Deploying apps in flows

`@app_deploy`

Simplify bookkeeping and lifecycle management for apps deployed from Metaflow flows. While you can deploy apps from within a flow using the AppDeployer API directly, doing so at scale introduces operational challenges: tracking which apps belong to which run, cleaning up apps when flows complete or fail, and discovering apps deployed across many runs. This decorator addresses these concerns automatically. When applied to a flow, @app_deploy provides:

Automatic Tagging: Every app deployed gains Metaflow metadata tags (flow name, run ID, step, task ID, project/branch info) enabling easy discovery and association with specific flow executions.
Lifecycle Management: Configure automatic cleanup policies to scale down or delete apps when the flow exits (on success or failure), preventing orphaned apps from accumulating.
Convenient Access: Exposes current.apps with the flow’s code package and container image, plus a list() method to discover all apps deployed in the current run.

Parameters:

string

default:"none"

Action to perform on all apps deployed in this run when the flow exits:

"none": No cleanup; apps remain running after flow completion.
"scale_down": Scale all deployed apps to zero replicas.
"delete": Delete all deployed apps.

Examples:

from metaflow import FlowSpec, step, current, app_deploy
from metaflow.apps import AppDeployer

@app_deploy
class MyFlow(FlowSpec):

    @step
    def start(self):
        # Deploy an app using the flow's code package
        deployer = AppDeployer(
            name="my-service",
            port=8000,
            image=current.apps.current_image,
            code_package=current.apps.metaflow_code_package,
            commands=["python server.py"],
        )
        self.app = deployer.deploy()
        self.next(self.end)

    @step
    def end(self):
        # List all apps deployed in this run
        apps = current.apps.list()
        print(f"Deployed {len(apps)} app(s)")

With cleanup policy to prevent orphaned apps:

@app_deploy(cleanup_policy="scale_down")
class MyFlow(FlowSpec):
    # Apps will be scaled to zero when flow completes or fails,
    # preventing resource waste from forgotten deployments
    ...

`current.apps`

Manager for apps deployed within a Metaflow flow execution. Accessible via current.apps when using the @app_deploy decorator. Provides access to the flow’s code package, container image, and methods to list apps deployed in the current run. Attributes:

object

The code package for the current flow, ready to use with AppDeployer.

string

The container image used by the current task (from fast bakery or similar).

string

The default Kubernetes container image from the Metaflow config.

Examples:

# python myflow.py --environment=fast-bakery run --with kubernetes
from metaflow.apps import AppDeployer

@pypi(packages={"flask": ">=2.0", "requests": ">=2.28"})
@step
def deploy_step(self):
    image = current.apps.current_image
    if image is None:
        image = current.apps.default_image
    # Use the flow's code package directly
    deployer = AppDeployer(
        name="my-app",
        port=8000,
        image=image,
        code_package=current.apps.metaflow_code_package,
        commands=["python app.py"],
    )
    deployed = deployer.deploy()

    # List apps from this run
    apps = current.apps.list()

Anaconda Platform

Getting Started

Platform Guides

Deploying apps in flows

`@app_deploy`

`current.apps`

​@app_deploy

​current.apps

`@app_deploy`

`current.apps`