Customizing Task Parameters

This example demonstrates how to customize and pass parameters to a Practicus AI worker. You can do so by defining environment variables in the WorkerConfig object.

Why Environment Variables?

Environment variables are often the easiest way to inject small pieces of configuration or parameters into a script. Practicus AI automatically sets these environment variables in the worker's environment when your script runs.

Basic Example

In this simple example, we show how to set environment variables in WorkerConfig and then access them in a Python script.

# task.py
import os

print("First Param:", os.environ["MY_FIRST_PARAM"])
print("Second Param:", os.environ["MY_SECOND_PARAM"])

Defining WorkerConfig with Environment Variables

Below, we create a WorkerConfig object. Notice how we specify environment variables in the env_variables dictionary. Practicus AI will ensure these variables are set when task.py is executed.

import practicuscore as prt

worker_config = prt.WorkerConfig(
    worker_image="practicus",  # The base container image
    worker_size="X-Small",     # Size configuration
    env_variables={
        "MY_FIRST_PARAM": "VALUE1",
        "MY_SECOND_PARAM": 123
    },
)

worker, success = prt.run_task(
    file_name="task.py",          # The name of the script to run
    worker_config=worker_config,
)

print("Task finished with status:", success)

When this code runs, it will print out the values of MY_FIRST_PARAM and MY_SECOND_PARAM from within task.py.

Airflow Integration

To integrate with Practicus AI Airflow, you can utilize the same approach. Airflow tasks can inject environment variables by writing out a WorkerConfig JSON file that Practicus AI can pick up.

For example: 1. Define your worker_config in Python. 2. Serialize it to JSON. 3. Store it in a file named after your task (e.g., task_worker.json if your script is task.py).

Example: Writing task_worker.json

worker_config_json = worker_config.model_dump_json(exclude_none=True)

with open("task_worker.json", "wt") as f:
    f.write(worker_config_json)

print("Generated worker_config JSON:\n", worker_config_json)

An example task_worker.json might look like:

{
  "worker_image": "practicus",
  "worker_size": "X-Small",
  "additional_params": "eyJlbnZfdmFyaWFibGVzIjogeyJNWV9GSVJTVF9QQVJBTSI6ICJWQUxVRTEiLCAiTVlfU0VDT05EX1BBUkFNIjogMTIzfX0="
}

Note that additional_params is base64-encoded data containing:

{
  "env_variables": {
    "MY_FIRST_PARAM": "VALUE1",
    "MY_SECOND_PARAM": 123
  }
}

This is how Practicus AI stores your configuration behind the scenes.

Summary

• Defining environment variables: Use env_variables within WorkerConfig to inject parameters.
• Accessing parameters: In your Python script (e.g., task.py), read them from os.environ.
• Airflow: Write out the worker_config to a JSON file. Practicus AI automatically picks that up.

By following these steps, you can effectively pass custom parameters and configurations to your Practicus AI tasks, making your data pipelines more dynamic and flexible.

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