Let me provide you with a practical guide on how to share code across multiple Prefect deployments. Here are the recommended approaches: 1. Create a Python Package This is the most robust and recommended approach:

plaintext
your_project/
├── shared_utils/
│   ├── __init__.py
│   ├── common_functions.py
│   └── helpers.py
├── flows/
│   ├── flow1.py
│   └── flow2.py
└── setup.py

2. Install Your Package Create a `setup.py`:

python
from setuptools import setup, find_packages

setup(
    name="your-shared-utils",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[
        "prefect>=3.0.0"
        # other dependencies
    ]
)

3. Use in Your Flows Example of how to use shared code:

python
# shared_utils/common_functions.py
from prefect import task

@task
def shared_task():
    return "I'm a shared task!"

# flows/flow1.py
from prefect import flow
from shared_utils.common_functions import shared_task

@flow
def my_flow():
    result = shared_task()
    return result

4. Deployment Options There are several ways to ensure your shared code is available in deployments: a. Git Repository Approach (Recommended):

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def my_flow():
    ...

if __name__ == "__main__":
    flow.from_source(
        source="<https://github.com/your-org/your-repo>",
        entrypoint="flows/flow1.py"
    ).deploy(
        name="my-deployment",
        work_pool_name="my-pool"
    )

b. Requirements File Approach: Create a

requirements.txt

that includes your package:

plaintext
git+<https://github.com/your-org/your-shared-utils.git>

Best Practices: 1. Use Absolute Imports: Always use absolute imports rather than relative imports to maintain clarity and prevent issues. 2. Version Control: Keep your shared code in version control and use specific versions in your dependencies. 3. Documentation: Document your shared functions well, especially if they'll be used across teams. 4. Testing: Include tests for your shared code to ensure reliability across different deployments. 5. Dependencies: Be explicit about dependencies in your shared package's

setup.py

or

requirements.txt

. Important Notes: - When using Git-based deployments, make sure your shared code repository is accessible to your Prefect workers. - Consider using semantic versioning for your shared package to manage updates and compatibility. - If your shared code changes frequently, you might want to use branch or commit references in your requirements. Would you like me to elaborate on any of these approaches or provide more specific examples for your use case?

@Marvin how to create functions (or python module) shared across multiple deployments? when i change my package, does it change all existing deployments to use the newer version of packages? Do i need to redeploy all deployments?

Let me break this down into two parts: first, how to create and share packages, and second, how updates work and what you need to do when making changes. ### Part 1: Creating Shared Packages Here's a practical setup for shared code:

plaintext
your_project/
├── shared_utils/
│   ├── __init__.py
│   ├── common_functions.py
│   └── helpers.py
├── flows/
│   ├── flow1.py
│   └── flow2.py
├── setup.py
└── prefect.yaml

1. Package Setup

python
# setup.py
from setuptools import setup, find_packages

setup(
    name="your-shared-utils",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[
        "prefect>=3.0.0"
        # other dependencies
    ]
)

2. Example Shared Code

python
# shared_utils/common_functions.py
from prefect import task

@task
def shared_task():
    return "I'm a shared task!"

3. Using in Flows

python
# flows/flow1.py
from prefect import flow
from shared_utils.common_functions import shared_task

@flow
def my_flow():
    result = shared_task()
    return result

### Part 2: Managing Updates Important: Changes to your package do NOT automatically propagate to existing deployments. Here's what you need to know: 1. Package Versioning When a deployment runs, it uses: - The package version specified in the deployment configuration - The latest version from the specified Git branch/commit if using Git-based deployments - The version installed in the worker's environment if using a local package 2. Update Strategies a. Git-based deployments (Recommended):

python
# deployment.py
from prefect import flow

@flow
def my_flow():
    ...

if __name__ == "__main__":
    flow.from_source(
        source="<https://github.com/your-org/your-repo>",
        entrypoint="flows/flow1.py",
        reference="main"  # specify branch or commit
    ).deploy(
        name="my-deployment",
        work_pool_name="my-pool"
    )

b. Using prefect.yaml:

yaml
# prefect.yaml
deployments:
  - name: my-deployment
    version: null  # will auto-increment
    tags: []
    description: null
    schedule: {}
    flow_name: my_flow
    entrypoint: flows/flow1.py:my_flow
    parameters: {}
    work_pool:
      name: my-pool
      work_queue_name: null
    dependencies:
      - git+<https://github.com/your-org/your-shared-utils.git@v0.1.0>

3. When You Make Changes YES, you need to redeploy deployments when you want them to use the new version of your package. Here's what to do: a. For Git-based deployments: - Update your code and push to Git - Redeploy each deployment that should use the new version:

bash
prefect deploy

b. For package-based deployments: - Update your package version in setup.py - Rebuild and republish your package - Update the package version in your deployment configuration - Redeploy each deployment Best Practices for Managing Updates: 1. Version Control: - Use semantic versioning for your package - Tag releases in Git - Specify exact versions in deployment dependencies 2. Deployment Organization: - Group related deployments in the same prefect.yaml - Use consistent naming conventions - Document which deployments use which shared packages 3. Update Automation:

python
# update_deployments.py
from prefect.deployments import Deployment
from prefect.client import get_client

async def update_all_deployments():
    async with get_client() as client:
        deployments = await client.read_deployments()
        for deployment in deployments:
            if "your-shared-utils" in deployment.dependencies:
                # Update deployment
                await Deployment.load(f"{deployment.name}/{deployment.flow_name}")
                await deployment.apply()

4. Testing Updates: - Test changes in a staging environment first - Consider maintaining multiple versions if backwards compatibility is needed - Use deployment tags to identify which deployments use shared code Remember:

- Each deployment update creates a new version - Old deployment versions are preserved and can be used for rollback - Workers need access to the updated package (either through Git or package installation) - Consider using deployment parameters for configuration instead of hardcoding values in shared code Would you like me to elaborate on any of these aspects or provide more specific examples for your use case?