<@ULVA73B9P> when I define deployments with yml. h...
# ask-marvin
f
@Marvin when I define deployments with yml. how to specify python package dependencies for a deployment?
m
thought for 112.1 seconds
Let me provide you with a clear explanation of how to specify Python package dependencies in your deployment YAML configuration. In Prefect 3.x, there are two main ways to specify dependencies in your
prefect.yaml
file: 1. Using a requirements.txt file:
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yaml
deployments:
  - name: my-deployment
    steps:
      - prefect.deployments.steps.pip_install_requirements:
          requirements_file: requirements.txt
          requirements: null
2. Directly specifying packages in the YAML:
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yaml
deployments:
  - name: my-deployment
    steps:
      - prefect.deployments.steps.pip_install_requirements:
          requirements_file: null
          requirements: ["pandas>=2.0.0", "requests>=2.28.0"]
You can choose either approach based on your needs: - Use
requirements_file
if you have a separate requirements.txt file (recommended for larger projects) - Use
requirements
to specify packages directly in the YAML (good for simpler deployments) Important notes: 1. These steps run when the deployment is executed, ensuring the required packages are available in the execution environment 2. You can specify version constraints using standard pip syntax (e.g.,
>=
,
==
,
~=
) 3. Each deployment in your YAML can have its own dependencies Here's a complete example with multiple deployments and different dependencies:
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yaml
deployments:
  - name: etl-deployment
    steps:
      - prefect.deployments.steps.pip_install_requirements:
          requirements: ["pandas>=2.0.0", "sqlalchemy>=2.0.0"]
    flow_name: etl_flow
    entrypoint: flows/etl.py:etl_flow

  - name: ml-deployment
    steps:
      - prefect.deployments.steps.pip_install_requirements:
          requirements_file: ml_requirements.txt
    flow_name: training_flow
    entrypoint: flows/training.py:training_flow
For more detailed information about deployment configuration options, you can refer to the Prefect documentation on prefect.yaml. Let me know if you need any clarification or have questions about more complex dependency scenarios!
f
@Marvin umm can we use piplock?
m
thought for 59.9 seconds
Let me explain how you can work with Pipfile.lock in your Prefect deployments. While there's no direct built-in support for Pipfile.lock, you have a few options: 1. Convert Pipfile.lock to requirements.txt (Recommended Approach):
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bash
# Generate requirements.txt from Pipfile.lock
pipenv lock -r > requirements.txt
Then in your `prefect.yaml`:
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yaml
deployments:
  - name: my-deployment
    steps:
      - prefect.deployments.steps.pip_install_requirements:
          requirements_file: requirements.txt
2. Use a Custom Shell Command Step:
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yaml
deployments:
  - name: my-deployment
    steps:
      - prefect.deployments.steps.run_shell_script:
          script: pipenv install
3. Hybrid Approach (if you need both Pipfile.lock and additional requirements):
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yaml
deployments:
  - name: my-deployment
    steps:
      - prefect.deployments.steps.run_shell_script:
          script: pipenv install
      - prefect.deployments.steps.pip_install_requirements:
          requirements: ["some-additional-package>=1.0.0"]
Important Notes: - The first approach (converting to requirements.txt) is most reliable as it works with Prefect's built-in dependency management - If using the shell script approach, ensure
pipenv
is installed in your execution environment - Remember that the environment where your flow runs needs to have access to
pipenv
if you're using the shell script approach - Consider automating the conversion of Pipfile.lock to requirements.txt as part of your deployment process if you want to keep using Pipenv for development Let me know if you need any clarification or have questions about implementing any of these approaches!
f
@Marvin can we include msodbc connector? it's not pyton pakcage
m
thought for 60.0 seconds
For including the Microsoft ODBC connector in your deployment, you'll need to use a Docker-based approach since it's a system-level dependency. Here's how you can do it: 1. First, create a
Dockerfile
that installs the MSODBC driver:
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dockerfile
FROM python:3.9

# Add Microsoft repository
RUN curl <https://packages.microsoft.com/keys/microsoft.asc> | apt-key add - \
    && curl <https://packages.microsoft.com/config/debian/11/prod.list> > /etc/apt/sources.list.d/mssql-release.list

# Install ODBC Driver
RUN apt-get update \
    && ACCEPT_EULA=Y apt-get install -y msodbcsql18 \
    && apt-get install -y unixodbc-dev \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

# If you have a requirements.txt
COPY requirements.txt .
RUN pip install -r requirements.txt

# If you're using Pipfile.lock
# COPY Pipfile* ./
# RUN pip install pipenv && pipenv install --system --deploy
2. Then in your
prefect.yaml
, specify the Docker build:
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yaml
deployments:
  - name: my-sql-deployment
    version: null
    tags: []
    description: null
    schedule: null
    flow_name: your_flow_name
    entrypoint: your_flow_file.py:your_flow_function
    parameters: {}
    work_pool:
      name: your-work-pool
      work_queue_name: default
    infrastructure:
      type: docker-container
      env: {}
      labels: {}
      name: null
      image: null
      dockerfile: ./Dockerfile  # Point to your Dockerfile
      platform: linux/amd64
      image_registry: null
3. If you need to verify the ODBC connection in your flow, you can add a simple test:
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python
from prefect import flow
import pyodbc

@flow
def test_odbc_connection():
    try:
        # List available drivers
        drivers = pyodbc.drivers()
        print("Available ODBC drivers:", drivers)
        
        # Your connection string
        conn_str = (
            "Driver={ODBC Driver 18 for SQL Server};"
            "Server=your_server;"
            "Database=your_database;"
            "UID=your_username;"
            "PWD=your_password;"
            "TrustServerCertificate=yes;"
        )
        
        conn = pyodbc.connect(conn_str)
        print("Successfully connected to database")
        conn.close()
        
    except Exception as e:
        print(f"Error: {e}")
Important Notes: 1. Make sure your Docker infrastructure is properly configured in your work pool 2. The example uses Debian-based installation. If you're using a different base image, you'll need to adjust the installation commands 3. You might need to add additional system dependencies depending on your specific needs 4. Remember to include
pyodbc
in your Python requirements if you're using it to connect to the database Additional Tips: - Test the Dockerfile locally before deploying - Make sure your work pool has sufficient permissions to build Docker images - Consider using environment variables for sensitive connection information - You might need to adjust the ODBC driver version (18 in this example) based on your requirements Would you like me to provide more specific details about any part of this setup, or do you need help with a particular aspect of the configuration?