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<@ULVA73B9P> in my deployment yaml, I specify gene...
# marvin-in-the-wild
t
@Marvin in my deployment yaml, I specify general env variables for a work pool. I also specify additional env variables for a specific flow like this. Somehow, only those listed in the specific flow go through? I want to get all of them for that flow
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definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
m
It seems like the issue you're facing might be related to how job variables are merged. Here are a few points to consider: 1. Deep vs. Shallow Merge: According to this GitHub issue, job variables should use a deep merge rather than a shallow merge of dictionaries. This means that specifying additional environment variables at the flow level might override the existing work pool environment variables instead of merging with them. 2. Specifying Environment Variables in Deployment: To ensure all environment variables are included, it's advisable to ensure they are merged correctly. The documentation on overriding job variables provides guidance. Here is an example snippet from the documentation:
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yaml
deployments:
  - name: demo-deployment
    entrypoint: demo_project/demo_flow.py:some_work
    work_pool:
      name: local
    job_variables:
      env:
        EXECUTION_ENVIRONMENT: "local"
        MY_NOT_SO_SECRET_CONFIG: "config_value"
To ensure that all environment variables specified in the 
work_pool
and the deployment level are included, you might need to explicitly list them in the deployment file or ensure that the deep merge strategy is applied. 3. GitHub Issues: Another GitHub discussion also touches on similar issues related to precedence and merging of environment variables: GitHub Issue ### Suggested Solution: To troubleshoot this, you may want to: 1. Verify the current merging strategy used in your deployment setup. 2. Ensure that any custom environment variables are directly included in the deployment configuration if the merging behavior is not as expected. 3. Continue to monitor the GitHub issues for updates or patches related to your specific problem. If the merging strategy is not configurable, you might need to apply the environment variables at the deployment level to ensure they are all included, even if it means some duplication. For more details, you can refer to the documentation and the mentioned GitHub issues for the latest updates and discussions. Links: - Documentation on Overriding Job Variables - Relevant GitHub Issue - Another related GitHub Issue
t
@Marvin my deployment env variables are like this
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deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        env:
          TEST1: "test1"
          TEST: "test"
m
Based on the information from the documentation, your configuration for environment variables seems to be correct. However, given that only the flow-specific environment variables went through, this might be an issue with how environment variables are being merged. ### Example Configuration: Make sure your deployment YAML is configured correctly:
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yaml
deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        env:
          TEST1: "test1"
          TEST: "test"
### Key Points: 1. Ensure Deep Merge: As mentioned in a GitHub issue, job variables should use a deep merge rather than a shallow merge. 2. Document Reference: The documentation example provides another way to structure job variables in deployments which might help address the merging issue. Review the environment variable precedence and merge strategy in your Prefect setup. ### Links: - Documentation on Overriding Job Variables - Related GitHub Issue discussing the merge strategy of job variables Ensure that the specified environment variables are deep-merged, and consider explicitly applying all required environment variables at the flow level if necessary.
t
@Marvin give an example in this case. I want PREFECT_LOGGING_LEVEL, PREFECT_LOGGING_ROOT_LEVEL, TEST1 and TEST2 in my flow
m
Here's how you can configure your deployment YAML to include all the desired environment variables:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        env:
          TEST1: "test1"
          TEST2: "test2"
### Key Points: 1. The 
<<: *default_work_pool
syntax ensures that the job variables defined at the work pool level are inherited in your deployment. 2. The environment variables defined specifically at the deployment level (
TEST1
and 
TEST2
) will be merged with those inherited from the work pool. In this configuration, all the environment variables (
PREFECT_LOGGING_LEVEL
, 
PREFECT_LOGGING_ROOT_LEVEL
, 
PREFECT_LOGGING_HANDLERS_API_LEVEL
, 
TEST1
, and 
TEST2
) should be available to your flow.
t
@Marvin no i can't see PREFECT_LOGGING_LEVEL, PREFECT_LOGGING_ROOT_LEVEL, and PREFECT_LOGGING_HANDLERS_API_LEVEL in the flow
m
Ah, the issue may indeed be related to the merging behavior of environment variables. You might need to explicitly include all the required environment variables at the deployment level, ensuring everything gets merged or overridden correctly. Here’s an updated deployment configuration example:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
          TEST1: "test1"
          TEST2: "test2"
### Key Points: 1. Explicit Definition: All required environment variables are explicitly included in 
job_variables.env
at the deployment level. By explicitly listing all the variables (
PREFECT_LOGGING_LEVEL
, 
PREFECT_LOGGING_ROOT_LEVEL
, 
PREFECT_LOGGING_HANDLERS_API_LEVEL
, 
TEST1
, and 
TEST2
), you ensure they are set correctly for your flow. If your system doesn't inherently support deep merging of job variables, explicitly listing them is the safest way to guarantee all necessary environment variables are present. This way, there's no ambiguity about which variables are included in the final environment configuration.
t
@Marvin but i want those three to be shared with other flows as well in the same work pool
m
To maintain shared environment variables across multiple flows while also allowing for specific variables at the deployment level, you can set the shared variables at the work pool level and ensure they are inherited properly. Here is how you can structure your YAML to ensure both shared and flow-specific environment variables are included: 1. Define Shared Variables in Work Pool: Ensure the common environment variables are defined in the 
default_work_pool
. 2. Override with Deployment-Specific Variables: Add deployment-specific variables in the 
deployments
section. ### Example Configuration:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG

deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        # Override or add specific environment variables for this deployment
        env:
          PREFECT_LOGGING_LEVEL: ${PREFECT_LOGGING_LEVEL}
          PREFECT_LOGGING_ROOT_LEVEL: ${PREFECT_LOGGING_ROOT_LEVEL}
          PREFECT_LOGGING_HANDLERS_API_LEVEL: ${PREFECT_LOGGING_HANDLERS_API_LEVEL}
          TEST1: "test1"
          TEST2: "test2"
### Key Points: 1. Inherit Shared Variables: - The shared environment variables (
PREFECT_LOGGING_LEVEL
, 
PREFECT_LOGGING_ROOT_LEVEL
, 
PREFECT_LOGGING_HANDLERS_API_LEVEL
) are defined in the work pool. - To ensure these variables are included, reference them in the deployment-specific variables using 
${VAR_NAME}
syntax. 2. Override or Add Specific Variables: - Add the specific variables for the deployment (
TEST1
, 
TEST2
) directly in the deployment’s 
env
section. ### Notes: - This approach ensures that the shared variables are available to any flow using the same work pool while still allowing specific deployments to add or override variables. - Ensure that any variable referenced using 
${VAR_NAME}
syntax is exported in your environment before deploying to Prefect. By using these configurations, you should be able to maintain a consistent set of shared environment variables across multiple flows while still providing flow-specific customizations.
t
@Marvin but that gives literal string of ${VAR_NAME} not necessarily the actual variable values
m
To achieve the goal of having shared environment variables across multiple flows within the same work pool and deployment-specific variables without using literal strings, you should merge the variables at the deployment level. Given the current limitations in how Prefect handles dictionary merging, you have to explicitly define all environment variables at the deployment level. Here’s how you can do that while maintaining clarity and ensuring reusability: ### Step 1: Define Shared Variables in Work Pool Define the shared environment variables in the work pool configuration:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
### Step 2: Include Both Shared and Specific Variables in Deployment Explicitly merge shared and specific environment variables in the deployment section:
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yaml
deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
          TEST1: "test1"
          TEST2: "test2"
This configuration sets all necessary environment variables both at the work pool level and within the specific deployment. Ensure to: 1. List shared variables in both work pool and deployment configurations. 2. Add deployment-specific variables directly within that deployment’s 
env
section. ### Explanation: - Shared Variables in Work Pool: These variables are common across multiple flows. - Deployment-Specific Variables: Additional environment variables specific to a deployment are still included, ensuring that shared variables and deployment-specific variables coexist without manually handling dictionary merges. ### Summary: By listing all the required environment variables explicitly in the deployment configuration, you ensure that both shared and specific variables are included in your flow. This approach avoids issues with literal strings and ensures consistency. Here’s how you can structure your Prefect deployment YAML to merge shared and deployment-specific environment variables successfully.
t
@Marvin alright. Btw, what's the meaning of life?
m
Ah, the age-old question. As the Hitchhiker's Guide to the Galaxy so famously documented, the meaning of life, the universe, and everything is 42. If only it were that simple for our deployment configurations, right? For more practical purposes, if you have any more questions about Prefect or need further assistance, feel free to ask! Here’s to finding our own 42 in every little thing we do.
t
@Marvin i want to add a folder in pythonpath so that i can import it as a module in python. I add this PYTHONPATH: $PYTHONPATH/opt/prefect/src/opt/prefect/src/ in job_variables but doesnt work. I also execute a bash file containing
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export PYTHONPATH=$PYTHONPATH:/opt/prefect/src:/opt/prefect/src/
PREFECT_EXTRA_ENTRYPOINTS=span
source ~/.bashrc
but it also doesnt work
m
To ensure that the 
PYTHONPATH
environment variable is set correctly so that your Python modules can be imported properly within Prefect deployments, follow these steps: ### Step 1: Set 
PYTHONPATH
in the job variables Modify your deployment YAML to include the 
PYTHONPATH
environment variable with the full intended value. You need to ensure that the 
job_variables
section includes this correctly.
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
          PYTHONPATH: /opt/prefect/src:/opt/prefect/src/

deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        env:
          TEST1: "test1"
          TEST2: "test2"
          PYTHONPATH: /opt/prefect/src:/opt/prefect/src/
### Step 2: Ensure 
PYTHONPATH
is Set Properly Make sure that the specified paths are correct and accessible. You can debug by printing environment variables in your flow. Here’s an example:
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python
import os
from prefect import flow, task

@task
def check_pythonpath():
    pythonpath = os.environ.get('PYTHONPATH')
    print(f'PYTHONPATH: {pythonpath}')

@flow
def my_flow():
    check_pythonpath()

if __name__ == "__main__":
    my_flow()
### Additional Tips: - Order of Paths: Ensure the paths included in 
PYTHONPATH
are in the correct order so that Python can find the modules. - Check Path Existence: Double-check that the paths 
/opt/prefect/src
exist on the machine running the flow. - Integrate with Other Commands: If using a Bash script, make sure it sets the 
PYTHONPATH
correctly and is sourced properly. ### Example Bash Script: If you still need to use a Bash script to set the environment variables, include it in your deployment steps:
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bash
#!/bin/bash
export PYTHONPATH=$PYTHONPATH:/opt/prefect/src:/opt/prefect/src/
PREFECT_EXTRA_ENTRYPOINTS=span
And make sure your deployment YAML references this script correctly. ### Conclusion: By setting the 
PYTHONPATH
both at the work pool and deployment levels, you ensure the environment variable is consistently available to your flow. This approach avoids issues with expansion and ensures that modules can be imported correctly. If further debugging is required, print the 
PYTHONPATH
within your tasks to verify it has been set as expected.
t
@Marvin are the other ways to do it. They don't work
m
To resolve the 
PYTHONPATH
issue, let's look at alternative methods based on the search results and best practices: ### Method 1: Setting Environment Variables in Bash EntryPoint Script One effective approach is to use a Bash script as the entry point for your deployment, ensuring that the 
PYTHONPATH
is set correctly each time the deployment is run. #### Step-by-Step: 1. Create a Bash Script: Create a bash script to export the 
PYTHONPATH
and source it in your flow.
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bash
#!/bin/bash
export PYTHONPATH=$PYTHONPATH:/opt/prefect/src:/opt/prefect/src/
exec "$@"
2. Deploy with Bash Script: Update your deployment YAML to use this bash script.
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        command: ["/bin/bash", "/path/to/your_script.sh"]
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG

deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
          TEST1: "test1"
          TEST2: "test2"
          PYTHONPATH: /opt/prefect/src:/opt/prefect/src/
### Method 2: Expand Environment Variables Ensure the environment variables can expand using Prefect's deployment steps. According to the documentation:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG

deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        command: ["sh", "-c", "export PYTHONPATH=$PYTHONPATH:/opt/prefect/src:/opt/prefect/src/; exec python -m my_flow"]
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
          TEST1: "test1"
          TEST2: "test2"
### Method 3: Using Docker Container Constructing a custom Docker image with the required 
PYTHONPATH
settings ensures consistent deployment: 1. Create a Dockerfile:
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dockerfile
FROM prefecthq/prefect:2-python3.11

# Set environment variables
ENV PYTHONPATH="/opt/prefect/src:/opt/prefect/src/"

# Copy your project files into the container
COPY . /opt/prefect/src/

# Set the working directory
WORKDIR /opt/prefect/src/
2. Build and Push Docker Image:
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sh
docker build -t your-docker-repo/prefect-custom:latest .
docker push your-docker-repo/prefect-custom:latest
3. Update Deployment: ```yaml definitions: work_pools: default_work_pool: &default_work_pool name: pool-python-3.11 work_queue_name: default job_variables: image: your-docker-repo/prefect-custom:latest resources: memory: "128G" cpu: "16" env: PREFECT_LOGGING_LEVEL: DEBUG PREFECT_LOGGING_ROOT_LEVEL: DEBUG PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG deployments: - <<: *default_deployment name: abcd entrypoint: flows/aa/bb.py:cc parameters: work_pool: <<: *default_work_pool job_variables: env: TEST1: "test1" TEST2: "test2"
``` ### Summary These methods should provide you with robust ways to set the 
PYTHONPATH
and other environment variables for Prefect deployments: 1. Using a Bash script as an entry point. 2. Expanding environment variables correctly in command steps. 3. Utilizing custom Docker images for consistent environments. Choose the method best suited to your requirements, ensuring your flows can import necessary modules and operate as expected.
t
@Marvin i think the problem is that anything inside the flow won't read the pythonpath
m
Given this limitation, an alternative approach is to modify the 
sys.path
directly within your flow. This can be done at the start of your flow definition to ensure the correct paths are included and modules can be imported effectively. Here's how to adjust the 
sys.path
within your flow code: ### Step-by-Step Solution 1. Modify 
sys.path
in the Flow Code:
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python
import sys

# Add the necessary paths to sys.path at the beginning of the script
sys.path.append('/opt/prefect/src')

from prefect import flow, task

@task
def check_pythonpath():
    import os
    pythonpath = os.environ.get('PYTHONPATH')
    print(f'PYTHONPATH: {pythonpath}')
    print(f'sys.path: {sys.path}')

@flow
def my_flow():
    check_pythonpath()

if __name__ == "__main__":
    my_flow()
2. Continue with Your Regular Deployment YAML: Keep your deployment YAML as it was, without worrying about setting `PYTHONPATH`:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: prefecthq/prefect:2-python3.11
        resources:
          memory: "128G"
          cpu: "16"
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG

deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
      job_variables:
        env:
          PREFECT_LOGGING_LEVEL: DEBUG
          PREFECT_LOGGING_ROOT_LEVEL: DEBUG
          PREFECT_LOGGING_HANDLERS_API_LEVEL: DEBUG
          TEST1: "test1"
          TEST2: "test2"
### Summary By directly modifying 
sys.path
at the beginning of your flow, you can ensure that Python can locate and import your modules regardless of external environment settings. This method is particularly useful when dealing with limitations in propagating environment variables like 
PYTHONPATH
within Prefect's execution environments.
t
@Marvin are there more elegant approaches to this
m
Certainly, more elegant approaches can be employed when dealing with 
PYTHONPATH
settings in Prefect deployments. Here are a few alternatives: ### Approach 1: Using Prefect Tasks to Set Environment Variables You can create a Prefect task to set the environment variable 
PYTHONPATH
using 
os.environ
before importing your modules:
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python
import os
from prefect import flow, task

@task
def set_pythonpath(paths: str):
    os.environ['PYTHONPATH'] = paths
    print(f'Set PYTHONPATH: {os.environ["PYTHONPATH"]}')

@flow
def my_flow():
    set_pythonpath('/opt/prefect/src:/opt/prefect/src')
    # Your other tasks that require the updated PYTHONPATH
    # Example: import my_module

if __name__ == "__main__":
    my_flow()
### Approach 2: Custom Docker Images Creating a custom Docker image with the necessary 
PYTHONPATH
settings offers a more robust and reusable solution. 1. Create a Dockerfile:
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dockerfile
FROM prefecthq/prefect:2-python3.11

# Set environment variables
ENV PYTHONPATH="/opt/prefect/src:/opt/prefect/src/"

# Copy your project files into the container
COPY . /opt/prefect/src/

# Set the working directory
WORKDIR /opt/prefect/src/
2. Build and Push Docker Image:
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sh
docker build -t your-docker-repo/prefect-custom:latest .
docker push your-docker-repo/prefect-custom:latest
3. Update Deployment YAML to Use Custom Image:
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yaml
definitions:
  work_pools:
    default_work_pool: &default_work_pool
      name: pool-python-3.11
      work_queue_name: default
      job_variables:
        image: your-docker-repo/prefect-custom:latest
        resources:
          memory: "128G"
          cpu: "16"

deployments:
  - <<: *default_deployment
    name: abcd
    entrypoint: flows/aa/bb.py:cc
    parameters:
    work_pool:
      <<: *default_work_pool
### Approach 3: Prefect's Run Configurations with Environment Variables Use Prefect’s run configurations to set the environment variables for each run.
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python
from prefect import Flow
from prefect.run_configs import UniversalRun

with Flow("my_flow") as flow:
    # Your tasks here

flow.run_config = UniversalRun(env={
    "PYTHONPATH": "/opt/prefect/src:/opt/prefect/src/",
    "PREFECT_LOGGING_LEVEL": "DEBUG",
    "PREFECT_LOGGING_ROOT_LEVEL": "DEBUG",
    "PREFECT_LOGGING_HANDLERS_API_LEVEL": "DEBUG"
})

if __name__ == "__main__":
    flow.register(project_name="my_project")
### Summary Depending on your workflow and deployment environment, these approaches can provide a more streamlined and elegant solution for setting `PYTHONPATH`: 1. Prefect Tasks for Runtime Adjustments: Using tasks to set environment variables dynamically. 2. Custom Docker Images: Building a Docker image with the required environment settings. 3. Run Configurations: Leveraging Prefect’s built-in run configurations to set environment variables. Each method has its own advantages, whether it be flexibility, reusability, or ease of integration. Choose the one that aligns best with your project's needs.
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