< Marvin> In Prefect 2 20 am deploying a few flows with `pre Prefect Community #ask-marvin

<@ULVA73B9P> In Prefect 2.20 am deploying a few fl...

merlin

01/09/2025, 4:46 PM

@Marvin In Prefect 2.20 am deploying a few flows with

prefect deploy --all --prefect-file prefect.yaml

, the yaml file configures push/pull steps with s3 code storage. There are 3 deployments in the file, and for each deployment prefect is doing

> Running push_to_s3 step...

. 1. Why is Prefect pushing the project code to s3 for every deployment? The yaml file has a push and pull section that applies to all, I think there is only the need to push to s3 one time. 2. Why is the push step so slow? The total size of the code is 100 files, about 300 KB.

Marvin

01/09/2025, 4:46 PM

1. Why is Prefect pushing to S3 for every deployment? Prefect's deployment mechanism, as configured in your

prefect.yaml

, executes the push step for each deployment. This behavior is due to the way Prefect handles deployments; each deployment is treated as an independent entity, and thus, it executes all specified steps, including pushing to S3. This ensures that any changes in the codebase are captured and available for each deployment. You can refer to the Prefect storage guide for more details. 2. Why is the push step slow? The slowness of the push step could be attributed to several factors, such as network latency or the configuration of your S3 bucket. The size of the code (100 files, about 300 KB) should not inherently cause slowness. It might be worth checking your network connection and the performance of the S3 bucket. Additionally, there might be overhead in the way Prefect packages and uploads the files. Unfortunately, there are no specific issues reported about this in the Prefect GitHub repository that directly address your concern. For further exploration, you might consider checking if there are any network-related issues or experimenting with different S3 configurations. If this continues to be a significant issue, it might be worth raising a new issue on Prefect's GitHub to get more insights from the community and maintainers.

merlin

01/09/2025, 6:10 PM

@Marvin That is very helpful discussion on both points. So for the deployment yaml configuration I would separate deployments into separate files, that way I can deploy and push code only for specific deployments that have changed. Is there any CICD or configuration that you know of that can trigger a deploy only when the code is changed for specific files/folders? Or, consider this: All my deployments are based on the same code and same execution environment. What is the best configuration to make deployment a simpler process? I moved away from git code storage because it requires VPN access at my company which is a friction point.

Marvin

01/09/2025, 6:10 PM

To streamline your deployment process using CI/CD and ensure deployments only occur when code changes, you can consider the following strategies: 1. CI/CD Trigger on Code Changes: Prefect can be integrated with CI/CD pipelines to automate deployments based on code changes. For instance, using GitHub Actions, you can configure workflows to trigger deployments only when specific files or directories change. This can be done using the

paths

condition in a GitHub Actions workflow. Here's a simplified example:

Copy code

yaml
   on:
     push:
       branches:
         - main
       paths:
         - "src/**"
         - "config/**"

This configuration triggers the workflow only when changes occur in the specified paths. More details can be found in the Prefect CI/CD guide. 2. Simplifying Deployment for Shared Code: Since all your deployments are based on the same code and environment, consider using a single deployment configuration with parameterized flows. This allows you to maintain a single source of truth for your code while varying behavior through parameters. Prefect's deployment interface supports this through the

flow.to_deployment

method, which can create deployment objects that share the same Python environment. 3. Avoiding Git Storage: Since VPN access is a friction point, using S3 or another cloud storage option remains viable. Ensure that your CI/CD pipeline has appropriate permissions to access this storage, and consider using Prefect's automation features to handle deployments based on external triggers or schedules. These strategies can help you maintain a more efficient and responsive deployment process while minimizing unnecessary operations.

merlin

01/09/2025, 6:14 PM

@Marvin Very good. 1. I'm on gitlab, can you share the CICD example for gitlab? Please include the prefect command, ie

prefect deploy --all --prefect-file ./project/prefect.yaml

2. Explain the

<http://flow.to|flow.to>_deployment

interface. So I create a 'template' deployment, and this will parametrize a flow by name thereby defining the deployment at runtime? So in theory there is a single deployment, and the scheduled instances all have different parameters which include the name of the flow that is meant to be used? This is intriguing, is it a commonly used pattern?

Marvin

01/09/2025, 6:14 PM

1. GitLab CI/CD Example for Prefect Deployment: To automate deployments using GitLab CI/CD, you can configure your

.gitlab-ci.yml

file to execute Prefect commands based on changes. Here's a basic example:

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yaml
   stages:
     - deploy

   deploy_prefect:
     stage: deploy
     script:
       - prefect deploy --all --prefect-file ./project/prefect.yaml
     only:
       changes:
         - src/**/*
         - project/prefect.yaml

This configuration will trigger the

deploy_prefect

job whenever there are changes in the

src

directory or the

prefect.yaml

file, ensuring deployments are only executed when relevant code changes occur. 2. flow.to_deployment
Interface: The

flow.to_deployment

interface allows you to define and register deployments programmatically within your Python script. It creates a deployment object that can be parameterized and scheduled independently. Here's a brief overview: - Single Deployment with Multiple Parameters: You can create a 'template' deployment and run it with different parameters, allowing different scheduled instances to execute with varying configurations. This is useful for scenarios where you want to test different configurations or run the same flow with different inputs. - Commonly Used Pattern: This pattern is indeed common when you want to maintain a single deployment configuration but execute it under different conditions or with different data. It simplifies the management of deployments by reducing redundancy and leveraging parameterization for flexibility. For more details on how to use this interface, you can explore the Prefect deployments tutorial.

merlin

01/10/2025, 12:46 AM

@Marvin Thank you. A more general question about s3 code storage: when the flow code (files and directories) are copied to s3, what is the overwrite mode? If the same file exists is it overwritten? Will all existing files be removed before the 'push' step so that files not existing locally will also not exist on s3? Or will stale files continue to exist at the s3 code storage location if they no longer exist in the prefect project?

Marvin

01/10/2025, 12:46 AM

When using S3 for code storage in Prefect, the behavior during the

push_to_s3

step is as follows: - Overwrite Mode: The

push_to_s3

step will overwrite existing files in the S3 bucket with the same name. This means that if a file exists both locally and in the S3 bucket, the local version will replace the one in S3. - Stale Files: The

push_to_s3

operation does not automatically remove files from S3 that no longer exist in your local project directory. As such, any files that have been deleted locally will continue to exist in the S3 storage unless explicitly removed. - File Exclusion: You can specify files and directories to exclude from being uploaded by using an

ignore_file

, similar to a

.gitignore

. This behavior ensures that your S3 storage reflects the current state of your local project, but it requires manual intervention to clean up any stale files. For more details, you can refer to the Prefect AWS integration guide.

merlin

01/10/2025, 12:48 AM

@Marvin Show an example of defining a deployment in

prefect.yaml

that uses the

<http://flow.to|flow.to>_deployment

paradigm, where the deployment parametrizes the flow name along with arguments and parameters.

Marvin

01/10/2025, 12:48 AM

To define a deployment in

prefect.yaml

using the

flow.to_deployment

paradigm, where you can parametrize the flow name along with arguments and parameters, you can follow this structure:

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yaml
version: 2

deployments:
  - name: my-parametrized-deployment
    flow_name: my_flow
    parameters:
      name: "example_name"
      limit: 100
    work_pool:
      name: my-work-pool
    storage:
      type: s3
      bucket: my-bucket
      folder: my-folder
    infrastructure:
      type: docker-container
      image: my-registry/my-image:latest

### Explanation: - Flow Name and Parameters: The

flow_name

specifies the flow to be deployed, and

parameters

allow you to set the values for the flow's parameters. This way, you can change the behavior of the flow at runtime based on these parameters. - Work Pool and Infrastructure: The

work_pool

and

infrastructure

sections define where and how the flow will be executed. In this example, it uses a Docker container. - Storage Configuration: The

storage

section specifies that the flow code will be stored in an S3 bucket, ensuring that the flow code is accessible during execution. This configuration allows you to manage deployments dynamically by changing flow parameters without altering the underlying codebase, making it a flexible approach for managing multiple flow executions with varying configurations. For more details, you can refer to the Prefect deployment guide.

merlin

01/10/2025, 1:54 AM

@Marvin this doesn't look like it will work. Your deployment definition doesn't have an entrypoint. There is

my_flow

defined, what does that look like in python?

Marvin

01/10/2025, 1:54 AM

To correctly define a deployment in

prefect.yaml

using the

flow.to_deployment

paradigm, you need to specify an entrypoint for your flow. Here's how you can structure both the

prefect.yaml

file and the corresponding Python flow: ###

prefect.yaml

Example

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yaml
version: 2

deployments:
  - name: my-parametrized-deployment
    entrypoint: path/to/your_flow_file.py:my_flow
    parameters:
      name: "example_name"
      limit: 100
    work_pool:
      name: my-work-pool
    storage:
      type: s3
      bucket: my-bucket
      folder: my-folder
    infrastructure:
      type: docker-container
      image: my-registry/my-image:latest

### Python Flow Example

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python
from prefect import flow

@flow
def my_flow(name: str, limit: int = 100):
    print(f"Flow executed with name: {name} and limit: {limit}")

if __name__ == "__main__":
    deployment = my_flow.to_deployment(name="my-parametrized-deployment")
    deployment.apply()

### Explanation: - Entrypoint: The

entrypoint

in the

prefect.yaml

specifies the path to the Python file containing your flow and the flow function name (

path/to/your_flow_file.py:my_flow

). - Flow Definition: The

my_flow

function is defined with parameters (

name

and

limit

) that can be set in the deployment configuration. This allows you to dynamically change the behavior of your flow at runtime. - Deployment Application: In the Python script, you use

my_flow.to_deployment()

to create a deployment object, which can be applied to register the deployment with Prefect. This setup allows you to manage your deployments effectively, with the flexibility to adjust parameters and configurations as needed. For more information, you can explore the Prefect deployment tutorial.

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