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Deployments

In the tutorials leading up to this one, you've been able to explore Prefect capabilities like flows, tasks, retries, caching, and so on. But so far, you've run flows as scripts.

Deployments take your flows to the next level: adding the information needed for scheduling flow runs or triggering a flow run via an API call. Deployments elevate workflows from functions that you call manually to API-managed entities.

Components of a deployment

You need just a few ingredients to turn a flow definition into a deployment:

That's it. To create flow runs based on the deployment, you need a few more pieces:

These all come with Prefect. You just have to configure them and set them to work. You'll see how to configure each component during this tutorial.

Optionally, you can configure storage for packaging and saving your flow code and dependencies.

Setting up

First, create a new folder that will contain all of the files and dependencies needed by your flow deployment. This is a best practice for developing and deploying flows.

$ mkdir prefect-tutorial
$ cd prefect-tutorial

You may organize your flow scripts and dependencies in any way that suits your team's needs and standards. For this tutorial, we'll keep files within this directory.

In order to demonstrate some of the benefits of Prefect deployments, let's add two additional files to our folder:

$ echo '{"some-piece-of-config": 100}' > config.json
$ echo 'AN_IMPORTED_MESSAGE = "Hello from another file"' > utilities.py

From flow to deployment

As noted earlier, the first ingredient of a deployment is a flow script. You've seen a few of these already, and perhaps have written a few, if you've been following the tutorials.

Let's start with a simple example that captures many aspects of a standard project:

  • Utility files that you import from to keep your code clean.
  • Parameterized runs with a basic CLI interface.
  • Logging.

This flow contains a single flow function log_flow(), and a single task log_task that logs messages based on a parameter input, your installed Prefect version, and an imported message from another file in your project:

import sys
import prefect
from prefect import flow, task, get_run_logger
from utilities import AN_IMPORTED_MESSAGE


@task
def log_task(name):
    logger = get_run_logger()
    logger.info("Hello %s!", name)
    logger.info("Prefect Version = %s ๐Ÿš€", prefect.__version__)
    logger.debug(AN_IMPORTED_MESSAGE)


@flow()
def log_flow(name: str):
    log_task(name)


if __name__ == "__main__":
    name = sys.argv[1]
    log_flow(name)

Save this in a file log_flow.py and run it as a Python script: python log_flow.py Marvin. You'll see output like this:

$ python log_flow.py Marvin
22:00:16.419 | INFO    | prefect.engine - Created flow run 'vehement-eagle' for flow 'log-flow'
22:00:16.570 | INFO    | Flow run 'vehement-eagle' - Created task run 'log_task-82fbd1c0-0' for task 'log_task'
22:00:16.570 | INFO    | Flow run 'vehement-eagle' - Executing 'log_task-82fbd1c0-0' immediately...
22:00:16.599 | INFO    | Task run 'log_task-82fbd1c0-0' - Hello Marvin!
22:00:16.600 | INFO    | Task run 'log_task-82fbd1c0-0' - Prefect Version = 2.3.1 ๐Ÿš€
22:00:16.626 | INFO    | Task run 'log_task-82fbd1c0-0' - Finished in state Completed()
22:00:16.659 | INFO    | Flow run 'vehement-eagle' - Finished in state Completed('All states completed.')

Like previous flow examples, this is still a script that you have to run locally.

In this tutorial, you'll use this flow script (and its supporting files!) to create a deployment on the Prefect server. With a deployment, you can trigger ad-hoc parametrized flow runs via the UI or an API call. You could also schedule automatic flow runs that run anywhere you can run a Prefect agent, including on remote infrastructure.

You'll create the deployment for this flow by doing the following:

  • Configure deployment settings via one of Prefect's interfaces (CLI or Python).
  • Optionally copy the relevant flow files to a specified storage location from which it can be retrieved for flow runs
  • Apply the deployment settings to create a deployment on the Prefect server
  • Inspect the deployment with the Prefect CLI and Prefect UI
  • Start an ad hoc flow run based on the deployment

Deployment creation with the Prefect CLI

To create a deployment from an existing flow script using the CLI, there are just a few steps:

  1. Use the prefect deployment build Prefect CLI command to create a deployment definition YAML file. By default this step also uploads your flow script and any supporting files to storage, if you've specified storage for the deployment.
  2. Optionally, before applying, you can edit the deployment YAML file to include additional settings that are not easily specified via CLI flags.
  3. Use the prefect deployment apply Prefect CLI command to create the deployment with the Prefect Orion server based on the settings in the deployment YAML file.

Build a deployment definition

All you need for the first step, building the deployment artifacts, is:

  • The path and filename of the flow script.
  • The name of the flow function that is the entrypoint to the flow.
  • A name for the deployment.

Entrypoint

What do we mean by the "entrypoint" function?

A flow script file may contain definitions for multiple flow (@flow) and task (@task) functions. The entrypoint is the flow function that is called to begin the workflow, and other task and flow functions may be called from within the entrypoint flow.

You can provide additional settings — we'll demonstrate that in a future step — but this is the minimum required information to create a deployment.

To build deployment files for log_flow.py, use the following command:

$ prefect deployment build ./log_flow.py:log_flow -n log-simple -q test

What did we do here? Let's break down the command:

  • prefect deployment build is the Prefect CLI command that enables you to prepare the settings for a deployment.
  • ./log_flow.py:log_flow specifies the location of the flow script file and the name of the entrypoint flow function, separated by a colon.
  • -n log-simple specifies a name for the deployment.
  • -q test specifies a work queue for the deployment. Work queues direct scheduled runs to agents.

You can pass other option flags. For example, you may specify multiple tags by providing a -t tag parameter for each tag you want applied to the deployment. Options are described in the Deployments documentation or via the prefect deployment build --help command.

What happens when you run prefect deployment build?

$ prefect deployment build ./log_flow.py:log_flow -n log-simple -q test
Found flow 'log-flow'
Default '.prefectignore' file written to
/Users/terry/prefect-tutorial/.prefectignore
Deployment storage None does not have upload capabilities; no files uploaded.  Pass --skip-upload to suppress this warning.
Deployment YAML created at
'/Users/terry/prefect-tutorial/log_flow-deployment.yaml'.

First, the prefect deployment build command checks that a valid flow script and entrypoint flow function exist before continuing.

Next, since we don't have one already in the folder, the command writes a .prefectignore file to the working directory where you ran the build command.

If we had specified remote storage for the deployment, the command would have attempted to upload files to the storage location. Since we did not specify storage, the deployment references the local files. We'll cover differences between using local storage and configuring a remote storage block in a later step.

Finally, it writes a log_flow-deployment.yaml file, which contains details about the deployment for this flow.

You can list the contents of the folder to see what we have at this step in the deployment process:

~/prefect-tutorial $ ls -a
.               .prefectignore      config.json                 log_flow.py
..              __pycache__         log_flow-deployment.yaml    utilities.py

Ignoring files with .prefectignore

By default, prefect deployment build automatically uploads your flow script and any supporting files in the present working directory to storage (if you've specified remote storage for the deployment).

To exclude files from being uploaded, you can create a .prefectignore file. .prefectignore enables you to specify files that should be ignored by the deployment creation process. The syntax follows .gitignore patterns.

.prefectignore is preconfigured with common artifacts from Python, environment managers, operating systems, and other applications that you probably don't want included in flows uploaded to storage.

It's also a good flow development practice to store flow files and their dependencies in a folder structure that helps ensure only the files needed to execute flow runs are uploaded to storage.

In our example, we might add a line to .prefectignore for config.json as it is an unused file.

Configure the deployment

Note that the flow requires a name parameter, but we didn't specify one when building the deployment.yaml file. To make sure flow runs based on this deployment have a default name parameter, we'll add one to the deployment definition. Additionally, one of our logs is a DEBUG level log — to ensure that log is emitted, we will specify a custom environment variable.

Open the log_flow-deployment.yaml file and edit the parameters to include a default as parameters: {'name': 'Marvin'} and the infra_overrides to include the relevant environment variable (note that both JSON and nested key/value pairs work here):

###
### A complete description of a Prefect Deployment for flow 'log-flow'
###
name: log-simple
description: null
version: 450637a8874a5dd3a81039a89e90c915
# The work queue that will handle this deployment's runs
work_queue_name: test
tags: []
parameters: {'name': 'Marvin'}
schedule: null
infra_overrides:
  env:
    PREFECT_LOGGING_LEVEL: DEBUG
infrastructure:
  type: process
  env: {}
  labels: {}
  name: null
  command:
  - python
  - -m
  - prefect.engine
  stream_output: true

###
### DO NOT EDIT BELOW THIS LINE
###
flow_name: log-flow
manifest_path: null
storage: null
path: /Users/terry/test/dplytest/prefect-tutorial
entrypoint: log_flow.py:log_flow
parameter_openapi_schema:
  title: Parameters
  type: object
  properties:
    name:
      title: name
      type: string
  required:
  - name
  definitions: null

Note that the YAML configuration includes the ability to add a description, a default work queue, tags, a schedule, and more.

Apply the deployment

To review, we have four files that make up the artifacts for this particular deployment (there could be more if we had supporting libraries or modules, configuration, and so on).

  • The flow code in log_flow.py
  • The supporting code in utilities.py
  • The ignore file .prefectignore
  • The deployment definition in log_flow-deployment.yaml

Now we can apply the settings in log_flow-deployment.yaml to create the deployment object on the Prefect Orion server API — or on a Prefect Cloud workspace if you had configured the Prefect Cloud API as your backend.

Use the prefect deployment apply command to create the deployment on the Prefect server, specifying the name of the log_flow-deployment.yaml file.

$ prefect deployment apply log_flow-deployment.yaml
Successfully loaded 'log-simple'
Deployment 'log-flow/log-simple' successfully created with id
'517fd294-2bd3-4738-9515-0c68092ce35d'.

You can now use the Prefect CLI to create a flow run for this deployment and run it with an agent that pulls work from the 'test' work queue:

$ prefect deployment run 'log-flow/log-simple'
$ prefect agent start -q 'test'

Now your deployment has been created by the Prefect API and is ready to create future log_flow flow runs through the API or the scheduler.

To demonstrate that your deployment exists, list all of the current deployments:

$ prefect deployment ls
                                Deployments
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ Name                                โ”ƒ ID                                   โ”ƒ
โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ hello-flow/example-deployment       โ”‚ 60a6911e-1125-4a33-b37f-3ba16b86837d โ”‚
โ”‚ leonardo_dicapriflow/leo-deployment โ”‚ 3d2f55a2-46df-4857-ab6f-6cc80ce9cf9c โ”‚
โ”‚ log-flow/log-simple                 โ”‚ 517fd294-2bd3-4738-9515-0c68092ce35d โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Use prefect deployment inspect to display details for a specific deployment.

$ prefect deployment inspect log-flow/log-simple
{
    'id': '517fd294-2bd3-4738-9515-0c68092ce35d',
    'created': '2022-08-22T20:06:54.719808+00:00',
    'updated': '2022-08-22T20:06:54.717511+00:00',
    'name': 'log-simple',
    'version': '450637a8874a5dd3a81039a89e90c915',
    'description': None,
    'flow_id': 'da22db55-0a66-4f2a-ae5f-e0b898529a8f',
    'schedule': None,
    'is_schedule_active': True,
    'infra_overrides': {'env': {'PREFECT_LOGGING_LEVEL': 'DEBUG'}},
    'parameters': {'name': 'Marvin'},
    'tags': [],
    'work_queue_name': 'test',
    'parameter_openapi_schema': {
        'title': 'Parameters',
        'type': 'object',
        'properties': {'name': {'title': 'name', 'type': 'string'}},
        'required': ['name']
    },
    'path': '/Users/terry/test/dplytest/prefect-tutorial',
    'entrypoint': 'log_flow.py:log_flow',
    'manifest_path': None,
    'storage_document_id': None,
    'infrastructure_document_id': '219341e5-0edb-474e-9df4-6c92122e56ce',
    'infrastructure': {
        'type': 'process',
        'env': {},
        'labels': {},
        'name': None,
        'command': ['python', '-m', 'prefect.engine'],
        'stream_output': True
    }
}

Customize this workflow to your needs

You may not want Prefect to automatically upload your files in the build step, or you may want to do everything in one single CLI command. Whatever your preference, the Prefect CLI is highly customizable:

  • prefect deployment build accepts a --skip-upload flag that avoids automatic file uploads
  • prefect deployment apply accepts an --upload flag that performs the file upload in the apply step
  • prefect deployment build accepts an --apply flag that also performs the apply step

Deployment creation with Python

We can perform all of the same actions above with Python as our interface instead of the CLI — which interface to use is ultimately a matter of preference.

Here we mirror the steps taken above with a new Python file saved as deployment.py in the root of our project directory:

# deployment.py

from log_flow import log_flow
from prefect.deployments import Deployment

deployment = Deployment.build_from_flow(
    flow=log_flow,
    name="log-simple",
    parameters={"name": "Marvin"},
    infra_overrides={"env": {"PREFECT_LOGGING_LEVEL": "DEBUG"}},
    work_queue_name="test",
)

if __name__ == "__main__":
    deployment.apply()

All of the same configuration options apply here as well: you can skip automatic file uploads, apply and build in one step, etc.

Run a Prefect Orion server

For the remainder of this tutorial, you'll use a local Prefect Orion server. Open another terminal session and start the Prefect Orion server with the prefect orion start CLI command:

$ prefect orion start

 ___ ___ ___ ___ ___ ___ _____    ___  ___ ___ ___  _  _
| _ \ _ \ __| __| __/ __|_   _|  / _ \| _ \_ _/ _ \| \| |
|  _/   / _|| _|| _| (__  | |   | (_) |   /| | (_) | .` |
|_| |_|_\___|_| |___\___| |_|    \___/|_|_\___\___/|_|\_|

Configure Prefect to communicate with the server with:

    prefect config set PREFECT_API_URL=http://127.0.0.1:4200/api

View the API reference documentation at http://127.0.0.1:4200/docs

Check out the dashboard at http://127.0.0.1:4200



INFO:     Started server process [84832]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:4200 (Press CTRL+C to quit)

Set the PREFECT_API_URL for your server

Note the message to set PREFECT_API_URL — configuring the URL of your Prefect Orion server or Prefect Cloud makes sure that you're coordinating flows with the correct API instance.

Go to your first terminal session and run this command to set the API URL to point to the Prefect Orion instance you just started:

$ prefect config set PREFECT_API_URL=http://127.0.0.1:4200/api
Set variable 'PREFECT_API_URL' to 'http://127.0.0.1:4200/api'
Updated profile 'default'

Use profiles to switch between 'PREFECT_API_URL' settings

You can create configuration profiles to save commonly used settings.

# View current configuration
$ prefect config view
PREFECT_PROFILE='default'
PREFECT_API_URL='http://127.0.0.1:4200/api' (from profile)

# Create a "local" profile using these settings
$ prefect profile create local --from default

Created profile with properties:
    name - 'local'
    from name - default

Use created profile for future, subsequent commands:
    prefect profile use 'local'

Use created profile temporarily for a single command:
    prefect -p 'local' config view

Agents and work queues

As mentioned at the beginning of this tutorial, you still need two more items to run orchestrated deployments: an agent and a work queue. You'll set those up next.

Agents and work queues are the mechanisms by which Prefect orchestrates deployment flow runs in remote execution environments.

Work queues let you organize flow runs into queues for execution. Agents pick up work from one or more queues and execute the runs.

In the Prefect UI, you can create a work queue by selecting the Work Queues page, then creating a new work queue. However, in our case you don't need to manually create a work queue because it was created automatically when you created your deployment. If you hadn't created your deployment yet, it would be created when you start your agent.

Open an additional terminal session, then run the prefect agent start command, passing a -q test option that tells it to pull work from the test work queue.

$ prefect agent start -q test
Starting agent connected to http://127.0.0.1:4200/api...

  ___ ___ ___ ___ ___ ___ _____     _   ___ ___ _  _ _____
 | _ \ _ \ __| __| __/ __|_   _|   /_\ / __| __| \| |_   _|
 |  _/   / _|| _|| _| (__  | |    / _ \ (_ | _|| .` | | |
 |_| |_|_\___|_| |___\___| |_|   /_/ \_\___|___|_|\_| |_|


Agent started! Looking for work from queue(s): test...

Remember that:

  • We specified the test work queue when creating the deployment.
  • The agent is configured to pick up work from the test work queue, so it will execute flow runs from the log-flow/log-simple deployment (and any others that also point at this queue).

Run the deployment locally

Now that you've created the deployment, agent, and associated work queue, you can interact with it in multiple ways. For example, you can use the Prefect CLI to run a local flow run for the deployment.

$ prefect deployment run log-flow/log-simple
Created flow run 'talented-jackdaw' (b0ba3195-912d-4a2f-8645-d939747655c3)

If you switch over to the terminal session where your agent is running, you'll see that the agent picked up the flow run and executed it. Recall that we set an environment variable that configures the Prefect logging level for runs of this deployment.

12:34:34.000 | INFO    | prefect.agent - Submitting flow run '8476a5e7-cb8f-4fa0-911b-883ec289dccc'
12:34:34.060 | INFO    | prefect.infrastructure.process - Opening process 'daffodil-vole'...
12:34:34.068 | INFO    | prefect.agent - Completed submission of flow run '8476a5e7-cb8f-4fa0-911b-883ec289dccc'
12:34:35.619 | DEBUG   | prefect.client - Connecting to API at http://127.0.0.1:4200/api/
12:34:35.657 | DEBUG   | Flow run 'daffodil-vole' - Loading flow for deployment 'log-simple'...
12:34:35.681 | DEBUG   | Flow run 'daffodil-vole' - Starting 'ConcurrentTaskRunner'; submitted tasks will be run concurrently...
12:34:35.681 | DEBUG   | prefect.task_runner.concurrent - Starting task runner...
12:34:35.688 | DEBUG   | prefect.client - Connecting to API at http://127.0.0.1:4200/api/
12:34:35.797 | DEBUG   | Flow run 'daffodil-vole' - Executing flow 'log-flow' for flow run 'daffodil-vole'...
12:34:35.797 | DEBUG   | Flow run 'daffodil-vole' - Beginning execution...
12:34:35.821 | INFO    | Flow run 'daffodil-vole' - Created task run 'log_task-99465d2b-0' for task 'log_task'
12:34:35.821 | INFO    | Flow run 'daffodil-vole' - Executing 'log_task-99465d2b-0' immediately...
12:34:35.856 | DEBUG   | Task run 'log_task-99465d2b-0' - Beginning execution...
12:34:35.857 | INFO    | Task run 'log_task-99465d2b-0' - Hello Marvin!
12:34:35.857 | INFO    | Task run 'log_task-99465d2b-0' - Prefect Version = 2.3.1 ๐Ÿš€
12:34:35.857 | DEBUG   | Task run 'log_task-99465d2b-0' - Hello from another file
12:34:35.887 | INFO    | Task run 'log_task-99465d2b-0' - Finished in state Completed()
12:34:35.914 | DEBUG   | prefect.task_runner.concurrent - Shutting down task runner...
12:34:35.914 | INFO    | Flow run 'daffodil-vole' - Finished in state Completed('All states completed.')
12:34:35.921 | DEBUG   | prefect.client - Connecting to API at http://127.0.0.1:4200/api/
12:34:36.149 | INFO    | prefect.infrastructure.process - Process 'daffodil-vole' exited cleanly.

Note that we referenced the deployment by name in the format "flow_name/deployment_name". When you create new deployments in the future, remember that while a flow may be referenced by multiple deployments, each deployment must have a unique name.

You can also see your flow in the Prefect UI. Open the Prefect UI at http://127.0.0.1:4200/. You'll see your deployment's flow run in the UI.

Deployment flow run on the Flow Runs page of the Prefect UI

Run a deployment from the UI

With a work queue and agent in place, you can also create a flow run for log_simple directly from the UI.

In the Prefect UI, select the Deployments page. You'll see a list of all deployments that have been created in this Prefect Orion instance.

The Deployments page displays a list of deployments created in Prefect

Now select log-flow/log-simple to see details for the deployment you just created.

Viewing details of a single deployment

Select Parameters to see the default parameters you specified in the deployment definition.

Viewing deployment parameters

You can start a flow run for this deployment from the UI by selecting the Run button, which gives you options to:

  • Create a flow run with the default settings
  • Create a flow run with custom settings

Deployment run options in the UI

If you choose a Custom flow run, you can configure details including:

  • Flow run name
  • A description of the run
  • Tags
  • Scheduled start time
  • Custom parameters

Configuring custom flow run settings

Let's change the name parameter for the next flow run. Under Parameters, select Custom.

Change the value for the name parameter to some other value. We used "Trillian".

Configuring custom flow run settings

Select Save to save any changed values, then select Run to create the custom flow run.

The Prefect Orion engine routes the flow run request to the work queue, the agent picks up the new work from the queue and initiates the flow run.

As before, the flow run will be picked up by the agent, and you should be able to see it run in the agent process.

12:37:50.045 | INFO    | prefect.agent - Submitting flow run '9ff2a05c-2dfd-4b27-a67b-b71fea06d12f'
12:37:50.108 | INFO    | prefect.infrastructure.process - Opening process 'xi19-campor-g'...
12:37:50.117 | INFO    | prefect.agent - Completed submission of flow run '9ff2a05c-2dfd-4b27-a67b-b71fea06d12f'
12:37:52.241 | DEBUG   | prefect.client - Connecting to API at http://127.0.0.1:4200/api/
12:37:52.281 | DEBUG   | Flow run 'xi19-campor-g' - Loading flow for deployment 'log-simple'...
12:37:52.303 | DEBUG   | Flow run 'xi19-campor-g' - Starting 'ConcurrentTaskRunner'; submitted tasks will be run concurrently...
12:37:52.304 | DEBUG   | prefect.task_runner.concurrent - Starting task runner...
12:37:52.312 | DEBUG   | prefect.client - Connecting to API at http://127.0.0.1:4200/api/
12:37:52.427 | DEBUG   | Flow run 'xi19-campor-g' - Executing flow 'log-flow' for flow run 'xi19-campor-g'...
12:37:52.427 | DEBUG   | Flow run 'xi19-campor-g' - Beginning execution...
12:37:52.456 | INFO    | Flow run 'xi19-campor-g' - Created task run 'log_task-99465d2b-0' for task 'log_task'
12:37:52.456 | INFO    | Flow run 'xi19-campor-g' - Executing 'log_task-99465d2b-0' immediately...
12:37:52.485 | DEBUG   | Task run 'log_task-99465d2b-0' - Beginning execution...
12:37:52.486 | INFO    | Task run 'log_task-99465d2b-0' - Hello Trillian!
12:37:52.487 | INFO    | Task run 'log_task-99465d2b-0' - Prefect Version = 2.3.1 ๐Ÿš€
12:37:52.487 | DEBUG   | Task run 'log_task-99465d2b-0' - Hello from another file
12:37:52.523 | INFO    | Task run 'log_task-99465d2b-0' - Finished in state Completed()
12:37:52.551 | DEBUG   | prefect.task_runner.concurrent - Shutting down task runner...
12:37:52.551 | INFO    | Flow run 'xi19-campor-g' - Finished in state Completed('All states completed.')
12:37:52.558 | DEBUG   | prefect.client - Connecting to API at http://127.0.0.1:4200/api/
12:37:52.792 | INFO    | prefect.infrastructure.process - Process 'xi19-campor-g' exited cleanly.

Go back the Flow Runs page in the UI and you'll see the flow run you just initiatied ran and was observed by the API.

The deployment flow run is shown in the UI run history

Select the flow run to see details. In the flow run logs, you can see that the flow run logged a "Hello Trillian!" message as expected.

The flow run logs show the expected Hello Trillian! log message

Next steps

So far you've seen a simple example of a single deployment for a single flow. But a common and useful pattern is to create multiple deployments for a flow. By using tags, parameters, and schedules effectively, you can have a single flow definition that serves multiple purposes or can be configured to run in different environments.

Cleaning up

You're welcome to leave the work queue and agent running to experiment and to handle local development.

To terminate the agent, simply go to the terminal session where it's running and end the process with either Ctrl+C or by terminating the terminal session.

You can pause or delete a work queue on the Prefect UI Work Queues page.

Next steps: Storage and infrastructure

Deployments get interesting when you can execute flow runs in environments other than your local machine. To do that, you'll need to configure Storage and Infrastructure, which is covered in our next tutorial.