How to run flows in local processes

The simplest way to create a deployment for your flow is by calling its serve method.

Serve a flow

The serve method creates a deployment for the flow and starts a long-running process that monitors for work from the Prefect server. When work is found, it is executed within its own isolated subprocess.

hello_world.py

from prefect import flow


@flow(log_prints=True)
def hello_world(name: str = "world", goodbye: bool = False):
    print(f"Hello {name} from Prefect! 🤗")

    if goodbye:
        print(f"Goodbye {name}!")


if __name__ == "__main__":
    # creates a deployment and starts a long-running
    # process that listens for scheduled work
    hello_world.serve(name="my-first-deployment",
        tags=["onboarding"],
        parameters={"goodbye": True},
        interval=60
    )

This interface provides the configuration for a deployment (with no strong infrastructure requirements), such as:

schedules
event triggers
metadata such as tags and description
default parameter values

Schedules are auto-paused on shutdownBy default, stopping the process running flow.serve will pause the schedule for the deployment (if it has one).When running this in environments where restarts are expected use thepause_on_shutdown=False flag to prevent this behavior:

if __name__ == "__main__":
    hello_world.serve(
        name="my-first-deployment",
        tags=["onboarding"],
        parameters={"goodbye": True},
        pause_on_shutdown=False,
        interval=60
    )

Additional serve options

The serve method on flows exposes many options for the deployment. Here’s how to use some of those options:

cron: a keyword that allows you to set a cron string schedule for the deployment; see schedules for more advanced scheduling options
tags: a keyword that allows you to tag this deployment and its runs for bookkeeping and filtering purposes
description: a keyword that allows you to document what this deployment does; by default the description is set from the docstring of the flow function (if documented)
version: a keyword that allows you to track changes to your deployment; uses a hash of the file containing the flow by default; popular options include semver tags or git commit hashes
triggers: a keyword that allows you to define a set of conditions for when the deployment should run; see triggers for more on Prefect Events concepts

Next, add these options to your deployment:

if __name__ == "__main__":
    get_repo_info.serve(
        name="my-first-deployment",
        cron="* * * * *",
        tags=["testing", "tutorial"],
        description="Given a GitHub repository, logs repository statistics for that repo.",
        version="tutorial/deployments",
    )

Triggers with .serveSee this example that triggers downstream work on upstream events.

serve() is a long-running processTo execute remotely triggered or scheduled runs, your script with flow.serve must be actively running. Stop the script with CTRL+C and your schedule will automatically pause.

Serve multiple flows at once

Serve multiple flows with the same process using the serve utility along with the to_deployment method of flows:

serve_two_flows.py

import time
from prefect import flow, serve


@flow
def slow_flow(sleep: int = 60):
    "Sleepy flow - sleeps the provided amount of time (in seconds)."
    time.sleep(sleep)


@flow
def fast_flow():
    "Fastest flow this side of the Mississippi."
    return


if __name__ == "__main__":
    slow_deploy = slow_flow.to_deployment(name="sleeper", interval=45)
    fast_deploy = fast_flow.to_deployment(name="fast")
    serve(slow_deploy, fast_deploy)

The behavior and interfaces are identical to the single flow case. A few things to note:

the flow.to_deployment interface exposes the exact same options as flow.serve; this method produces a deployment object
the deployments are only registered with the API once serve(...) is called
when serving multiple deployments, the only requirement is that they share a Python environment; they can be executed and scheduled independently of each other

A few optional steps for exploration include:

pause and unpause the schedule for the "sleeper" deployment
use the UI to submit ad-hoc runs for the "sleeper" deployment with different values for sleep
cancel an active run for the "sleeper" deployment from the UI

Hybrid execution optionPrefect’s deployment interface allows you to choose a hybrid execution model. Whether you use Prefect Cloud or self-host Prefect server, you can run workflows in the environments best suited to their execution. This model enables efficient use of your infrastructure resources while maintaining the privacy of your code and data. There is no ingress required. Read more about our hybrid model.

Serve instance methods

You can serve flow methods that are part of a class instance. This is useful when you want to configure a flow once at initialization time and reuse that configuration across all runs.

data_processor.py

from prefect import flow


class DataProcessor:
    """Processor configured at initialization time."""

    def __init__(self, environment: str):
        # Configuration is set once when the instance is created
        if environment == "prod":
            self.api_url = "https://api.example.com"
            self.batch_size = 1000
        else:
            self.api_url = "https://staging.example.com"
            self.batch_size = 100

    @flow(log_prints=True)
    def process_batch(self, batch_id: str):
        """Process a batch using the configured settings."""
        print(f"Processing batch {batch_id}")
        print(f"API URL: {self.api_url}")
        print(f"Batch size: {self.batch_size}")
        # processing logic here using self.api_url and self.batch_size


if __name__ == "__main__":
    # Create processor configured for staging environment
    processor = DataProcessor(environment="staging")
    # All flow runs will use the staging configuration
    processor.process_batch.serve(name="batch-processor")

The instance configuration (set during __init__) is available to all flow runs. This is useful for environment-specific settings, connection parameters, or any configuration that should be consistent across all runs of the deployment.

Retrieve a flow from remote storage

Just like the .deploy method, the flow.from_source method is used to define how to retrieve the flow that you want to serve.

`from_source`

The flow.from_source method on Flow objects requires a source and an entrypoint.

`source`

The source of your deployment can be:

a path to a local directory such as path/to/a/local/directory
a repository URL such as https://github.com/org/repo.git
a GitRepository object that accepts
- a repository URL
- a reference to a branch, tag, or commit hash
- GitCredentials for private repositories

`entrypoint`

A flow entrypoint is the path to the file where the flow is located within that source, in the form

{path}:{flow_name}

For example, the following code will load the hello flow from the flows/hello_world.py file in the PrefectHQ/examples repository:

load_from_url.py

from prefect import flow


my_flow = flow.from_source(
    source="https://github.com/PrefectHQ/examples.git",
    entrypoint="flows/hello_world.py:hello"
)


if __name__ == "__main__":
    my_flow()

40:33.818 | INFO    | prefect.engine - Created flow run 'muscular-perch' for flow 'hello'
40:34.048 | INFO    | Flow run 'muscular-perch' - Hello world!
40:34.706 | INFO    | Flow run 'muscular-perch' - Finished in state Completed()

For more ways to store and access flow code, see the Retrieve code from storage page.

You can serve loaded flowsYou can serve a flow loaded from remote storage with the same serve method as a local flow:

serve_loaded_flow.py

from prefect import flow


if __name__ == "__main__":
    flow.from_source(
        source="https://github.com/org/repo.git",
        entrypoint="flows.py:my_flow"
    ).serve(name="my-deployment")

Remote storage polling

When you serve a flow loaded from remote storage, the serving process periodically polls your remote storage for updates to the flow’s code. This pattern allows you to update your flow code without restarting the serving process. Note that if you change metadata associated with your flow’s deployment such as parameters, you will need to restart the serve process.

Workflows

Deployments

Configuration

Automations

Workflow Infrastructure

Prefect Cloud

Prefect Self-hosted

AI

Migrate

How to run flows in local processes

Serve a flow

Additional serve options

Serve multiple flows at once

Serve instance methods

Retrieve a flow from remote storage

`from_source`

`source`

`entrypoint`

Remote storage polling

Further reading

​Serve a flow

​Additional serve options

​Serve multiple flows at once

​Serve instance methods

​Retrieve a flow from remote storage

​from_source

​source

​entrypoint

​Remote storage polling

​Further reading

Serve a flow

Additional serve options

Serve multiple flows at once

Serve instance methods

Retrieve a flow from remote storage

`from_source`

`source`

`entrypoint`

Remote storage polling

Further reading