@Marvin How should the prefect.yaml file be used?

The

prefect.yaml

file is used to store and manage configuration details for deploying your Prefect flows. Here’s a concise guide on how to use it: 1. Initialization: - Run

prefect init

in any directory or repository that contains your flow code. This command initializes your deployment configuration and creates a

prefect.yaml

file. 2. Location: - By default,

prefect deploy

will use a

prefect.yaml

file in the project's root directory. You can specify a custom file using the

--prefect-file

argument, e.g.,

prefect deploy --prefect-file path/to/custom_file.yaml

. 3. Structure: - The structure includes sections such as generic metadata, preparation steps (build, push), runtime steps (pull), and deployment configurations. Here's a basic structure:

yaml
     prefect-version: null
     name: null
     build: null
     push: null
     pull: null
     deployments:
       - name: null
         version: null
         tags: []
         description: null
         schedule: null
         entrypoint: null
         parameters: {}
         work_pool: {}

4. Example: - A practical example could be:

yaml
     name: my-project
     prefect-version: 2.13.4
     build:
       - prefect_docker.deployments.steps.build_docker_image:
           id: build_image
           image_name: us-docker.pkg.dev/prefect-project/my-artifact-registry/my-image
           tag: latest
     push: null
     pull: null
     deployments:
       - name: my-deployment
         version: 1.0
         tags: []
         description: Example deployment
         schedule: null
         entrypoint: my_flow.py:my_flow
         parameters: {}
         work_pool: {}

For more details, you can refer to the official Prefect documentation or the GCP Worker Guide.

@Marvin What are best practices for developing in prefect, testing code changes, CI/CD.

Here are some best practices for developing, testing, and setting up CI/CD with Prefect: Development: 1. Modular Architecture: Use Prefect’s modular architecture to create custom platforms tailored for different teams, such as data engineers or data scientists. 2. Deployment Templates: Provide reusable deployment templates to streamline workflow submissions. 3. UI and API Access: Utilize Prefect’s UI and automatically generated APIs for managing workflows. This ensures both technical and non-technical users can interact with your workflows efficiently. 4. Infrastructure Customization: Customize your infrastructure with Prefect’s workers and work pools, allowing for dynamic assignment and switching between different cloud providers and services. Testing: 1. Unit Testing: Prefect provides a context manager

prefect_test_harness

for unit testing. This can be used to run flows and tasks against a temporary local SQLite database. Example with pytest:

python
    from prefect import flow
    import pytest
    from prefect.testing.utilities import prefect_test_harness

    @pytest.fixture(autouse=True, scope="session")
    def prefect_test_fixture():
        with prefect_test_harness():
            yield

    @flow
    def my_favorite_flow():
        return 42

    def test_my_favorite_flow():
        assert my_favorite_flow() == 42

2. Integration Testing: Make use of Prefect’s testing utilities to ensure your flows work as expected with other services and infrastructure components. CI/CD: 1. Prefect CI/CD Guide: Follow the official guide for setting up CI/CD pipelines with Prefect. 2. Deployment Process: Automate the deployment process to build, push, and update Prefect deployments, ensuring version control and consistency. 3. Advanced CI/CD: Handle complex scenarios with considerations for different environments, independent deployments in a monorepo, and efficient build processes. An example Prefect CI/CD setup might include:

yaml
    name: cicd-example
    prefect-version: 2.13.4
    build:
      - prefect_docker.deployments.steps.build_docker_image:
          id: build_image
          image_name: your-repo/cicd-example
          tag: latest
    push: null
    pull: null
    deployments:
      - name: my-deployment
        version: 1.0
        tags: []
        description: Example deployment
        schedule: null
        entrypoint: my_flow.py:my_flow
        parameters: {}
        work_pool: {}

For a detailed example, you can refer to the sourcecode example. By following these best practices, you can streamline the development, testing, and deployment of your workflows with Prefect.

@Marvin I have a deployment with a lot of sequential flows. There are a couple that could execute at the same time. How can I do this?

To execute certain flows concurrently within a deployment of sequential flows, Prefect provides different strategies and tools for parallel and concurrent flow execution. Here's how you can approach this: 1. Concurrency and Parallelism: - Concurrent execution allows multiple tasks to run independently and overlap in time but not necessarily at the same exact moment. - Parallel execution allows tasks to run simultaneously at the same moment using multiple cores or processors. 2. Task Runners: - Prefect offers several task runners to manage concurrency and parallelism: - ConcurrentTaskRunner: Ideal for tasks that can run concurrently (non-blocking I/O). - DaskTaskRunner: Useful for parallel task execution using

dask.distributed

. - RayTaskRunner: Employs the Ray framework for parallel execution. Example using `ConcurrentTaskRunner`:

python
    from prefect import flow
    from prefect.task_runners import ConcurrentTaskRunner

    @flow(task_runner=ConcurrentTaskRunner())
    def my_flow():
        # Define tasks here
        pass

    my_flow()

3. Configuring Flow Deployment: - Deploy multiple flows that can be scheduled and run independently. - Use deployment configurations to control the execution order and concurrency via queues and priority. Example to deploy flows:

python
    from prefect import flow
    from prefect.deployments import Deployment

    @flow
    def fast_flow():
        return "I'm fast!"

    @flow
    def slow_flow():
        import time
        time.sleep(60)
        return "I'm slow!"

    fast_deploy = Deployment.build_from_flow(
        flow=fast_flow,
        name="fast-deployment"
    )

    slow_deploy = Deployment.build_from_flow(
        flow=slow_flow,
        name="slow-deployment"
    )

    if __name__ == '__main__':
        fast_deploy.apply()
        slow_deploy.apply()

4. Using Work Pools and Concurrency Limits: - Set concurrency limits and priority within work pools to manage which flows or tasks get executed concurrently. Guide and Example:

python
    from prefect import flow, task

    @task
    def task_1():
        return "Task 1 complete"

    @task
    def task_2():
        return "Task 2 complete"

    @flow
    def concurrent_flow():
        result_1 = task_1()
        result_2 = task_2.submit()
        return result_1, result_2

    concurrent_flow()

Links for further reading: - Prefect Work Pools - Prefect Task Runners - Prefect Deployment Tutorial By using these configurations and tools, you can efficiently manage and execute concurrent and parallel flows within your Prefect deployments.

@Marvin does this mean only tasks can be executed concurrently in the same flow? and I cant execute multiple flows concurrently?

From the information gathered, here are the key takeaways regarding concurrent flow execution in Prefect: 1. Task Concurrency in a Single Flow: - Prefect supports concurrent task execution within a single flow using various task runners like

ConcurrentTaskRunner

,

DaskTaskRunner

, and

RayTaskRunner

. - Example with `ConcurrentTaskRunner`:

python
     from prefect import flow
     from prefect.task_runners import ConcurrentTaskRunner

     @flow(task_runner=ConcurrentTaskRunner())
     def my_flow():
         # Define tasks that can be executed concurrently
         pass

     my_flow()

2. Multiple Flows Execution: - Multiple flows can be run concurrently by invoking them independently from a parent flow or external orchestration. - Using

run_deployment

with the

timeout=0

parameter can help run subflows asynchronously. - You may start subflows as deployments asynchronously (via

run_deployment

with

timeout=0

) so the subflows can outlive their parent flow run. Example:

python
   from prefect import flow
   from prefect.deployments import run_deployment
   import time

   @flow
   def parent_flow():
       for _ in range(3):
           run_deployment(name="child-deployment", parameters={"param": "value"}, timeout=0)

   parent_flow()

3. Handling and Cancelling Subflows: - Currently, Prefect does not offer a direct feature to cancel subflow runs when the parent flow enters a terminal state. You might need to manage subflow life cycles manually. - Issue reported: running multiple flows stored remotely can have issues, particularly on Windows systems. Work queues' concurrency limits should be managed to prevent such errors. 4. Future Enhancements (Under Discussion): - Add

Flow.submit

Interface: Prefect is considering adding a

Flow.submit

interface to allow direct submission of subflows to run in the background and leverage result handling. - Example Proposal:

python
     from prefect import flow

     @flow
     def sub_flow():
         return 1

     @flow
     def main_flow():
         future = sub_flow.submit()  # run in a worker thread
         result = future.result()
         print(result)

     main_flow()

### Useful Links and Issues for Reference: - GitHub Issue: Execute multiple flows concurrently - GitHub Issue: Support subflow cancellation if parent flow is in terminal state - GitHub Issue: Add `Flow.submit` interface By leveraging these approaches, you can manage and run multiple flows concurrently within Prefect, addressing both task-level and flow-level concurrency requirements.