Fugo Takefusa
08/07/2025, 2:41 AMMarvin
08/07/2025, 2:41 AMMarvin
08/07/2025, 2:42 AMprefect.yaml
file:
1. Using a requirements.txt file:
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:
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:
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!Fugo Takefusa
08/07/2025, 2:44 AMMarvin
08/07/2025, 2:44 AMMarvin
08/07/2025, 2:45 AMbash
# Generate requirements.txt from Pipfile.lock
pipenv lock -r > requirements.txt
Then in your `prefect.yaml`:
yaml
deployments:
- name: my-deployment
steps:
- prefect.deployments.steps.pip_install_requirements:
requirements_file: requirements.txt
2. Use a Custom Shell Command Step:
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):
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!Fugo Takefusa
08/07/2025, 2:48 AMMarvin
08/07/2025, 2:48 AMMarvin
08/07/2025, 2:49 AMDockerfile
that installs the MSODBC driver:
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:
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:
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?