@Marvin on Prefect 2's documentation, it is said that it is possible to run custom steps by packaging them into the default working dir of a Docker image. Suppose that I want to write my own custom step by doing that. I want my custom step to receive an argument arg and simply print the arg received when it's called by prefect.yaml. Can you write this python code for me and instruct me on how I can package it in a way so it's accessible by prefect.yaml?

It seems like we encountered a couple of roadblocks. The tool I used to search for Prefect 2.x documentation on custom steps needs specific queries, and there was a mishap getting access to GitHub issues as well. Given these hiccups, let's proceed with what we know conceptually about Docker and Prefect while I sort that out. To create a custom step that receives an argument and prints it, you'll want to follow these steps: 1. Create a Python Script for Your Custom Step:

python
# custom_step.py
import sys

def print_arg(arg):
    print("Received argument:", arg)

if __name__ == "__main__":
    if len(sys.argv) > 1:
        print_arg(sys.argv[1])
    else:
        print("No argument provided.")

This script will take an argument from the command line and print it. 2. Dockerfile to Package Your Script: Create a

Dockerfile

to package your custom step into a Docker image.

dockerfile
# Start with the base Python image
FROM python:3.9-slim

# Set the working directory
WORKDIR /app

# Copy the custom step script into the Docker container
COPY custom_step.py /app/custom_step.py

# Set the entry point to run your script
ENTRYPOINT ["python", "/app/custom_step.py"]

3. Build and Publish Your Docker Image: First, build your Docker image by running:

bash
docker build -t my-prefect-custom-step .

If you're using a container registry, you can push it there. Replace

my-docker-registry

and

my-prefect-custom-step

with your details:

bash
docker tag my-prefect-custom-step my-docker-registry/my-prefect-custom-step:latest
docker push my-docker-registry/my-prefect-custom-step:latest

4. Configure prefect.yaml: In your Prefect deployment configuration (

prefect.yaml

), you need to specify using your Docker image:

yaml
 - type: docker
   docker:
     image: my-docker-registry/my-prefect-custom-step:latest
     networks: []
     # Example of providing the argument
     Args: ["Hello, Prefect!"]

For more detailed steps, or if you'd like to confirm things are correct, checking Prefect's latest documentation will be crucial once access is resolved. If there are specific subtopics you want more clarity on, let me know!

cool, @Marvin now how can I call it from prefect.yaml and pass the argument from there?

To call your custom step from

prefect.yaml

and pass an argument, you'll need to adjust the YAML configuration to run the Docker container with the desired argument. Here's how you can structure your

prefect.yaml

file: 1. Prefect Deployment Configuration: In your existing

prefect.yaml

, ensure you define the flow's deployment and specify the Docker image, passing the argument directly in the command section.

yaml
version: 2

flows:
  - name: my_flow
    run_config:
      type: docker
      docker:
        image: my-docker-registry/my-prefect-custom-step:latest
        networks: []

    # Specify entrypoint and pass argument here
    entrypoint: ["python", "/app/custom_step.py", "Hello, from Prefect!"]

In this example: -

version: 2

is specified for Prefect 2.x. - The

image

field under

docker

specifies which Docker image to use. - The

entrypoint

overrides the default entry-point of the Docker image, allowing you to run your script with an argument directly. This setup will run your Docker container and execute the script with the argument "Hello, from Prefect!" as specified in the YAML configuration. Make sure your Docker image is available in your registry if you plan on using it outside your local environment. Getting more details from Prefect's documentation could further optimize or verify this setup if required. Let me know if you need more adjustments or further features implemented!

@Marvin in the deployments section of my prefect.yaml, how can I access the output of a step executed in the pull section? More specifically, I'd like to set an env var with this output in the job_variables subsection

To set an environment variable with the output from a step executed in the

pull

section of your

prefect.yaml

, you can use Prefect's templating capabilities to access outputs from previous steps. While direct support for capturing the output of steps dynamically into environment variables might not be extensively detailed, I'll give you a general idea based on what you can do. 1. Define the Pull Step Output: Let's say you have a pull step like this:

yaml
pull:
  - prefect_aws.deployments.steps.pull_from_s3:
      id: pull-data
      bucket: my-bucket
      folder: my-folder
      credentials: null

In this setup, the

pull_from_s3

step could output a bucket or folder value. 2. Access the Pull Step Output in Job Variables: In the

job_variables

section, you can template values using the outputs from the pull step. Prefect typically allows templating using Jinja-like syntax.

yaml
deployments:
  - name: my-deployment
    job_variables:
      MY_NEW_ENV_VAR: "{{ pull-data.output_variable }}"

Unfortunately, the documentation excerpts I could access don't specify the exact syntax for accessing outputs directly like this. You might need to access the Prefect documentation more thoroughly or experiment with the deployment YAML syntax to effectively capture outputs. For related documentation and further reading, check out these resources: - Prefect Deployment Guide - Overriding Job Variables in Prefect I hope this helps! Let me know if you continue facing challenges or need more information.

@Marvin shouldn't my env vars be in the env subsection of the job_variables subsection?

You're absolutely right! When configuring

env

variables within the

job_variables

section of your

prefect.yaml

, they should indeed be placed under the

env

subsection. This allows your flow to access environment variables correctly. Here's how you can structure it:

yaml
deployments:
  - name: my-deployment
    entrypoint: path/to/your_flow.py:flow_function
    job_variables:
      env:
        MY_ENV_VAR: "{{ pull-data.output_variable }}"

In this configuration: -

entrypoint

specifies the Python script and function where your flow is defined. - Inside

job_variables

, the

env

key holds a dictionary of environment variables that you'd like to set. - By using templating (

{{ pull-data.output_variable }}

), you can dynamically insert values if your previous steps (like

pull-data

) provide outputs you need. This structure ensures that the environment variables are correctly set for your Prefect flow execution. It's a sensible way to pass dynamic data between different parts of your deployment configuration. If you have more queries or need further assistance, feel free to ask!

@Marvin I just did this, but I still cant access MY_ENV_VAR from inside my flows. python os.environ doesn't contain it

It seems like you've encountered an issue where the environment variables specified in your

prefect.yaml

are not making it into the flow's execution environment. Here are a few clarifications and checks you can consider: 1. Using Existing Environment Variables: Ensure that the environment variables are properly templated if you rely on existing system environment variables. Use the syntax

{{ $ENV_VAR_NAME }}

if that's the case. 2. Deploying with Environment Variables: When you deploy your flow, verify that the machine or environment executing the deployment has access to those environment variables. They should be set in the system or specified directly in the deployment configuration. 3. Example Configuration: Here’s a sample configuration snippet based on Prefect's guidelines:

yaml
   deployments:
     - name: my-deployment
       entrypoint: path/to/your_flow.py:flow_function
       work_pool:
         name: local
       job_variables:
         env:
           EXECUTION_ENVIRONMENT: "{{ $EXECUTION_ENVIRONMENT }}"
           MY_ENV_VAR: "{{ $MY_ENV_VAR }}"

4. Ensure Prefect Version Compatibility: Double-check your Prefect version to ensure the configuration aligns with the correct syntax as changes might exist between versions. 5. Manual Os-Check: By adding some logging or print statements using

os.environ.get('MY_ENV_VAR')

within your flow, confirm if the variable is even partially set. 6. Documentation Reference: For further insights, see Prefect’s documentation about Overriding Job Variables. Hopefully, one of these approaches resolves your issue. Reconfirm your deployment pipeline to ensure it’s using the yaml files correctly and doesn’t cache old versions or incorrect steps. If things still seem amiss, it might be worth revisiting the setup to ensure all connections are correctly established. Let me know how it goes, or if there’s anything else specific you want to explore!