying close attention to how the configurations are defined and used within the flows.

Steps to Reproduce

The following code snippets and commands can be used to reproduce the issue:

1. Create `trigger_foo.py`:

# trigger_foo.py
from metaflow import DeployedFlow, kubernetes, project, step, Config, FlowSpec

@project(name="test")
class TriggerFoo(FlowSpec):
 config = Config("config", default_value={"hello": "world"})

 @kubernetes() # I needed an image that has requirements to run `from_argo_workflows` etc.
 @step
 def start(self):
 prelabel_flow = DeployedFlow.from_argo_workflows(identifier="test.prod.foo")
 prelabel_flow.trigger()

 self.next(self.end)

 @kubernetes()
 @step
 def end(self):
 pass


if __name__ == "__main__":
 TriggerFoo()

2. Create `foo.py`:

# foo.py
from metaflow import FlowSpec, kubernetes, project, step

@project(name="test")
class Foo(FlowSpec):

 @kubernetes()
 @step
 def start(self):
 self.next(self.end)

 @kubernetes()
 @step
 def end(self):
 pass


if __name__ == "__main__":
 Foo()

3. Run the following commands:

python foo.py --production argo-workflows create
python trigger_foo.py --production argo-workflows create
python trigger_foo.py --production argo-workflows trigger

These steps outline a clear and concise method for replicating the bug. The trigger_foo.py script defines a flow, TriggerFoo, that uses a deploy-time Config and triggers another flow, Foo, using DeployedFlow. The foo.py script defines the Foo flow, a simple flow with a start and end step. The commands then deploy both flows to Argo Workflows in production mode and attempt to trigger TriggerFoo. The critical point here is the use of DeployedFlow.from_argo_workflows to reference the deployed Foo flow and the subsequent trigger() call. This sequence of actions exposes the configuration-related issue in Metaflow versions 2.19.7 and above. When executed in the affected versions, this process will likely result in the "Options were not properly set" error. In contrast, running the same steps in Metaflow version 2.19.6 or earlier should not produce the error, highlighting the regression introduced in the later versions. By following these steps, users can reliably reproduce the issue and verify potential fixes or workarounds. The use of Argo Workflows in this example further emphasizes the importance of understanding how Metaflow interacts with external orchestration platforms and how configurations are handled in such integrated environments.

Code Explanation

Let's break down the code to understand the issue better.

`trigger_foo.py`

This script defines the TriggerFoo flow, which is responsible for triggering the Foo flow.

Config("config", default_value={"hello": "world"}): This line defines a deploy-time configuration named "config" with a default value.
DeployedFlow.from_argo_workflows(identifier="test.prod.foo"): This line retrieves a reference to the deployed Foo flow in the production environment.
prelabel_flow.trigger(): This line triggers the Foo flow. This is the point where the error occurs in Metaflow versions 2.19.7 and above.

`foo.py`

This script defines a simple flow, Foo, with a start and end step. This flow represents the flow being triggered by TriggerFoo.

The key aspect of the code is the interaction between the deploy-time Config in TriggerFoo and the DeployedFlow.trigger() call. The error suggests that the configuration context is not being correctly propagated or handled when the Foo flow is triggered. Specifically, the options related to the deploy-time config seem to be missing or improperly set when the triggered flow attempts to initialize. This could be due to a change in how Metaflow serializes or deserializes configurations during flow triggering, or it could be related to how the configuration context is passed between flows in the context of Argo Workflows. Understanding the flow of configuration data during flow triggering is critical to diagnosing this issue. The fact that this error appears in versions 2.19.7 and later indicates a regression, meaning a change in the codebase likely introduced this bug. Therefore, examining the changes made between versions 2.19.6 and 2.19.7 might provide clues as to the root cause of the problem. In subsequent sections, we will discuss potential workarounds and solutions for this issue.

Potential Causes

The root cause of this issue likely lies in how Metaflow handles deploy-time configurations when triggering flows using DeployedFlow. Several factors could be contributing to the problem:

Configuration Serialization/Deserialization: There might be an issue with how configurations are serialized before being passed to the triggered flow and deserialized within the triggered flow's context. A change in the serialization format or the deserialization logic could lead to the "Options were not properly set" error.
Context Propagation: The configuration context might not be correctly propagated from the triggering flow to the triggered flow. This could be due to an issue in how Metaflow manages the flow context or how it interacts with the underlying orchestration platform (Argo Workflows in this case).
Argo Workflows Integration: The integration with Argo Workflows might have introduced changes in how configurations are handled during flow triggering. It's possible that a change in the Argo Workflows API or Metaflow's interaction with it is causing the issue.
Regression Bug: As the issue is present in versions 2.19.7 and above but not in 2.19.6, it strongly suggests a regression bug. This means a specific code change introduced between these versions is likely the culprit. Identifying the commit that introduced this change would be crucial for fixing the issue.

To diagnose the root cause, it would be beneficial to:

Examine the code changes between Metaflow versions 2.19.6 and 2.19.7, focusing on areas related to configuration handling, flow triggering, and Argo Workflows integration.
Add logging and debugging statements to the Metaflow code to trace the flow of configuration data during the triggering process.
Investigate the Metaflow documentation and community forums for any reported issues or discussions related to this behavior.

Understanding these potential causes is crucial for developing effective solutions. In the next section, we will explore potential workarounds that users can employ to mitigate this issue while a permanent fix is being developed.

Potential Workarounds

While the underlying issue requires a fix in Metaflow itself, several workarounds can be employed to mitigate the problem in the meantime:

Downgrade Metaflow Version: The most straightforward workaround is to downgrade to Metaflow version 2.19.6 or earlier, where the issue is not present. This can be done by specifying the version during installation:
```
pip install metaflow==2.19.6
```
This approach ensures that the code runs without encountering the bug, but it also means missing out on any new features or bug fixes introduced in later versions.
Pass Configurations as Parameters: Instead of relying on deploy-time configurations, consider passing the necessary configuration values as parameters to the triggered flow. This can be achieved by modifying the trigger() call to include the desired parameters:
```
prelabel_flow.trigger(my_config_value=config.hello)
```
This approach requires modifying the triggered flow to accept these parameters and use them accordingly. It provides more explicit control over configuration passing, but it can also lead to more verbose code and potential maintenance overhead.
Use Environment Variables: Another workaround is to set environment variables before triggering the flow and access them within the triggered flow. This can be done using the os module in Python:
```
import os
os.environ['MY_CONFIG_VALUE'] = config.hello
prelabel_flow.trigger()
```
This approach avoids relying on Metaflow's configuration handling during flow triggering, but it can also make the code less portable and harder to reason about. It's important to ensure that the environment variables are set correctly in the deployment environment.
Modify the Triggered Flow: If possible, modify the triggered flow to avoid relying on deploy-time configurations altogether. This might involve hardcoding the configuration values or retrieving them from an external source. This approach can be more invasive, but it can also eliminate the source of the problem altogether.

These workarounds offer various ways to circumvent the issue, each with its own trade-offs. The best approach depends on the specific use case and the constraints of the project. It's crucial to carefully evaluate the implications of each workaround before implementing it.

Conclusion

In conclusion, the issue of triggering deployed flows with deploy-time configurations in Metaflow versions 2.19.7 and above represents a significant regression that can disrupt workflows. The "Options were not properly set" error indicates a problem with how configurations are handled during flow triggering, likely stemming from changes in configuration serialization, context propagation, or integration with Argo Workflows. While a permanent fix is needed in Metaflow, several workarounds can be employed to mitigate the issue, including downgrading the Metaflow version, passing configurations as parameters, using environment variables, or modifying the triggered flow. Each workaround has its own trade-offs, and the best approach depends on the specific use case.

It is essential for Metaflow users to be aware of this issue and its potential workarounds. Monitoring the Metaflow community forums and release notes for updates and a permanent fix is also recommended. Understanding the intricacies of Metaflow's configuration mechanisms and flow triggering processes is crucial for effective troubleshooting and building robust workflows. By carefully considering the potential causes of the issue and implementing appropriate workarounds, users can continue to leverage Metaflow's capabilities while minimizing the impact of this bug.

For more information on Metaflow and its features, please visit the official Metaflow documentation: Metaflow Documentation.