@Marvin, I have a flow that I am pulling from minio and running by deploying it in a work pool. How do I get it to run in a virtual environment?

To help you with setting up a Prefect flow to run in a virtual environment, I need to confirm the version of Prefect you are using. Prefect's configuration and deployment strategies can differ between versions 2.x and 3.x. Could you please let me know which version of Prefect you are using? If you're unsure, you can try running

prefect version

in your command line to check.

@Marvin 3

To run your Prefect 3.x flow in a virtual environment, you can make use of the work pool and deployment features, specifically by utilizing job variables and pull steps such as

pip_install_requirements

for managing your environment and dependencies. Here’s a general approach: 1. Work Pool Configuration: You need to configure your work pool to manage the execution environment. For Prefect, this might include specifying a base Python environment or Docker container. You can define job variables in your deployment configuration for more control. 2. Define Deployment Steps: Prefect allows you to define pull steps in your deployment configuration. You can use pull steps to set up your environment. For example, use the

pip_install_requirements

step to install necessary packages. 3. Handle Dependencies: Ensure that the required dependencies are listed in a

requirements.txt

file. Use the pull steps to install these dependencies in your virtual environment. 4. Troubleshooting: In case of issues like

ModuleNotFoundError

when using local modules, check if these modules are properly listed and referenced in your

requirements.txt

. You might also need to ensure that local paths and version management align with your deployment setup (as discussed in GitHub issues). Check out these documents that may provide additional guidance: - Prefect Deployment Documentation - Work Pools in Prefect For issues specific to module imports or path errors, this could be helpful: - ModuleNotFoundError Issue on GitHub These resources should help you set up your Prefect flow to run within a virtual environment effectively. If you run into specific errors, feel free to provide more details so I can assist further.

@Marvin my python job configuration looks like this:

from prefect import flow
from prefect.filesystems import RemoteFileSystem

if __name__ == "__main__":
    remote_file_system_block = RemoteFileSystem.load("minio-flows")

    flow.from_source(
        source=remote_file_system_block,
        entrypoint="spotify_data_pull.py:run"
    ).deploy(
        name="minio-spotify-deployment",
        work_pool_name="spotify_data_pull",
        cron="0 * * * *")

Would the virtual environment set up go into this python job?

The setup of a virtual environment for your Prefect flow deployment would not be directly specified within the Python script you shared. Instead, it usually involves the configuration of your Prefect deployment, especially if you're using a work pool for execution. However, you can achieve the setup of the environment through Prefect deployment configurations and job variables. Here are the steps to integrate it properly: 1. Deployment Configuration: Ensure that your Prefect deployment YAML or equivalent configuration file specifies the environment setup. If you're installing dependencies using

pip

, you'll typically define this in a

requirements.txt

file. 2. Work Pool and Pull Steps: Within the Prefect deployment, use pull steps to set up the virtual environment by specifying dependencies. Here's how you might structure your configuration:

yaml
   deployments:
     - name: minio-spotify-deployment
       work_pool:
         name: spotify_data_pull
         job_variables:
           # Example Docker image or other environment setups if needed
           # e.g., environment: { BASE_IMAGE: "python:3.9" }
       pull:
         - prefect.deployments.steps.git_clone:
             repository: <your-repo-url>
             branch: <branch-name>
         - prefect.deployments.steps.pip_install_requirements:
             requirements_file: requirements.txt
             directory: <path-where-requirements-exist>

3. Environment Specification: If you want to use a specific Docker image or have any other environment settings, specify them in the

job_variables

section of the deployment. 4. Runtime Environment: Specify any runtime environment variables or configurations your flow might need in the

job_variables

or

environment

. Refer to the Prefect Deployment Documentation for more detailed guidance on setting up deployments, including environment management. By following this approach, you ensure the virtual environment setup is part of your deployment process, rather than the Python script itself. Let me know if you need further clarification on any of these steps!

hi @Jonny what type of work pool are you using? you can define pull steps for your deployment either way, but if you're using a containerized work pool type, then I would recommend installing your deps in an image that you can specify

Hi @Nate, I am using a "process" work pool

gotcha! so there’s different ways you can attack it but ultimately wherever you ran

prefect worker start

that process will need all the python deps that your scheduled flows need. so if you want to pip install stuff in the same venv as your worker, you can. but this can become a pain if you want to run a bunch of different deployments with different deps on the same worker. so there are a couple common alternate paths • so the worker itself doesn’t need to know anything about the deps of the deployments it runs (each container can have its own deps) • use a

prefect.yaml

to define your deployment (instead of

from_source

/

deploy

) similar to how marvin demoed above and use the run_shell_script step to do whatever setup you need (activate a venv, pip install things etc) - examples here • lastly if you’re feeling adventurous you can write a worker that behaves how you want like this . for example, this one uses uv to run flows in an ephemeral python environment (which might fit your use case well, but is not a standard prefect worker, i just wrote it :) ) but all of those alternatives are just if you think you’ll run into problems running all your flows in the same python venv, if you think that will be fine then it’s just

source .venv/bin/activate
prefect worker start ... (etc)

Wow, thank you for the in depth reply! I actually am already running my worker in a docker container. So if I just pip install my requirements.txt file in that container it will all work out? I didn't realise the environment was attached to the worker!

yes! if you pip install your requirements in your container and restart your worker, it should work. but part of the reason for my lengthy reply🙂 is that i’ve seen people run into drift this way, where say they update to pandas==N+1 locally but forget to go reinstall their deps in their container, then their deployment runs breaks. so either docker or being clever with pull steps / uv solve that

Oh they're definitely going to change a lot! But I'm just starting using docker, linux and prefect so I'm just aiming for the fastest way to see results atm and then I can work on making it all more stable! I think if I can get this working I'll give the yaml version a go next

sounds good! that video i linked in my earlier message might be a good resource. i have a short series where i • start from vanilla python • use serve (easy mode process worker) • use a process worker • use a docker worker but feel free to ask any questions along the way!