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Coordinating Larger Metaflow Projects


This page applies equally to all production orchestrators supported by Metaflow, i.e. AWS Step Functions, Argo Workflows, and Airflow. Examples below mention step-functions but you can replace step-functions with argo-workflows or airflow to get equivalent behavior on your orchestrator of choice (except the last part about event triggering, which applies only to Argo Workflows).

Most Metaflow projects start as a simple Python script that is developed by a single data scientist. Metaflow takes care of keeping results organized automatically, so you can focus on developing models and the business logic around them.

Over time, the project matures to the point that you want to deploy it to a production orchestrator to test how the model works with real-life, updating data. In Metaflow, this is a matter of executing a single command like step-functions create. Having the workflow run automatically with fresh data is a great way to surface unforeseen issues in the code.

After a few iterations, the workflow starts to work reliably. If the results are promising enough, stakeholders can start relying on the results of your workflow. Often, success attracts more developers to join the project. At this point, you will need to start thinking about how to coordinate work amongst multiple people and how to iterate on new, experimental versions of the workflow while providing stable results to your stakeholders. This is where the @project decorator comes in.

The @project decorator

During development, multiple people can work on the same workflow simultaneously as Metaflow keeps executions isolated through independently stored artifacts and namespaces. However, by default, all production deployments are bound to the name of the workflow. If multiple people call step-functions create independently, each deployment will overwrite the previous one.

In the early stages of a project, this simple model is convenient but as the project grows, it is desirable that multiple people can test their own production deployments without interference. Or, as a single developer, you may want to experiment with multiple independent deployments of your workflow.

Metaflow provides a @project decorator to address this need. The @project decorator is used at the FlowSpec-level to bind a Flow to a specific project. All flows with the same project name belong to the same project.

You can test this by executing the following flow.
from metaflow import FlowSpec, step, project, current

class ProjectFlow(FlowSpec):

def start(self):
print('project name:', current.project_name)
print('project branch:', current.branch_name)
print('is this a production run?', current.is_production)

def end(self):

if __name__ == '__main__':

Save the above snippet in a file, Now you can run the flow as usual:

python run

The @project decorator exposes new project-related attributes, project_name, branch_name, and is_production in the current object which you can use to alter the behavior of the flow depending on the execution context. Besides the new attributes in current, the flow works exactly as before when executed outside a production orchestrator.

Projects in production

The main benefit of @project relates to deployments on a production orchestrator. Below, we will cover this case: How to manage a production project with multiple developers collaborating.

Single Flow, multiple developers

If ProjectFlow did not have a @project decorator, it would get deployed as a workflow called ProjectFlow on AWS Step Functions by step-functions create. Only one version of ProjectFlow could exist on a production orchestrator at a time. Everyone deploying the flow would need to know the production token assigned to the deployment.

On the UI of your production orchestrator, you would see one workflow called ProjectFlow:

The @project decorator changes this behavior. Let's deploy ProjectFlow:

python step-functions create

The @project decorator adds a user-specific prefix in the workflow name: the workflow gets deployed with a name like example_project.user.YOURNAME.ProjectFlow where YOURNAME is your username. Metaflow gets the username by looking, in order, at the following environment variables: METAFLOW_USER, SUDO_USER, USERNAME and USER.

This allows multiple developers to deploy their workflows on a production orchestrator without fear that they might interfere with someone else's deployment. Imagine Alice, Bob, and Carol collaborating on a project. Each one of them can call step-functions create independently, which results in three separate workflows in production:

Note that each one of these deployments gets an isolated namespace and a separate production token. This means that if your code refers to Flow('ProjectFlow').latest_run in production, it is guaranteed to refer to a run that corresponds to its own isolated deployment. The deployments don't interfere with each other.

Main production deployment

In addition to user-specific deployments, most projects have a single blessed production version which represents the official results of the workflow.

The @project decorator exposes a new top-level command-line argument, --production that denotes a production run or deployment. See what happens when you run ProjectFlow with --production:

python --production run

The current.branch_name will be set to prod and current.is_production is set to True. For instance, you could write results to a production table only if current.is_production.

You can deploy a production version to AWS Step Functions as follows:

python --production step-functions create

Instead of deploying the flow with a user-specific prefix, this will deploy the flow as You will get a warning about missing production token if you are not authorized to deploy the flow to production.

The production deployment gets a separate, isolated namespace of its own:

Custom branches

Imagine that ProjectFlow has a stable version in production. Now, Alice and Bob want to start developing a new, experimental version of the flow. They can work on a common codebase and run the code locally independently. Eventually, they will want to deploy the experimental version to a production orchestrator and let it run in parallel with the production version for a while, to see that it works correctly.

Alice and Bob could deploy the experimental version under a user-specific namespace of theirs but this would make it hard to keep iterating on the code, as one of the usernamespaces would be reserved for the long-running experiment. A better approach is to deploy the experimental code under a custom branch.

Try the following:

python --branch better_version run

The flow reports that the branch name is test.better_version. You can deploy the custom branch to AWS Step Functions:

python --branch better_version step-functions create

which will result in another separate, isolated namespace:

Alice and Bob can share the production token corresponding to the branch, so either of them can redeploy the branch when needed.

Custom branches for production deployments

There are scenarios where Alice might need to run multiple variants of ProjectFlow in production. Alice can very simply use custom branches to run multiple production versions -

Try the following:

python --branch better_version --production run

The flow reports that the branch name is prod.better_version. You can deploy the custom branch to AWS Step Functions:

python --branch better_version --production step-functions create

@project and event triggering

Importantly, workflows connected through the @trigger_on_finish decorator respect the @project decorator. Besides deploying individual workflows as branches, as shown above, you can deploy flows-of-flows as isolated branches. Read more about this pattern in Deploying Variants of Event-Triggered Flows.


The @project decorator makes available three classes of namespaces that will affect the behavior of a production deployment:

  • user is the default. It will deploy to a user-specific, private namespace. Use it for testing production deployments.
  • test denotes custom branches that can be shared amongst multiple users. Use it for deploying experimental versions that can run in parallel with production. Deploy custom branches with --branch foo.
  • prod denotes the global production namespace. Use it for deploying the official production version of the project. Deploy to production with --production. For multiple production variants, deploy custom branches with --production --branch foo.

Note that the isolated namespaces offered by @project work best when your code is designed to respect these boundaries. For instance, when writing results to a table, you can use current.branch_name to choose the table to write to, or you can disable writes outside production by checking current.is_production.