@Marvin How do i read workers HOME directory env? cam_flow.deploy( name="cccg-deployment", work_pool_name="cccg-pool", image="", job_variables={ "volumes": [ f"${HOME}/clearml.conf:/home/uzer/clearml.conf", f"${HOME}/.rapidlib:/home/uzer/.rapidlib", ], "entrypoint": "", }, )

It seems there were some issues retrieving the latest Prefect version and searching the documentation directly. Let's proceed by addressing your question regarding reading the worker's home directory environment variable within a Prefect deployment configuration. To reference the

HOME

environment variable in a deployment, you can use Python's

os

module. Here's how you might modify your deployment configuration to achieve that:

python
import os

cam_flow.deploy(
    name="cccg-deployment",
    work_pool_name="cccg-pool",
    image="",
    job_variables={
        "volumes": [
            f"{os.environ['HOME']}/clearml.conf:/home/uzer/clearml.conf",
            f"{os.environ['HOME']}/.rapidlib:/home/uzer/.rapidlib",
        ],
        "entrypoint": "",
    },
)

In this example,

os.environ['HOME']

retrieves the HOME environment variable for the system on which this script is executed. If you're still having trouble or need specific information regarding your Prefect version, I recommend verifying your Prefect version and consulting the official Prefect documentation for the most accurate guidance. If you'd like, you can share your Prefect version, and I can assist you with more targeted help.

@Marvin I dont need it to read the env from where i execute this script. I need to get the env from where the flow is executing from (another machine)

To access environment variables from the worker where a Prefect flow is executing, you can specify environment variables for the worker's runtime environment when creating or configuring a work pool. This involves setting up job variables that ensure the necessary environment variables are available during the execution of your flow on the worker machine. Here's a brief overview of how you can achieve this: 1. Set Environment Variables in Work Pool Configuration: You can specify a set of environment variables that will be automatically set in the runtime environment of the flow run. This is usually configured when setting up your work pool. Example:

json
{
  "job_variables": {
    "env": {
      "HOME": "/path/on/worker",
      "ANOTHER_VAR": "value"
    }
  }
}

2. Access Environment Variables in Flow: Once the environment variables are set in the worker's environment, you can access them within your flow code using Python’s

os.environ

. Example flow code:

python
import os
from prefect import flow

@flow
def my_flow():
    home_directory = os.environ.get("HOME")
    print(f"Worker Home Directory: {home_directory}")

For comprehensive guidance on configuring job variables for work pools, you can refer to the official docs here: Overriding Work Pool Job Variables. If you encounter specific issues or errors, feel free to ask for further assistance!

@Marvin I need to know them beforehand, because im executing these flows in docker and i need to mount volumes. That means i need to know where the HOME dir is in order to mount the docker. Im on prefect 3

When deploying Prefect flows in a Docker environment with Prefect 3.0, you generally set up work pools with Docker workers to run your flows. However, if you need to know the home directory of your worker ahead of time for volume mounting, here are a couple of approaches: 1. Standard Home Directory in Docker: By default, most Docker containers set the home directory for the user to

/root

. You can assume this is the case unless you're using a custom Docker image that changes the default user. 2. Setting a Custom Home Directory: If you want to customize this, you can specify environment variables when creating the Docker container. Set the

HOME

environment variable explicitly in your Dockerfile or your Docker run configuration. 3. Custom Shell Script: Adjust the entrypoint of your Docker container to export a known home path ahead of execution. This is more of a workaround than a direct solution, but it ensures the path is defined according to your needs. Here's an example of starting a Docker worker with a specified environment:

bash
docker run -e HOME=/custom/home/path -v ${HOME}/clearml.conf:/custom/home/path/clearml.conf your-prefect-image

In the above, replace

/custom/home/path

with the path you want. For more detailed instructions and examples on running Prefect flows in Docker, you can check Prefect's documentation on this topic: - Run flows in Docker containers This page contains more insights into setting up Docker work pools and managing worker environments which might help align your infrastructure setup with your specific requirements. If you have further questions or need additional help configuring your Docker environments, feel free to ask!