@Marvin When we run

python flows/my-flow.py

to deploy, how does prefect know about python req?

When deploying a Prefect flow, you can specify Python package dependencies that should be installed at runtime. There are two primary ways to define these dependencies: 1. Directly in your deployment script, you can set

job_variables

to include a

pip_packages

list with the packages you want to install. Here's an example of how you would do this:

python
from prefect import flow

if __name__ == "__main__":
    flow.from_source(
        source="<https://github.com/your-repo/demo.git>",
        entrypoint="flow.py:my_flow",
    ).deploy(
        name="test-managed-flow",
        work_pool_name="my-managed-pool",
        job_variables={"pip_packages": ["pandas", "prefect-aws"]}
    )

2. Alternatively, you can specify a

requirements.txt

file that includes a list of all the required Python packages. In both cases, these dependencies are used to set up the Python environment when the flow is executed. This means you don’t have to worry about manually installing dependencies on the remote infrastructure; Prefect ensures that the specified dependencies are available when executing your flow. You can find more detailed information and instructions in Prefect's documentation here.

@Marvin what if I am building multiple images?

When handling Python dependencies for Prefect flows especially when building multiple Docker images, you have a couple of options: 1. Use the default

prefecthq/prefect

image, and configure

prefect.deployments.steps.pip_install_requirements

in the pull step to install dependencies at runtime. This approach allows you to use a single base image and specify dependencies at the time of deployment which are then installed when the flow runs. Here's an example configuration for the step in your deployment script:

yaml
   steps:
     - pull:
         image: prefecthq/prefect:latest
         pip_install_requirements: ["path/to/requirements.txt"]

2. If your flows require extra dependencies or shared libraries that are expensive to install (such as those requiring compilation), it's recommended to build a custom Docker image instead. This strategy involves creating a Docker image that includes all necessary dependencies and shared task definitions. Each of your flows can rely on this shared image, and source code can be stored externally. This approach is advantageous because it streamlines development; the shared image only needs to be rebuilt when dependencies change, not when source code for the flows changes. Here is an outline of what your Dockerfile might look like when building a custom image:

dockerfile
# Start from a specific Python base image
FROM python:3.7

# Install necessary system packages
RUN apt-get update && apt-get install -y --no-install-recommends <system-packages>

# Install Python package dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy flow code, set up entrypoint, etc.
COPY . /app
WORKDIR /app

With a custom Docker image, each flow can specify which image to use in the deployment process, allowing you to tailor the environment specifically to the flow's needs. The flows would be launched using the custom image already having all dependencies pre-installed, which can significantly reduce the startup time of your flows and ensure that all necessary libraries are present. For more detailed information and best practices on using Docker with Prefect, you can check the Prefect Docker guide.

@Marvin Is there a way to specify the req file?

Yes, you can specify a Python requirements file (

requirements.txt

) for Prefect flows when you're building custom Docker images or dynamically installing dependencies at runtime. Here's how you can do it in both scenarios: 1. Custom Docker Image: When building a Docker image for your flow, you can copy the requirements file into the image and use

pip

to install the packages during the build process. Here's an example of what your

Dockerfile

might look like:

Dockerfile
FROM python:3.8

# Copy the requirements file into the image
COPY requirements.txt /app/requirements.txt

# Install the Python dependencies
RUN pip install --no-cache-dir -r /app/requirements.txt

# Copy the rest of your application code
COPY . /app

WORKDIR /app

# Set the command to run your application
CMD ["python", "your-flow-script.py"]

You would then build the image with the Docker CLI and push it to a Docker registry that your execution environment can access. 2. Runtime Installation: If you are using the default

prefecthq/prefect

base image or another image where you wish to install dependencies at runtime, you can specify

job_variables

with a key

pip_install

pointing to the path of your requirements file when deploying the flow.

python
from prefect import flow

if __name__ == "__main__":
    flow.from_source(
        source="<https://github.com/your-repo/demo.git>",
        entrypoint="flow.py:my_flow",
    ).deploy(
        name="my-flow-deployment",
        work_pool_name="my-managed-pool",
        job_variables={
            "pip_install": "requirements.txt"
        }
    )

This will instruct Prefect to install the Python packages specified in the

requirements.txt

file at runtime, before the flow starts executing. Make sure that the file path provided is correct and that it can be accessed from the context where the flow will run. If the

requirements.txt

is part of your source code repository, ensure it is in a location where Prefect can retrieve it during the deployment process.

Copy code

pip_install

Marvin means

EXTRA_PIP_PACKAGES

here, but that accepts space delimited packages to

pip install

- it doesn't accept a

requirements.txt

like that, otherwise the above is mostly correct +1 to the custom image route and install extras at runtime if needed