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<@ULVA73B9P> is it possible to specify a different...
# ask-marvin
r
@Marvin is it possible to specify a different python interpreter when deploying a flow via 
prefect.yaml
on to a work pool of type 
process
? Something like this:
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deployments:
  - name: "my_workflow"
    entrypoint: "main.py:my_workflow"
    work_pool:
      name: green
      work_queue_name: low
    job_variables:
      command: |
        echo "About to activate" && . /opt/prefect/flows/demo1/activate.sh && /opt/prefect/flows/demo1/env/bin/python -m prefect.engine
      working_dir: "/opt/prefect/flows/demo1"
m
✅ thought for 68.6 seconds
Based on my research, I need to point out a few things about your question and example: 1. The syntax you're showing isn't quite correct for what you're trying to achieve. In Prefect 3.x, the process work pool doesn't directly support specifying a custom Python interpreter through 
job_variables
in the way you've shown. 2. However, there are a few ways you can achieve what you want: a. Environment Variables: You can set environment variables that affect the Python path:
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yaml
   deployments:
     - name: "my_workflow"
       entrypoint: "main.py:my_workflow"
       work_pool:
         name: green
         work_queue_name: low
       job_variables:
         env:
           PYTHONPATH: "/opt/prefect/flows/demo1/env/bin"
           VIRTUAL_ENV: "/opt/prefect/flows/demo1/env"
b. Virtual Environment Activation: The best practice would be to have your worker running in the correct virtual environment already, rather than trying to switch environments during flow execution. 3. The 
command
override you're attempting isn't supported for process work pools - this is more similar to how you might configure a Docker or Kubernetes work pool. Recommendations: 1. The most reliable approach is to run your Prefect worker itself using the desired Python interpreter:
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bash
   /opt/prefect/flows/demo1/env/bin/python -m prefect worker start -p "green"
2. If you need different Python environments for different flows, consider using separate work pools for each environment, each with its own worker running in the appropriate environment. 3. If you absolutely need to use different Python interpreters for different flows, you might want to consider using the Docker infrastructure type instead, which gives you more control over the execution environment. Would you like me to provide more details about any of these approaches or explain how to set up a Docker-based solution instead?
r
@Marvin unfortunately my projects mandates that I use work pools of type 
process
- can you expand on the first option, using env variables?
m
✅ thought for 37.4 seconds
Based on my research and understanding of Prefect's process work pools, here's how you can use environment variables to control the Python environment: 1. In your 
prefect.yaml
, you can specify environment variables that will be available to your flow during execution:
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yaml
deployments:
  - name: "my_workflow"
    entrypoint: "main.py:my_workflow"
    work_pool:
      name: green
      work_queue_name: low
    job_variables:
      env:
        # Path to your virtual environment's Python
        PATH: "/opt/prefect/flows/demo1/env/bin:${PATH}"
        # Virtual environment location
        VIRTUAL_ENV: "/opt/prefect/flows/demo1/env"
        # Additional Python-specific environment variables
        PYTHONPATH: "/opt/prefect/flows/demo1"
        # Any other environment variables your flow needs
        CUSTOM_VAR: "value"
2. Important considerations: - The environment variables are inherited by the process that runs your flow - The 
PATH
variable modification ensures your virtual environment's Python is found first - Setting 
VIRTUAL_ENV
helps some tools recognize the virtual environment context - You can add any additional environment variables your flow needs 3. Best practices: - Make sure the paths you specify actually exist on the machine where the worker will run - Consider using relative paths with a base directory if possible - Test the environment setup with a simple flow first to verify it's working as expected 4. Limitations: - This approach assumes the virtual environment is already created at the specified location - You'll need to ensure all required dependencies are installed in that virtual environment - The worker process needs appropriate permissions to access the specified paths Would you like me to provide an example of how to test this setup with a simple flow, or would you like more details about any specific aspect of this configuration?
r
@Marvin my deployment file now looks like this:
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deployments:
  - name: "my_workflow"
    entrypoint: "main.py:my_workflow"
    work_pool:
      name: green
      work_queue_name: low
    job_variables:
      env:
        PATH: "/opt/prefect/flows/demo1/env/bin:${PATH}"
        VIRTUAL_ENV: "/opt/prefect/flows/demo1/env"
        PYTHONPATH: "/opt/prefect/flows/demo1"
      working_dir: "/opt/prefect/flows/demo1"
I tried to run my sample flow and it still reports running from the same env as the worker
m
✅ thought for 50.4 seconds
Based on my research and the GitHub issues I found (particularly #18181 which discusses this exact limitation), I can now explain why your current configuration isn't working and what alternatives you have: 1. Current Limitation: Process work pools in Prefect 3.x currently execute flows using the same Python interpreter as the worker process. This is a known limitation, and there isn't a direct way to override this behavior through 
job_variables
alone. 2. Available Solutions: a. Run the Worker in the Correct Environment (Recommended): Instead of trying to change the environment during flow execution, run the worker itself in the desired virtual environment:
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bash
   /opt/prefect/flows/demo1/env/bin/python -m prefect worker start -p "green"
b. Use Multiple Workers: Run different workers (each in their own environment) and use different work pools for different environment requirements. c. Create a Wrapper Script: You could create a wrapper script that activates the virtual environment and then runs your flow. However, this is more complex and less reliable. 3. Why Your Current Config Isn't Working: - The 
PATH
and 
VIRTUAL_ENV
environment variables alone don't change which Python interpreter is used - The process work pool inherits the Python interpreter from the worker process - Environment variables are passed to the flow execution but don't affect the interpreter selection 4. Recommendation: Given your requirement to use process work pools, I recommend: 1. Create separate work pools for different Python environments if needed 2. Run each worker using the specific Python interpreter you want to use for your flows 3. Deploy your flows to the appropriate work pool based on their Python environment requirements Would you like me to provide more details about setting up multiple work pools or creating a specific worker configuration for your use case?
n
sorry @Ricardo Garcia Silva marvin is confabulating here
you were indeed on the right track to override 
command
in the job variables
r
@Nate yes, thankfully marvin provided me with stuff to look at with its mention of gh issue 18181 and I eventually landed on that repo, which is gold! I was able to somehow make it work, albeit in my case I am using 
pixi
to manage the environment - I'm in the process of automating the stuff now, but my manual attempts have been successful. Thanks for reaching out!
n
also check it out, I tested gpt 5 on your question and
Short answer: yes.
In Prefect 3.x, Process workers let you override the command they run via job_variables.command in your prefect.yaml. That’s exactly how you can point at a specific Python interpreter.
Two sane ways to do it:
1) Preferred: run the worker from the venv you want
- Start your worker using the venv’s prefect, then don’t override command at all:
- /opt/prefect/flows/demo1/env/bin/prefect worker start -p green -q low
- The subprocess the worker launches will use that environment’s Python by default.
2) Override the command in the deployment (what you sketched)
- job_variables.command is a string; the Process worker will use it instead of the auto-generated command.
- If you need to source an activation script (a shell builtin), wrap it in a shell:
- command: bash -lc 'echo "About to activate" && source /opt/prefect/flows/demo1/activate.sh && exec /opt/prefect/flows/demo1/env/bin/python -m prefect.engine'
A cleaned-up prefect.yaml example:
- deployments:
- name: my_workflow
entrypoint: main.py:my_workflow
work_pool:
name: green
work_queue_name: low
job_variables:
working_dir: /opt/prefect/flows/demo1
# Option A: override the command to a specific python
command: bash -lc 'source /opt/prefect/flows/demo1/activate.sh && exec /opt/prefect/flows/demo1/env/bin/python -m prefect.engine'
# Option B (instead of command): just put the venv bin first on PATH
# env:
# PATH: "/opt/prefect/flows/demo1/env/bin:{{ env.PATH }}"
Notes and caveats:
- Leaving command unset is recommended; Prefect generates the right engine command for you. If you override it, you own keeping it compatible across upgrades.
- The path you reference must exist on the machine where the Process worker is running. Process workers don’t magically ship your venv around.
- You can inspect the Process pool’s template (and see that command is a string variable) with:
- prefect work-pool get-default-base-job-template --type process
Docs and references:
- Customize job variables: https://docs-3.prefect.io/v3/how-to-guides/deployments/customize-job-variables
- Process worker API (ProcessJobConfiguration/variables): https://docs-3.prefect.io/v3/api-ref/python/prefect-workers-process
- Workers overview: https://docs-3.prefect.io/v3/concepts/workers
If you tell me your OS and how you launch the worker (systemd, Docker, bare process), I can suggest the least fragile variant.
... its a lot better!
❤️ 1
gpt5 marvin now live here as well (please don't tag it too much 👼 )
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