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Scheduling Metaflow Flows with Argo Workflows

Argo Workflows is a Kubernetes-native workflow orchestrator - you can read Argo Workflows documentation to learn all about it. If you just want to get your flow in production, this document contains everything you need to know.

In Metaflow's point of view, the main benefits of Argo Workflows are the following:

  • As of today, Argo Workflows is the only production orchestrator supported by Metaflow that supports Metaflow's event-triggering functionality through Argo Events. For many users, this by itself is the main reason for choosing Argo Workflows.
  • Argo Workflows orchestrates workflows expressed as directed acyclic graphs. This means that we can map Metaflow flows to the corresponding Argo Workflows Workflow Template fully automatically. This gives you much more detail about what gets executed and how, in contrast to treating Metaflow scripts as black boxes.
  • Argo Workflows comes with tooling that is required for running workflows in production. You can benefit from battle-hardened solutions provided by the Kubernetes community for alerting, monitoring, and scheduling. By using Argo Workflows your Metaflow flows can integrate seamlessly with the wider Kubernetes offerings.

When running on Argo Workflows, Metaflow code works exactly as it does locally: No changes are required in the code. All data artifacts produced by steps run on Argo Workflows are available using the Client API. All tasks are run on Kubernetes respecting the @resources decorator, as if the @kubernetes decorator was added to all steps, as explained in Executing Tasks Remotely.

This document describes the basics of Argo Workflows scheduling. If your project involves multiple people, multiple workflows, or it is becoming business-critical, check out the section around coordinating larger Metaflow projects.

Pushing a flow to production

Let's use the flow from the section about parameters as an example:

from metaflow import FlowSpec, Parameter, step

class ParameterFlow(FlowSpec):
alpha = Parameter('alpha',
help='Learning rate',

def start(self):
print('alpha is %f' % self.alpha)

def end(self):
print('alpha is still %f' % self.alpha)

if __name__ == '__main__':

Save this script to a file To deploy a version to Argo Workflows, simply run

python --with retry argo-workflows create

This command takes a snapshot of your code in the working directory, as well as the version of Metaflow used and exports the whole package to Argo Workflows for scheduling.

It is highly recommended that you enable retries when deploying to Argo Workflows, which you can do easily with --with retry as shown above. However, make sure that all your steps are safe to retry before you do this. If some of your steps interact with external services in ways that can't tolerate automatic retries, decorate them with retry with times set to zero (times=0) as described in How to Prevent Retries.

The command will export your workflow to Argo Workflows as a workflow template. You can also search for the workflow template by name within the Argo Workflows UI.

You can click on Submit new workflow to submit your generated Workflow Template for execution

Metaflow automatically maps Parameters of your flow to corresponding parameters on Argo Workflows.

After you click Submit, Argo Workflow starts running the flow:

In this case, the run should succeed without problems. If there were errors, you could reproduce them locally as explained in Debugging with Metaflow.

You can trigger the workflow through command line as well:

python argo-workflows trigger --alpha 0.5

If you run argo-workflows create again, it will create a new version of your flow on Argo Workflows. The newest version becomes the production version automatically. If you want to test on Argo Workflows without interfering with a production flow, you can change the name of your class, e.g. from ParameterFlow to ParameterFlowStaging, and argo-workflows create the flow under a new name or use the @project decorator.

Note that argo-workflows create creates a new isolated production namespace for your production flow. Please read Organizing Results to learn all about namespace behavior.

Limiting the number of concurrent tasks

By default, Metaflow configures Argo Workflows to execute at most 100 tasks concurrently within a foreach step. This should ensure that most workflows finish quickly without overwhelming your Kubernetes cluster, the execution backend.

If your workflow includes a large foreach and you need results faster, you can increase the default with the --max-workers option. For instance, argo-workflows create --max-workers 500 allows 500 tasks to be executed concurrently for every foreach step.

This option is similar to run --max-workers that is used to limit concurrency outside Argo Workflows.

Deploy-time parameters

You can customize Argo Workflows deployments through Parameters that are evaluated at the deployment time, i.e. when argo-workflows create is executed.

For instance, you can change the default value of a Parameter based on who deployed the workflow or what Git branch the deployment was executed in. Crucially, the function in Parameter is evaluated only once during argo-workflows create and not during the execution of the flow.

You can run the flow locally as usual. The function inside Parameter is called only once when the execution starts.

from metaflow import FlowSpec, Parameter, step, JSONType
from datetime import datetime
import json

def deployment_info(context):
return json.dumps({'who': context.user_name,

class DeploymentInfoFlow(FlowSpec):
info = Parameter('deployment_info',

def start(self):
print('This flow was deployed at %s by %s'\
% (['when'],['who']))

def end(self):

if __name__ == '__main__':

When argo-workflows create is called, deployment_info is evaluated which captures your username and the time of deployment. This information remains constant on Argo Workflows, although the user may override the default value.

The context object is passed to any function defined in Parameter. It contains various fields related to the flow being deployed. By relying on the values passed in context, you can create generic deploy-time functions that can be reused by multiple flows.

Scheduling a flow

By default, a flow on Argo Workflows does not run automatically. You need to set up a trigger to launch the flow when an event occurs. With Argo Workflows, Metaflow supports two kinds of triggering:

  1. Triggering based on events (using @trigger and @trigger_on_finish).
  2. Triggering based on time (using @schedule), described below.

Time-based triggering

Metaflow provides built-in support for triggering Metaflow flows through time-based (cron) triggers. Use a time-based trigger if you want to trigger the workflow at a certain time.

Time-based triggers are implemented at the FlowSpec-level using the @schedule decorator. This flow is triggered hourly:

from metaflow import FlowSpec, schedule, step
from datetime import datetime

class HourlyFlow(FlowSpec):

def start(self):
now ='%Y-%m-%d %H:%M:%S')
print('time is %s' % now)

def end(self):

if __name__ == '__main__':

You can define the schedule with @schedule in one of the following ways:

  • @schedule(weekly=True) runs the workflow on Sundays at midnight.
  • @schedule(daily=True) runs the workflow every day at midnight.
  • @schedule(hourly=True) runs the workflow every hour.
  • @schedule(cron='0 10 * * ? *', timezone='Etc/UTC') runs the workflow at the given Cron schedule, in this case at 10am UTC every day.

Reproducing failed production runs

Let's use DebugFlow from the debugging section as an example. The flow contains a bug in the step b. When you run it, the failed run will look like this on the Argo Workflows UI:

Notice the execution ID of branchflow-r8qcn. When running on Argo Workflows, Metaflow uses the Argo Workflows workflow execution name (prefixed with argo-) as the run id.

The graph visualization shows that step b failed, as expected. First, you should inspect the logs of the failed step to get an idea of why it failed. You can access Kubernetes step logs in the Argo Workflows UI by selecting the failed task and clicking on the logs button.

Next, we want to reproduce the above error locally. We do this by resuming the specific Argo Workflows run that failed:

python resume --origin-run-id argo-branchflow-r8qcn

This will reuse the results of the start and a step from the Argo Workflows run. It will try to rerun the step b locally, which fails with the same error as it does in production.

You can fix the error locally as above. In the case of this simple flow, you can run the whole flow locally to confirm that the fix works. After validating the results, you would deploy a new version to production with argo-workflows create.

However, this might not be a feasible approach for complex production flow. For instance, the flow might process large amounts of data that can not be handled in your local instance. We have better approaches for staging flows for production:

Staging flows for production

The easiest approach to test a demanding flow is to run it with Kubernetes. This works even with resume:

python resume --origin-run-id argo-branchflow-r8qcn --with kubernetes

This will resume your flow and run every step on Kubernetes. When you are ready to test a fixed flow end-to-end, just run it as follows:

python run --with kubernetes

Alternatively, you can change the name of the flow temporarily, e.g. from DebugFlow to DebugFlowStaging. Then you can run argo-workflows create with the new name, which will create a separate staging flow on Argo Workflows. You can also use the @project decorator.

You can test the staging flow freely without interfering with the production flow. Once the staging flow runs successfully, you can confidently deploy a new version to production.