@Marvin generate a python script that deploys a flow. The script will also accept a list of pip_packages that will be installed on the worker when the flow is executed.

To deploy a flow in Prefect 3.x and include additional pip packages for installation on the worker, you can use the

deploy

function and specify the packages using the

job_variables

parameter. Here's a basic script to achieve this:

python
from prefect import flow, deploy

@flow
def my_flow():
    print("Hello from Prefect!")

# Define your pip packages
pip_packages = ["numpy", "pandas"]

# Deploy the flow
deploy(
    flow=my_flow,
    name="my_deployment",
    work_pool_name="my_work_pool",  # Ensure this matches your setup
    job_variables={
        "EXTRA_PIP_PACKAGES": " ".join(pip_packages)
    }
)

### Key Points: - Flow Definition: Define your flow using the

@flow

decorator. - Deployment: Use the

deploy

function to create a deployment of the flow. - Pip Packages: Specify additional pip packages using the

EXTRA_PIP_PACKAGES

key in

job_variables

. ### Relevant Links: - Deploying with Prefect - Docker Infrastructure Examples This script assumes you have a work pool named "my_work_pool" set up in your Prefect environment. Adjust the

work_pool_name

accordingly.

@Marvin I see in my deployment that my Job Variables contains

{
  "EXTRA_PIP_PACKAGES": "tqdm"
}

However when I run the deployed flow, I get the error "ModuleNotFoundError: No module named 'tqdm'"

The error you're encountering with the

EXTRA_PIP_PACKAGES

not installing as expected might be related to several issues observed in Prefect deployments. Here are a few steps and considerations to address the problem: 1. Check Work Pool Configuration: Ensure that the work pool is correctly configured to respect the

EXTRA_PIP_PACKAGES

. Sometimes, configurations might not propagate as expected. 2. Environment and Image: If you're using a custom Docker image or a specific environment, verify that it allows for dynamic installation of packages. The base image should have the necessary permissions and configurations to install packages at runtime. 3. Concurrency Issues: There have been reports of

ModuleNotFoundError

when running multiple flows concurrently, especially with shared infrastructure. Ensure that the environment is not being modified by another process concurrently. 4. Prefect Version: Ensure you're using a compatible version of Prefect that supports the

EXTRA_PIP_PACKAGES

feature. Sometimes, bugs in specific versions can cause unexpected behavior. 5. Logs and Debugging: Check the logs for any additional error messages or warnings that might provide more context. Prefect's debug mode can be enabled to gather more detailed logs. If these steps do not resolve the issue, consider raising it on the Prefect GitHub issues page with detailed logs and configurations for further assistance.

@Marvin how can I ensure that the work pool is correctly configured to respect the

EXTRA_PIP_PACKAGES

To ensure that your work pool is correctly configured to respect the

EXTRA_PIP_PACKAGES

, you can follow these steps: 1. Configuration Methods: Work pools can be configured using the Prefect UI, CLI commands, REST API, or a Terraform provider for Prefect Cloud. Choose the method that best fits your workflow for managing configurations. 2. Prefect UI: - Navigate to the Work Pools section by clicking the Work Pools icon. - Review the list of currently configured work pools. - Ensure that the work pool associated with your deployment is configured to allow the installation of additional packages. 3. Prefect CLI: - Use the CLI to inspect and modify work pool configurations. This can be useful if you need to script or automate changes. 4. Environment Variables: - Make sure that the

EXTRA_PIP_PACKAGES

environment variable is correctly set in the work pool's job variables. This variable should be passed to the workers executing the flow. 5. Worker Configuration: - Verify that the workers in the pool are set up to respect environment variables and have permissions to install packages. For detailed documentation on configuring work pools, visit the Work Pools Documentation. By ensuring these configurations are correct, you can help mitigate issues with package installations during flow execution.

@Marvin How do I make sure that the

EXTRA_PIP_PACKAGES

environment variable is correctly set in the work pool's job variables? What must I enter under Environment Variable?

To ensure that the

EXTRA_PIP_PACKAGES

environment variable is correctly set in the work pool's job variables, you should follow these steps: 1. Access Work Pool Configuration: - Navigate to the work pool settings through the Prefect UI or use the Prefect CLI. 2. Set Environment Variable: - In the job variables section, you need to specify the

EXTRA_PIP_PACKAGES

key and provide a space-separated string of the packages you want to install. - For example, if you want to install

tqdm

and

requests

, you would set:

json
     {
       "EXTRA_PIP_PACKAGES": "tqdm requests"
     }

3. Save Changes: - Ensure you save the changes to the work pool configuration to apply the new environment variables. This configuration allows the workers in the work pool to dynamically install the specified Python packages when executing the flow. Make sure that the worker environment has internet access and permissions to install packages from PyPI.

@Marvin this did not work. However when I deploy a flow and I add "- prefect.deployments.steps.pip_install_requirements: requirements_file: production/Flows/foldername/requirements.txt stream_output: false" now it will install all modules in my requirements.txt file when I run the flow

It seems like using the

pip_install_requirements

step in your deployment configuration is effectively installing the necessary packages as specified in your

requirements.txt

. This approach is more explicit and might be more reliable in some environments than using

EXTRA_PIP_PACKAGES

. Here's a brief explanation of why this might be working better: 1. Explicit Installation: By specifying a

requirements.txt

file, you are providing a clear list of dependencies that Prefect can install before executing the flow. This is often more robust as it handles complex dependencies better. 2. Configuration: The

pip_install_requirements

step directly uses the Python

pip

tool to install packages, which might bypass certain limitations or misconfigurations that could affect

EXTRA_PIP_PACKAGES

. 3. Environment Consistency: This method ensures that all necessary packages are installed in the correct versions, leading to more consistent environments across different runs. If this method works for you, it might be a good practice to continue using it, especially if you have a comprehensive list of dependencies managed in a

requirements.txt

file. This approach aligns well with typical Python project setups and can help avoid issues related to missing or incompatible packages.