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<@ULVA73B9P> With prefect 3, and a system worker, ...
# ask-marvin
j
@Marvin With prefect 3, and a system worker, deployment failed with error exception: ModuleNotFoundError("No module named 'pandas'")
m
✅ thought for 102.0 seconds
Based on the research and issue review, I can help you resolve the 
ModuleNotFoundError: No module named 'pandas'
error with your system worker. This is a common issue that occurs when the required dependencies aren't available in the environment where the flow is being executed. Here are the solutions, in order of recommendation: 1. Add Dependencies to 
prefect.yaml
 The most straightforward solution is to specify your dependencies in your 
prefect.yaml
file. Here's how to do it:
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yaml
   deployments:
     - name: your-deployment-name
       work_pool:
         name: your-work-pool
       job:
         variables:
           pip_packages: ["pandas"]  # Add any other required packages here
2. Create a Requirements File Alternatively, you can create a 
requirements.txt
file in your project directory and reference it in your `prefect.yaml`:
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yaml
   deployments:
     - name: your-deployment-name
       work_pool:
         name: your-work-pool
       job:
         variables:
           pip_requirements: ["./requirements.txt"]
3. Install Dependencies in the Worker Environment If you're running the system worker locally, you can also install the required packages directly in the environment where the worker is running:
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bash
   pip install pandas
However, this is less recommended as it doesn't make the dependencies explicit in your deployment configuration. 4. Use a Virtual Environment You can create a dedicated virtual environment for your flow and worker:
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bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install pandas prefect
Then run your worker from this environment. Important Notes: - Make sure to redeploy your flow after making changes to the 
prefect.yaml
file - The system worker will install the specified packages before executing the flow - If you're using multiple flows with different dependencies, it's recommended to use separate deployments with their own dependency specifications - For production environments, consider using Docker-based workers instead of system workers, as they provide better dependency isolation Would you like me to provide more specific guidance based on your particular setup? It would be helpful to know: 1. Are you using a 
prefect.yaml
file currently? 2. Are there other dependencies besides pandas that you need? 3. Is this for a development or production environment?
j
The Process worker is a docker container with only the official prefect image
@Marvin The Process worker is a docker container with only the official prefect image. I'm using a prefect.yaml file for deployment
m
✅ thought for 30.3 seconds
You have two main options to resolve this: 1. Recommended: Create a Custom Docker Image Create a 
Dockerfile
that extends the official Prefect image and adds your required dependencies:
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dockerfile
   FROM prefecthq/prefect:2-python3.10
   RUN pip install pandas
   # Add any other dependencies you need
Then build and use this image in your `prefect.yaml`:
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yaml
   deployments:
     - name: your-deployment-name
       work_pool:
         name: your-work-pool
         job_variables:
           image: your-custom-image:tag
2. Alternative: Use pip_packages in prefect.yaml If you want to keep using the official Prefect image, you can specify the packages to install in your `prefect.yaml`:
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yaml
   deployments:
     - name: your-deployment-name
       work_pool:
         name: your-work-pool
       job:
         variables:
           image: prefecthq/prefect:2-python3.10
           pip_packages: ["pandas"]
The first approach (custom Docker image) is recommended for production environments because: - It's more reliable and reproducible - It's faster since dependencies don't need to be installed on every flow run - You have better control over the exact versions of packages - You can include any system-level dependencies if needed For either approach, make sure to: 1. Update your deployment after making changes:
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bash
   prefect deploy
2. Ensure your Process worker can access either the custom image or the internet to download packages Would you like me to provide more details about either approach? Also, it would be helpful to know: 1. Are you using any other dependencies besides pandas? 2. Is this for a production environment? 3. Do you have the ability to build and host custom Docker images in your environment?
j
Can you point me the documentation about all options of the prefect.yml file ?
@Marvin Can you point me the documentation about all options of the prefect.yml file ?
m
✅ thought for 67.5 seconds
Here's a comprehensive guide to the 
prefect.yaml
configuration options: ### Basic Structure
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yaml
# Optional metadata about your project
name: your-project-name
description: "Project description"

# Define how your deployment should be built
build: null  # or specific build instructions

# Define one or more deployments
deployments:
  - name: your-deployment-name
    version: null  # optional version
    tags: []  # optional tags
    description: "Deployment description"
    schedule: null  # optional schedule
    flow_name: your_flow_name
    entrypoint: path/to/your/flow.py:flow_function
    parameters: {}  # optional flow parameters
    work_pool:
      name: your-work-pool-name
      work_queue_name: default  # optional
      job:
        # Job-specific configurations
        variables:
          # Worker-specific variables
          image: "prefecthq/prefect:2-python3.10"  # Docker image
          pip_packages: []  # packages to install
          pip_requirements: []  # requirements files to install
          python_version: "3.10"
          working_dir: null
          # Additional environment variables
          env:
            KEY: "value"
### Key Sections 1. Project Metadata
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yaml
   name: string
   description: string
2. Build Configuration
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yaml
   build:
     python_version: string
     python_packages: list[string]
     commands: list[string]
3. Deployment Configuration
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yaml
   deployments:
     - name: string
       version: string
       tags: list[string]
       description: string
       schedule: object  # Schedule configuration
       flow_name: string
       entrypoint: string  # format: "path/to/file.py:flow_function"
       parameters: dict  # Flow parameters
4. Work Pool Configuration
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yaml
   work_pool:
     name: string
     work_queue_name: string
     job:
       variables:
         # Process/Docker worker variables
         image: string
         pip_packages: list[string]
         pip_requirements: list[string]
         working_dir: string
         env: dict
5. Job Variables for Different Worker Types
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yaml
   job:
     variables:
       # For Process workers with Docker
       image: string
       command: list[string]
       volumes: list[string]
       networks: list[string]
       
       # For Python environment management
       pip_packages: list[string]
       pip_requirements: list[string]
       conda_env: string
       
       # General configuration
       working_dir: string
       env: dict
### Important Links - Prefect YAML Documentation - Work Pools and Workers Guide - Deployment Configuration ### Usage Tips 1. You can initialize a new 
prefect.yaml
using:
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bash
   prefect init
2. Deploy using a specific yaml file:
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bash
   prefect deploy --prefect-file path/to/prefect.yaml
3. You can have multiple deployment configurations in a single 
prefect.yaml
file. 4. The file is validated when you run 
prefect deploy
, so any configuration errors will be caught then. Would you like me to elaborate on any specific section or provide examples for a particular use case?
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