@Marvin local setup of Prefect 2. Two git branches in the repo (where .prefect and prefect.yaml also are stored). Created a new deployment

dud_25_01_02

from within the git-branch

geo_matching

and did

prefect server start

as well as

prefect worker start --pool "local-work-pool"

. See the simple prefect.yaml below. In the GUI, when starting a flow run of

dud_25_01_02

, will the code of the flow be running from the main branch or the branch

geo_matching

where I was checked in, when all of the above was done.

name: goal_100_mastr_pipeline
prefect-version: 2.14.16
build:

locations
push:

locations
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /Users/Gunnar/Documents/IT_Projects/goal_100_mastr_pipeline
deployments:
- name: dud_25_01_02
  version:
  tags: []
  description:
  entrypoint: src/download_update_flow.py:download_update_flow
  parameters: {}
  work_pool:
    name: local-work-pool7
    work_queue_name:
    job_variables: {}
  schedule:
  is_schedule_active: true

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@Marvin so if prefect does not automatically switch branches, what does it do? Is the flow-code stored somewhere else beside the ,py in the branch of the repo? Is it referenced somehow by prefect?

Prefect does not store the flow code itself in its server or cloud. Instead, it references the location of the code through configurations specified during deployment. Here’s how it generally works: 1. Flow Code Storage: Prefect allows you to specify where your flow code is stored. This could be a local file system, a Git repository, a Docker image, or cloud storage like AWS S3, Azure Blob Storage, or Google Cloud Storage. The choice depends on your deployment needs and infrastructure. 2. Deployment Configuration: When you create a deployment, you define the location of your flow code in the

prefect.yaml

file or through the Python API. This includes specifying the entry point (the path to the Python file and the function name where the flow is defined). 3. Execution Environment: When you run a flow, the Prefect worker retrieves the code from the specified storage location. For example, if your code is stored in a Git repository, the worker will clone the repository and execute the flow from there. 4. Local vs Remote Execution: For local executions, Prefect might use local file system storage by default. For remote executions, you need to configure a storage option that suits your needs. For more detailed guidance on storing and referencing flow code, you can explore the Prefect documentation: - Prefect Storage Concepts - Flow Code Storage Options In summary, Prefect references your flow code based on your deployment configuration, ensuring that the code is accessible to the execution environment when needed.

@Marvin And in my local case as demonstrated in the prefect.yaml above?

In your local setup, as indicated by the

prefect.yaml

you've shared, here's how Prefect handles your flow code: 1. Local File System: Since you're running everything locally and haven't specified any cloud or remote storage, Prefect will use the local file system. This means that the flow code will be executed from the directory specified in your deployment configuration. 2. Working Directory: Your

prefect.yaml

specifies a working directory using the

set_working_directory

step:

yaml
   pull:
   - prefect.deployments.steps.set_working_directory:
       directory: /Users/Gunnar/Documents/IT_Projects/goal_100_mastr_pipeline

This ensures that when the flow runs, it will use the code from this specific directory on your local machine. 3. Entrypoint: The entrypoint for your flow is defined as:

yaml
   entrypoint: src/download_update_flow.py:download_update_flow

This means Prefect will look for the

download_update_flow

function in the

src/download_update_flow.py

file within the specified working directory. 4. Execution: When you start a flow run from the Prefect UI or CLI, it will execute the flow code directly from your local setup as defined in the

prefect.yaml

. In essence, Prefect will execute the flow code from the

geo_matching

branch because that's where you created the deployment and that's where your local file system will point to when running the flow.

@Marvin for my local setup this also implies, that changes to the code of the flow will be accounted for when the flow is run, although the deployment was not updated or a new deployment created

Yes, in your local setup, changes to the flow code will be reflected the next time you run the flow, even if you don't update or create a new deployment. This is because Prefect will execute the flow code directly from your local file system each time a flow run is initiated. Since the deployment configuration points to the specific directory and entrypoint file, any modifications you make to the flow code in that file will be picked up during execution. This behavior is typical for local deployments where the flow code is accessed directly from the file system.

@Marvin in the .yaml the pull section currently looks the following

# pull section allows you to provide instructions for cloning this project in remote locations
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /Users/Gunnar/Documents/IT_Projects/goal_100_mastr_pipeline

what exactly is this about, and since this is an absolute local path, would it prevent successfully running a deployment from another cloned location? And how would we go about, if for example a colleague is to run the deployment locally from his machine?

The

pull

section in your

prefect.yaml

file is part of Prefect's deployment configuration. It defines how Prefect retrieves or sets up the deployment in remote execution environments. Each step within the

pull

section is executed sequentially and can include actions such as setting the working directory. The line you're using:

yaml
- prefect.deployments.steps.set_working_directory:
    directory: /Users/Gunnar/Documents/IT_Projects/goal_100_mastr_pipeline

indicates that Prefect should set the working directory to an absolute path on your local machine. This would indeed cause issues if the deployment is run from a different location, such as a colleague's machine, because that path likely doesn't exist there. To make this more portable, you might want to use environment variables or relative paths that can be dynamically set based on the execution environment. You could, for example, use a path relative to the deployment directory or set an environment variable that each user can configure on their machine. For more details, you can refer to the Prefect 3.x documentation on deployment configurations: Prefect YAML Documentation. If you need further customization, you might consider using Prefect's capabilities to reference environment variables or other dynamic configurations. Would you like more information on setting up environment variables or using relative paths?

@Marvin since the provided absolute path is just the directory of repository, eg where prefect.yaml is located, would it be suitable to just change it to

./

?

Yes, changing the directory to

./

would be a suitable solution if your

prefect.yaml

file is located in the root of your repository. This way, it sets the working directory to the current directory where the deployment is executed, making it more portable across different environments and users. Here's how you can modify it:

yaml
pull:
- prefect.deployments.steps.set_working_directory:
    directory: ./

This relative path will ensure that the working directory is set to wherever the

prefect.yaml

file is located, assuming the deployment is executed from the root of the repository. This should work well for different users who clone the repository to their local machines. If you have more specific requirements or need further assistance, feel free to ask!

@Marvin would I need to register this change with

prefect deploy ...

?

Yes, after making changes to your

prefect.yaml

file, you will need to register these changes using the

prefect deploy

command. This command processes the

prefect.yaml

file, resolves any variables or references, and registers the deployment specification with Prefect. Here's a brief overview of what happens when you run `prefect deploy`: 1. The

prefect.yaml

file is loaded, and the steps in the

build

,

push

, and

pull

sections are processed. 2. The deployment is registered with Prefect, incorporating any updates you've made. So, to ensure your changes take effect, execute the

prefect deploy

command in the directory containing your

prefect.yaml

file. For more details, you can refer to the Prefect documentation: Prefect YAML Deployment.

@Marvin for a colleague to run this deployment locally, would git clone be enough?

For a colleague to run the deployment locally, simply cloning the repository with

git clone

is usually the primary step. However, there are additional considerations to ensure everything runs smoothly: 1. Dependencies: Ensure that any dependencies required by the flow are installed. This can often be managed by including a

requirements.txt

file in your repository and using a step in your

prefect.yaml

to install these dependencies. 2. Environment Variables: If your deployment relies on environment variables, make sure these are set up on your colleague's machine. You can provide a

.env

file or instructions on what needs to be configured. 3. Prefect Setup: Your colleague will need to have Prefect installed and configured on their machine. This includes setting up any necessary API keys or connecting to a Prefect server if needed. 4. Run Deployment: After cloning and setting up the environment, your colleague can run

prefect deploy

to register the deployment and then trigger flow runs as needed. For more detailed guidance, you can refer to the Prefect documentation on local deployment setups: Prefect Deployment Guide. If you have any specific configurations or additional setup steps, ensure that these are documented for your colleague.

@Marvin another thing. My colleague is able to work with python-repositories on his windows-machine. Is he able to install prefect within the repo on a windows machine?

Yes, your colleague can install Prefect on a Windows machine. Prefect is a Python package and can be installed using

pip

, which is compatible with Windows environments. Here are the steps: 1. Ensure Python 3.9 or Newer: Prefect requires Python 3.9 or newer. Make sure your colleague has an appropriate Python version installed. 2. Virtual Environment: It's recommended to install Prefect within a Python virtual environment to manage dependencies cleanly. 3. Installation: Prefect can be installed via Windows PowerShell, Command Prompt, or using

conda

. The basic installation command using

pip

is:

bash
   pip install prefect

4. Path Configuration: After installation, your colleague may need to manually add the Python local packages

Scripts

folder to their PATH environment variable. This is often required for the Prefect CLI and other executables to be recognized in the command line. 5. Verification: They can verify the installation by running:

bash
   prefect version

This should display the installed version of Prefect. For more detailed instructions, you can refer to the Prefect Installation Guide. If there are any specific issues or requirements, feel free to ask!