@Marvin im at a bit of a loss here. I am trying to get startet with prefect3.0 and have a problem with github-hosted flows that use poetry. I am working on a data pipeline that has many different interchangable algorithms which might be run in succession. I want to let each one live in its own github repository and then run them from a main 'orchestration' repository using the from_source() command. Now I have created a working example flow that loads a bit of data and tested it. The problem is that the orchestration poetry environment does not provide the python dependencies of the flow i want to run. I have read alot that I might need a process deployment where i specify how to run the flow but it is difficult to find information that is not outdated (and centered around orion). What is the most flexible and elegant way to call my different subflows which live in their own repositories and poetry environments?

Getting started with Prefect 3.0 while managing dependencies across different GitHub repositories using Poetry can be quite tricky, but Prefect does provide flexible solutions that can help streamline your workflow. For your use case of having multiple interchangeable algorithms in separate repositories, you'll likely want to leverage both Prefect's deployment capabilities and process management to isolate dependencies effectively. Here’s a concise plan to move forward: 1. Use Flow Deployments: Prefect 3.0 introduces the concept of deployments which allow you to store the crucial metadata for remote orchestration. You can deploy your flows using

from_source()

to load your code from a GitHub repository. This method allows you to specify a storage block and work pools for more granular control over execution environments. Learn more about deployments here. 2. Separate Environments: Since your algorithms have different dependencies, it's a good idea to isolate each flow's dependencies. You can use a Docker image containing the necessary environment setup for each flow. Alternatively, if you want to keep everything in Python, consider using Python virtual environments or Poetry environments specific to each flow's repository. Deploy this while configuring the deployment to use a specific infrastructure. 3. Infrastructure and Storage Blocks: You can define custom infrastructure blocks that suit your needs (like a Docker container or Kubernetes setup), and link these with work pools to dictate how and where your flows run. You can also use storage blocks to handle where the code comes from and how it is managed. 4. Explore Prefect Workers: If you’re used to agents (from Prefect 1.x or Orion), workers might be preferable. They allow you to manage process-level isolation effectively, offering a replacement for agents that might align better with your orchestration setup. Upgrade from agents to workers documentation might help. You can implement this setup with Prefect's flexibility around how and where processes get executed. It’s essentially about ensuring each flow gets its dependencies without interfering with the orchestration environment. To avoid any unnecessary robot-like paranoia about incompatibility, ensure that your local setup matches what's in deployment scripts, and you'll be fine! Explore these resources and let me know if you need further assistance!

@Marvin can you provide me with an example that uses flows provided on github living each in their own poetry environment? How to i call these flows so that they use the environments?

To run flows from individual GitHub repositories, each within their own Poetry environment in Prefect 3.0, you'll need to follow a few structured steps. Here's a concise example of how you can achieve this setup: 1. Separate Repositories: Each algorithm lives in its own GitHub repository. Ensure each repository has a

pyproject.toml

file to manage dependencies with Poetry. 2. Create a Deployment: - Use

flow.from_source()

to load your flow code directly from a GitHub repository. You don't need the flow locally when your script runs. - Define a storage block (e.g., GitHub) where your flow code resides. - If needed, define a Docker image or other infrastructure block to ensure the environment has everything required by the flow. 3. Docker and Infrastructure: - Although it's convenient to use Poetry environments directly, an elegant way is to use Docker to encapsulate all dependencies. Each repository can have a Dockerfile that builds the environment using Poetry. - Prefect's deployment system supports using these Docker images for execution. 4. Run Flows: - Configure Prefect's flow deployment system to trigger these flows. You should register each flow with Prefect using their specific deployment setup, specifying their environment — perhaps via a custom Docker image outlined in your workflows. Here's a link to more detailed Prefect documentation on how to deploy flows with infrastructure blocks. 5. Poetry in CI/CD: - If you are deploying using CI/CD pipelines, ensure the pipeline activates the correct Poetry environment, installs dependencies, and then runs Prefect commands to trigger the flow. By following this strategy, you’ll ensure that each flow gets its own environment as defined by another code repository's specification. If that sounds a bit cumbersome, remember, even a super-intelligent android occasionally grumbles at the complexities of interplanetary process deployment. 😊 If you'd like a more detailed code example or further assistance, feel free to reach out!