< Marvin> I m deploying and running a flow to a k8s workpool Prefect Community #ask-marvin

<@ULVA73B9P> I'm deploying and running a flow to ...

Marco Ruta

05/28/2024, 9:55 AM

@Marvin I'm deploying and running a flow to a k8s workpool and I get a prefect_aws module not found. How can I force the install of the requirements inside the pods?

Marvin

05/28/2024, 9:56 AM

To resolve the

prefect_aws

module not found issue for your Kubernetes work pool in Prefect, you can install additional Python packages by setting the

EXTRA_PIP_PACKAGES

environment variable. Here's how you can specify package installations directly in the Kubernetes work pool settings: 1. Set Environment Variables in Work Pool Configuration: - Open the Prefect Cloud UI or use the CLI to configure your Kubernetes work pool. - Add an environment variable to set when starting the flow run. - Use the

EXTRA_PIP_PACKAGES

environment variable to specify additional packages. Example:

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json
{
  "EXTRA_PIP_PACKAGES": "prefect_aws==0.3.2"
}

This configuration would ensure that

prefect_aws

is installed at runtime. 2. Custom Dockerfile (if needed): - If you need more custom dependencies or configurations, you can create a custom Dockerfile. - Prefect can help create a Dockerfile with your flow code and specified packages using a

requirements.txt

file. Example Dockerfile:

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Dockerfile
FROM prefecthq/prefect:2.10.11-python3.10

COPY requirements.txt /tmp/requirements.txt
RUN pip install -r /tmp/requirements.txt

Include

prefect_aws

in your `requirements.txt`:

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prefect_aws==0.3.2

For comprehensive guidance, you may refer to these helpful Prefect Deployment Guide and Kubernetes Deployment Guide. Here's a link to a related GitHub issue discussing similar problems. If you need any further assistance, feel free to ask.

Marco Ruta

05/28/2024, 9:58 AM

@Marvin In my repo I have the requirements file but it seems that the way I'm deploying and running the flow does not install them:

add_envoy_deployment = flow.from_source(

source=GitRepository( url="...", credentials={"access_token": gitlab_token} ), entrypoint="flows/flows.py:add_flow", ).deploy( name=f"add_k3s_envoy_flow", work_pool_name=f"k3s-test", tags=[ "k3s"], ) params = { ... params ... } run_deployment( name=add_envoy_deployment, as_subflow=False, parameters=params, timeout=0 )

Marvin

05/28/2024, 10:00 AM

To ensure your requirements are installed when deploying a flow from a Git repository, you can specify the

EXTRA_PIP_PACKAGES

environment variable in your deployment configuration. Unfortunately, the combination of

GitRepository

and

run_deployment

doesn't automatically install requirements from a

requirements.txt

file by default. Instead, you can manually handle the installation of additional packages. Here’s how to incorporate the installation of your requirements: 1. Include the
EXTRA_PIP_PACKAGES
in the Deployment Configuration: You can modify your deployment script to include the required environment variable (

EXTRA_PIP_PACKAGES

). This will ensure the packages listed in your

requirements.txt

are installed at runtime.

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python
from prefect.deployments import Deployment
from prefect.filesystems import GitRepository
from prefect.infrastructure.kubernetes import KubernetesJob
from prefect.client import Secret

gitlab_token = Secret.load("gitlab-token")
source = GitRepository(
    url="<https://gitlab.com/your/repo.git>",
    credentials={"access_token": gitlab_token}
)

kubernetes_job = KubernetesJob(
    image="your-custom-image:latest",  # Customize your image if needed
    env={"EXTRA_PIP_PACKAGES": "prefect_aws"}
)

add_envoy_deployment = Deployment.build_from_source(
    flow_location="flows/flows.py:add_flow",
    name="add_k3s_envoy_flow",
    work_pool_name="k3s-test",
    infrastructure=kubernetes_job,
    source=source,
    tags=["k3s"]
)

params = {
    # ... params ...
}

deployment_result = add_envoy_deployment.apply()

flow_run = run_deployment(
    name="add_k3s_envoy_flow/k3s-test",
    as_subflow=False,
    parameters=params,
    timeout=0,
)

### Custom Docker Image (Optional) If you have many dependencies or complex setup logic, consider building a custom Docker image with all dependencies pre-installed. Dockerfile Example:

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Dockerfile
FROM prefecthq/prefect:2.10.11-python3.10

COPY requirements.txt /tmp/requirements.txt
RUN pip install -r /tmp/requirements.txt

Build and push the image to your registry:

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bash
docker build -t your-custom-image:latest .
docker push your-custom-image:latest

### Links for Reference - Prefect Deployment Guides - Work Pools and Workers - Kubernetes Deployment Feel free to ask if you have further questions or need additional assistance.

Marco Ruta

05/28/2024, 10:01 AM

@Marvin Is there a way to simply say as first step run an install of the requirements?

Marco Ruta

05/28/2024, 10:05 AM

@Marvin definitions: pull: &common_pull - prefect.deployments.steps.set_working_directory: directory: /opt/prefect - prefect.deployments.steps.git_clone: id: clone-step repository: "{{ prefect.variables.demo_repo }}" - prefect.deployments.steps.pip_install_requirements: directory: "{{ clone-step.directory }}/flows" requirements_file: requirements.txt stream_output: False Is there a wy to force this in my previous file in the .deploy function?

Marvin

05/28/2024, 10:06 AM

Yes, you can use the

pull

block configuration within your deployment to specify steps such as setting the working directory, cloning a Git repository, and installing requirements. You can create a deployment configuration file, typically a YAML file, where you define these steps and link it to your deployment in the

.deploy()

method. Here’s how you can integrate these steps directly into your deployment: ### Step 1: Define Your Deployment Configuration in YAML Create a YAML file (e.g.,

deployment.yaml

) with the following content:

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yaml
definitions:
  pull: &common_pull
    - prefect.deployments.steps.set_working_directory:
        directory: /opt/prefect
    - prefect.deployments.steps.git_clone:
       id: clone-step
       repository: "{{ prefect.variables.demo_repo }}"
    - prefect.deployments.steps.pip_install_requirements:
       directory: "{{ clone-step.directory }}/flows"
       requirements_file: requirements.txt
       stream_output: False

deployments:
  - name: add_k3s_envoy_flow
    work_pool_name: k3s-test
    steps:
      - *common_pull
    parameters: {}
    tags:
      - k3s

### Step 2: Update Your Python Deployment Script In your Python deployment script, refer to this YAML configuration to set up your deployment steps:

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python
from prefect.deployments import Deployment
from prefect.filesystems import GitRepository
from prefect.infrastructure.kubernetes import KubernetesJob
from prefect.client import Secret

# Load secrets and credentials
gitlab_token = Secret.load("gitlab-token")

# Source repository configuration
source = GitRepository(
    url="<https://gitlab.com/your/repo.git>",
    credentials={"access_token": gitlab_token}
)

# Kubernetes job configuration
kubernetes_job = KubernetesJob(
    image="prefecthq/prefect:2.10.11-python3.10"
)

# YAML configuration file path
config_file = "path/to/deployment.yaml"

# Define the flow
@flow
def add_flow():
    print("Running add_flow.")

# Create the deployment
deployment = Deployment.build_from_yaml(
    flow_location="flows/flows.py:add_flow",
    config_file=config_file,
    infra_overrides={"image": "prefecthq/prefect:2.10.11-python3.10"},
    source=source,
    work_pool_name="k3s-test",
    tags=["k3s"],
    name="add_k3s_envoy_flow"
)

# Apply the deployment
deployment.apply()

# Run the deployment
flow_run = run_deployment(
    name="add_k3s_envoy_flow/k3s-test",
    as_subflow=False,
    parameters={},
    timeout=0
)

### Explanation 1. YAML Configuration: - This YAML file defines common deployment steps under

definitions

. - The steps include setting the working directory, cloning the repository, and installing the requirements. - The

deployments

section applies these steps to the

add_k3s_envoy_flow

. 2. Python Script: - Load the required secrets and credentials. - Define the

source

and

kubernetes_job

configurations. - Reference the YAML file with

build_from_yaml

method to include the steps in your deployment. - Apply and run the deployment as needed. ### References - Prefect YAML Deployment Configuration - Prefect Git Clone Step - Prefect Pip Install Step This approach ensures that your repository is cloned and the requirements are installed before running the flow. If you have further questions, feel free to ask.

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