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# ask-marvin
s
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m
So, you're asking how to efficiently monitor and run multiple independent projects with possibly many deployments each. Seems like you want to avoid chaos in your setups. Which version of Prefect are you using? I need to know to get you the right information.
a
@Marvin 3.0.2
m
### Best Practices for Monitoring and Running Multiple Independent Projects in Prefect 3.x #### 1. Handling Multiple Deployments To efficiently manage multiple deployments of the same or different flows, you can use the 
deploy
function to create these deployments. Each project can have its configurations, dependencies, and workflows. Here’s an example to illustrate creating multiple deployments:
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python
from prefect import deploy, flow

@flow(log_prints=True)
def buy():
    print("Buying securities")

@flow(log_prints=True)
def sell():
    print("Selling securities")

if __name__ == "__main__":
    deploy(
        buy.to_deployment(name="buy-deploy", work_pool_name="my-docker-pool"),
        sell.to_deployment(name="sell-deploy"),
        image="my-registry/my-image:dev",
        push=False,
    )
More on Multiple Deployments #### 2. Using CI/CD for Deployment Management For complex projects, especially those using monorepos or multiple environments, integrating Prefect with GitHub Actions or another CI/CD tool helps maintain a smooth workflow. Deployments can be automatically selected based on conditions such as branch changes or folder modifications:
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yaml
on:
  push:
    branches:
      - stg
      - main
    paths:
      - "project_1/**"
Example Repository with CI/CD Workflow #### 3. Concurrency Management When dealing with multiple tasks or flows, managing concurrency ensures you do not exceed resource limits. Prefect provides concurrency limits to control the number of tasks running in parallel:
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python
from prefect.concurrency.sync import concurrency

@task
def database_query(query):
    with concurrency("database", occupy=1):
        # execute query
        ...
Concurrency Limits #### 4. Organizing Workspaces Workspaces in Prefect can be used to separate environments like staging and production. This approach allows you to isolate the changes and impacts of your deployments.
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yaml
- stg
- main
#### 5. Dynamic vs. Static Infrastructure Choose between static infrastructure (long-lived) or dynamic infrastructure (scalable) based on your use case. Using the 
serve
method on the Flow object is a good fit for managing multiple flows on static infrastructure. Static vs. Dynamic Infrastructure ### References: 1. Deploy to CI/CD Workspaces 2. Concurrency Management 3. Managing Deployments These best practices should help you keep your Prefect projects organized and running smoothly! If you need more specific information, feel free to ask.
a
@Marvin Suppose I have 2 independent projects and each have separate deployments defined via YAML. Projects are stored locally with one folder for each project. Is it adviseable to put all folders in one main folder? And how do I go about deploying each project
m
Given your setup with two independent projects each having their own YAML deployments, let's break down the approach. ### Is it Advisable to have a Main Folder? Organizing your projects into a common main folder is a commonly accepted practice. This will simplify the management of your projects, and ensure a cleaner structure. More importantly, it allows shared CI/CD tools to easily track and manage changes across multiple projects. ### Deploying Each Project Step-by-Step Guide to Deploy Each Project: 1. Directory Structure: Here's an example of how you might structure your directories:
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main_project_directory/
    ├── project_1/
    │   ├── flows/
    │   └── prefect.yaml
    └── project_2/
        ├── flows/
        └── prefect.yaml
2. Sample 
prefect.yaml
File: Each project will have its own 
prefect.yaml
that defines its deployment. project_1/prefect.yaml:
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yaml
    name: project_1
    version: 1
    storage:
      block: "local"
      path: "./flows"
    infrastructure:
      type: "process"
project_2/prefect.yaml:
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yaml
    name: project_2
    version: 1
    storage:
      block: "local"
      path: "./flows"
    infrastructure:
      type: "process"
3. Deploying Projects: Use the Prefect CLI to deploy each project locally. Navigate to each project's directory and run the deployment command.
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bash
    cd main_project_directory/project_1
    prefect deployment build --apply
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bash
    cd main_project_directory/project_2
    prefect deployment build --apply
4. Using CI/CD for Automated Deployments: Set up CI/CD pipelines (e.g., GitHub Actions) to handle automatic deployments when changes are pushed. Here’s a simple example using GitHub Actions: .github/workflows/deploy-projects.yaml:
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yaml
    name: Deploy Projects

    on:
      push:
        branches:
          - main

    jobs:
      deploy_project_1:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v2
          - name: Set up Python
            uses: actions/setup-python@v2
            with:
              python-version: '3.8'
          - name: Install Prefect
            run: pip install prefect
          - name: Deploy Project 1
            run: |
              cd project_1
              prefect deployment build --apply

      deploy_project_2:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v2
          - name: Set up Python
            uses: actions/setup-python@v2
            with:
              python-version: '3.8'
          - name: Install Prefect
            run: pip install prefect
          - name: Deploy Project 2
            run: |
              cd project_2
              prefect deployment build --apply
This setup keeps each project self-contained while still benefiting from the shared structure of a parent directory. It also simplifies automation with CI/CD tools. For more information and detailed examples, you can refer to the Prefect 3.x documentation on deployments and best practices: - Deploy via YAML - Infrastructure Examples If you have any more specific questions or need further assistance setting up your projects, let me know!
a
@Marvin What if each project also has its own requirements file?
m
If each project has its own requirements file, you can definitely accommodate that within your setup. This is a common scenario, and it ensures that each project can manage its dependencies independently. Here's how you can handle this: ### Directory Structure with Requirements Files
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main_project_directory/
├── project_1/
│   ├── flows/
│   ├── requirements.txt
│   └── prefect.yaml
└── project_2/
    ├── flows/
    ├── requirements.txt
    └── prefect.yaml
### Sample 
prefect.yaml
Files You would need to modify each 
prefect.yaml
file to include steps to install the dependencies specified in the 
requirements.txt
. project_1/prefect.yaml:
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yaml
name: project_1
version: 1
storage:
  block: "local"
  path: "./flows"
infrastructure:
  type: "process"
pull_steps:
  - prefect.infrastructure.steps.ProcessEnv:
      env:
        PIP_INSTALL: "pip install -r ./requirements.txt"
project_2/prefect.yaml:
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yaml
name: project_2
version: 1
storage:
  block: "local"
  path: "./flows"
infrastructure:
  type: "process"
pull_steps:
  - prefect.infrastructure.steps.ProcessEnv:
      env:
        PIP_INSTALL: "pip install -r ./requirements.txt"
### Deploying Projects with Requirements When deploying each project, Prefect will automatically run the 
pip install
command to install the dependencies listed in 
requirements.txt
. ### Automating with CI/CD Here’s a simple example of how to set up GitHub Actions to handle the deployments and install the requirements for each project: .github/workflows/deploy-projects.yaml:
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yaml
name: Deploy Projects

on:
  push:
    branches:
      - main

jobs:
  deploy_project_1:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 1 Requirements
        run: |
          cd project_1
          pip install -r requirements.txt
      - name: Deploy Project 1
        run: |
          cd project_1
          prefect deployment build --apply

  deploy_project_2:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 2 Requirements
        run: |
          cd project_2
          pip install -r requirements.txt
      - name: Deploy Project 2
        run: |
          cd project_2
          prefect deployment build --apply
### Conclusion By using the 
pull_steps
section in 
prefect.yaml
, each project can define what needs to be done to set up its environment, including installing its requirements. This way, you maintain independent environments for each project even when deploying from a shared CI/CD pipeline. For further reading, you can check: - Prefect Deployment Guide - CI/CD with Prefect If you have more questions or need further details, feel free to ask!
a
@Marvin For deployment entrypoint in 
prefect.yaml
, is it possible to not call the flow directly? I'm using argparse to dynamically define flow inputs based on config arguments.
m
Yes, it is possible to use an entry point that does not directly call the flow. You can use a custom script where you leverage 
argparse
to dynamically define the flow inputs based on config arguments. Here's how you can do it: ### Example Structure In each project folder, create a script (e.g., 
run_flow.py
) that handles argument parsing and flow execution:
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plaintext
main_project_directory/
├── project_1/
│   ├── flows/
│   ├── prefect.yaml
│   ├── requirements.txt
│   └── run_flow.py
└── project_2/
    ├── flows/
    ├── prefect.yaml
    ├── requirements.txt
    └── run_flow.py
### Sample 
run_flow.py
with argparse project_1/run_flow.py:
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python
import argparse
from prefect import flow

@flow
def my_flow(my_param):
    print(f"Flow received parameter: {my_param}")

def main():
    parser = argparse.ArgumentParser(description="Run Prefect flow with dynamic parameters.")
    parser.add_argument("--my_param", type=str, required=True, help="Parameter for the flow.")
    args = parser.parse_args()
    
    my_flow(args.my_param)

if __name__ == "__main__":
    main()
### 
prefect.yaml
Configuration Modify your 
prefect.yaml
to use 
run_flow.py
as the entry point: project_1/prefect.yaml:
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yaml
name: project_1
version: 1
storage:
  block: "local"
  path: "./flows"
infrastructure:
  type: "process"
pull_steps:
  - prefect.infrastructure.steps.ProcessEnv:
      env:
        PIP_INSTALL: "pip install -r ./requirements.txt"
entrypoint: "run_flow.py"
By setting the entry point to your custom script, Prefect will run 
run_flow.py
, which can use 
argparse
to handle dynamic input parameters. ### Deploying the Project You can then deploy the project as usual:
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bash
cd main_project_directory/project_1
prefect deployment build --apply
#### Automating with CI/CD .github/workflows/deploy-projects.yaml:
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yaml
name: Deploy Projects

on:
  push:
    branches:
      - main

jobs:
  deploy_project_1:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 1 Requirements
        run: |
          cd project_1
          pip install -r requirements.txt
      - name: Deploy Project 1
        run: |
          cd project_1
          prefect deployment build --apply

  deploy_project_2:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 2 Requirements
        run: |
          cd project_2
          pip install -r requirements.txt
      - name: Deploy Project 2
        run: |
          cd project_2
          prefect deployment build --apply
### Conclusion This setup allows you to dynamically define flow inputs using 
argparse
within a custom script, facilitating complex workflows and dynamic configurations. For more details, refer to: - Prefect Deployment Guide - Prefect CLI Commands Let me know if you need further assistance!
a
@Marvin Doesn't work, I get 
MissingFlowError
if I try to call the entire python file
m
The 
MissingFlowError
occurs because Prefect requires an explicit flow definition in the entry point to identify and register the flow. Instead of calling the entire Python file, you need to specify the exact flow to Prefect. ### Solution: Modify Prefect CLI Command in Deployment Script To work around this, use a script to handle 
argparse
and then call this script separately in your CI/CD pipeline or execution environment rather than as the entry point in Prefect. Here's how you can do it correctly: ### Directory Structure Your directory structure remains the same:
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main_project_directory/
├── project_1/
│   ├── flows/
│   ├── prefect.yaml
│   ├── requirements.txt
│   └── run_flow.py
└── project_2/
    ├── flows/
    ├── prefect.yaml
    ├── requirements.txt
    └── run_flow.py
### 
run_flow.py
with 
argparse
Keep your custom script for handling 
argparse
as is:
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python
# project_1/run_flow.py
import argparse
from prefect import flow

@flow
def my_flow(my_param):
    print(f"Flow received parameter: {my_param}")

def main():
    parser = argparse.ArgumentParser(description="Run Prefect flow with dynamic parameters.")
    parser.add_argument("--my_param", type=str, required=True, help="Parameter for the flow.")
    args = parser.parse_args()
    
    my_flow(args.my_param)

if __name__ == "__main__":
    main()
### Prefect Deployment Script In 
prefect.yaml
, you will specify the flow directly. Define a simpler script that Prefect can execute to register the flow: project_1/prefect.yaml:
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yaml
name: project_1
version: 1
storage:
  block: "local"
  path: "./flows"
infrastructure:
  type: "process"
pull_steps:
  - prefect.infrastructure.steps.ProcessEnv:
      env:
        PIP_INSTALL: "pip install -r ./requirements.txt"
entrypoint: "flows.my_flow"
### Deployment Process Using CI/CD .github/workflows/deploy-projects.yaml:
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yaml
name: Deploy Projects

on:
  push:
    branches:
      - main

jobs:
  deploy_project_1:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 1 Requirements
        run: |
          cd project_1
          pip install -r requirements.txt
      - name: Deploy Project 1
        run: |
          cd project_1
          prefect deployment build --apply

  deploy_project_2:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 2 Requirements
        run: |
          cd project_2
          pip install -r requirements.txt
      - name: Deploy Project 2
        run: |
          cd project_2
          prefect deployment build --apply
### Running the Flows To run the flows and pass dynamic parameters, execute 
run_flow.py
separately in your execution environment:
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bash
cd main_project_directory/project_1
python run_flow.py --my_param value
### Conclusion Prefect requires the exact flow to be specified at deployment for registration purposes. Use a separate script to handle dynamic parameters (
run_flow.py
), and call this script separately when you run your flows. For more information, you can refer to: - Prefect Deployment Guide - Prefect CLI Commands Let me know if you need further assistance!
a
@Marvin I got a non-zero error code (1) when running deployment in Prefect Cloud. Any possible issues?
m
A non-zero exit code (1) typically indicates an error during the deployment process. Here are some potential issues and troubleshooting steps: ### Common Issues and Solutions 1. Misconfigured `prefect.yaml`: Ensure that your 
prefect.yaml
file is correctly configured with the necessary fields. Example `prefect.yaml`:
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yaml
   name: project_1
   version: 1
   storage:
     block: "local"
     path: "./flows"
   infrastructure:
     type: "process"
   pull_steps:
     - prefect.infrastructure.steps.ProcessEnv:
         env:
           PIP_INSTALL: "pip install -r ./requirements.txt"
   entrypoint: "flows.my_flow"
2. Dependency Issues: Ensure all dependencies specified in the 
requirements.txt
are installed correctly. requirements.txt:
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plaintext
   prefect
   any_other_dependency
3. File Paths and Import Errors: Check that all file paths and imports are correct. Ensure the 
entrypoint
in 
prefect.yaml
points to the correct flow. 4. Invalid Flow Definition: Ensure the flow defined in your Python script matches the expected format. Prefect tries to identify the flow based on the 
entrypoint
. 5. Permission Issues: Ensure that the Prefect agent running the deployment has permission to access all required resources (files, network, etc.). ### Troubleshooting Steps #### 1. Verify 
prefect.yaml
File Make sure that the 
prefect.yaml
file is correctly formatted and contains all required fields. #### 2. Check Error Logs Look at the error logs provided by Prefect Cloud. The logs can give detailed insight into what went wrong. #### 3. Simplify Your Deployment Start with a minimal 
prefect.yaml
and gradually add complexity to isolate the issue. Minimal `prefect.yaml`:
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yaml
name: project_1
version: 1
storage:
  block: "local"
  path: "./flows"
infrastructure:
  type: "process"
entrypoint: "flows.my_flow"
#### 4. Test Locally Before deploying to Prefect Cloud, test the entire setup locally.
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bash
cd main_project_directory/project_1
prefect deployment build --apply
prefect agent start
Run the flow locally to ensure it works:
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bash
python run_flow.py --my_param value
#### 5. Validate Environment Setup Ensure that the environment where your flow runs has all required dependencies. This might include setting up a virtual environment. #### 6. Handle Exit Codes in CI/CD Make sure that your CI/CD pipeline captures and logs the error details if the deployment fails. Example GitHub Action:
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yaml
name: Deploy Projects

on:
  push:
    branches:
      - main

jobs:
  deploy_project_1:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install Prefect
        run: pip install prefect
      - name: Install Project 1 Requirements
        run: |
          cd project_1
          pip install -r requirements.txt
      - name: Deploy Project 1
        run: |
          cd project_1
          prefect deployment build --apply || exit 1
### Conclusion Diagnosing deployment issues can be tricky without the exact error message, but the above steps and best practices should help you troubleshoot common problems. If the issue persists, please check the detailed logs from Prefect Cloud or try simplifying your 
prefect.yaml
. For further insight: - Prefect Deployment Guide Feel free to share more details or specific error messages if you need more targeted assistance!
a
@Marvin I have a class method that is defined as a flow. When I try to put this method as my entrypoint in 
prefect.yaml
, it isn't found
m
Using a class method as the entry point for a Prefect flow can be tricky because Prefect expects a standalone function as the 
entrypoint
. However, you can structure your code to work around this. Here's how you can proceed: ### Example Class with Flow Method First, let's define a class with a flow method.
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python
# project_1/flows/my_module.py
from prefect import flow

class MyClass:
    @flow
    def my_flow(self, my_param):
        print(f"Flow received parameter: {my_param}")
### Workaround: Wrapper Function Since Prefect expects a standalone function as the entry point, you can create a wrapper function that instantiates the class and calls the class method.
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python
# project_1/flows/wrapper.py
from .my_module import MyClass

def run_my_class_flow(my_param):
    instance = MyClass()
    instance.my_flow(my_param)
### 
prefect.yaml
Configuration Set the entry point in 
prefect.yaml
to point to the wrapper function, not the class method directly. project_1/prefect.yaml:
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yaml
name: project_1
version: 1
storage:
  block: "local"
  path: "./flows"
infrastructure:
  type: "process"
pull_steps:
  - prefect.infrastructure.steps.ProcessEnv:
      env:
        PIP_INSTALL: "pip install -r ./requirements.txt"
entrypoint: "flows.wrapper.run_my_class_flow"
### Deployment and Running the Flow 1. Build and Apply Deployment:
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bash
    cd main_project_directory/project_1
    prefect deployment build ./flows/wrapper.py:run_my_class_flow --apply
2. Trigger the Flow: Now you can trigger the flow as you normally would, and it will call the class method via the wrapper function. ### Running with Arguments To run the flow with dynamic arguments, you would handle this in your CI/CD or execution environment. The flow would be triggered with the necessary parameters in your wrapper function. Example:
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bash
python -c "from flows.wrapper import run_my_class_flow; run_my_class_flow(my_param='value')"
### Conclusion Using a wrapper function to call the class method allows Prefect to recognize the function specified in the 
entrypoint
field of your 
prefect.yaml
. This approach maintains the object-oriented design while satisfying Prefect's requirement for an entry point. For more information: - Prefect Deployment Guide - Prefect CLI Commands Let me know if you need more help!
a
@Marvin I'm getting this 
UnicodeEncodeError
when trying to run a simple deployment: File "C:\Program Files\Python311\Lib\encodings\cp1252.py", line 19, in encode return codecs.charmap_encode(input,self.errors,encoding_table)[0] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ UnicodeEncodeError: 'charmap' codec can't encode characters in position 0-2: character maps to <undefined> (edited)
m
I'm getting this 
UnicodeEncodeError
when trying to run a simple deployment: File "C:\Program Files\Python311\Lib\encodings\cp1252.py", line 19, in encode return codecs.charmap_encode(input,self.errors,encoding_table)[0] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ UnicodeEncodeError: 'charmap' codec can't encode characters in position 0-2: character maps to <undefined> The 
UnicodeEncodeError
you're encountering typically occurs when handling non-ASCII characters in environments set to use 
cp1252
encoding (common in Windows). This issue can arise during string processing, especially if there are special characters or non-ASCII characters in your script or configurations. ### Solutions 1. Set Environment Variable: You can set the 
PYTHONIOENCODING
environment variable to 
utf-8
to ensure your Python scripts use UTF-8 encoding. Command Line:
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cmd
   set PYTHONIOENCODING=utf-8
Permanently (Windows): - Open Environment Variables (through System Properties). - Add a new User or System variable: 
PYTHONIOENCODING
with value 
utf-8
. 2. Modify Python Script: Force encoding within your script explicitly. Example:
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python
   import sys
   sys.stdout = open(sys.stdout.fileno(), mode='w', encoding='utf8', buffering=1)
   sys.stderr = open(sys.stderr.fileno(), mode='w', encoding='utf8', buffering=1)
3. Check Source Files and Strings: Ensure no special or non-ASCII characters exist inadvertently in your script or variables. ### Example Implementation Here's an example on how to explicitly set encoding in a Python script. project_1/flows/wrapper.py:
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python
import sys
sys.stdout = open(sys.stdout.fileno(), mode='w', encoding='utf8', buffering=1)
sys.stderr = open(sys.stderr.fileno(), mode='w', encoding='utf8', buffering=1)

from .my_module import MyClass

def run_my_class_flow(my_param):
    instance = MyClass()
    instance.my_flow(my_param)
Running Deployment:
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bash
cd main_project_directory/project_1
prefect deployment build ./flows/wrapper.py:run_my_class_flow --apply
### Changing Windows Console Code Page Another option is to change the console code page to UTF-8 before running your script. This can be temporarily achieved by running:
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cmd
chcp 65001
### Conclusion Ensure that your environment and scripts explicitly handle UTF-8 encoding to avoid issues related to 
UnicodeEncodeError
. Setting environment variables, modifying your script to enforce UTF-8 encoding, and checking your source files for special characters should resolve the issue. For further reference: - Python Unicode HOWTO - Prefect Deployment Guide Let me know if these solutions help or if you need more assistance!
Seems like if I make a class method a flow, the 
self
parameter is required. How do I rewrite code so this isn't required? Seems like if I make a class method a flow, the 
self
parameter is required. How do I rewrite code so this isn't required? If you prefer to avoid requiring the 
self
parameter within a class method defined as a Prefect flow, you can use class or static methods. These methods don't require an instance of the class (
self
), making them easier to work with in some scenarios. ### Using Static Methods A static method does not depend on the instance (
self
) or class (
cls
). It's essentially a regular function that resides within a class. #### Example with Static Method 1. Define a Static Method in Your Class:
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python
   # project_1/flows/my_module.py
   from prefect import flow

   class MyClass:
       @staticmethod
       @flow
       def my_flow(my_param):
           print(f"Flow received parameter: {my_param}")
2. Wrapper Function:
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python
   # project_1/flows/wrapper.py
   from .my_module import MyClass

   def run_my_class_flow(my_param):
       MyClass.my_flow(my_param)
3. Prefect YAML Configuration:
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yaml
   name: project_1
   version: 1
   storage:
     block: "local"
     path: "./flows"
   infrastructure:
     type: "process"
   pull_steps:
     - prefect.infrastructure.steps.ProcessEnv:
         env:
           PIP_INSTALL: "pip install -r ./requirements.txt"
   entrypoint: "flows.wrapper.run_my_class_flow"
### Using Class Methods A class method depends on the class (
cls
), but not on a particular instance (
self
). You can use them when you need to operate on the class itself. #### Example with Class Method 1. Define a Class Method in Your Class:
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python
   # project_1/flows/my_module.py
   from prefect import flow

   class MyClass:
       @classmethod
       @flow
       def my_flow(cls, my_param):
           print(f"Flow received parameter: {my_param}")
2. Wrapper Function:
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python
   # project_1/flows/wrapper.py
   from .my_module import MyClass

   def run_my_class_flow(my_param):
       MyClass.my_flow(my_param)
3. Prefect YAML Configuration:
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yaml
   name: project_1
   version: 1
   storage:
     block: "local"
     path: "./flows"
   infrastructure:
     type: "process"
   pull_steps:
     - prefect.infrastructure.steps.ProcessEnv:
         env:
           PIP_INSTALL: "pip install -r ./requirements.txt"
   entrypoint: "flows.wrapper.run_my_class_flow"
### Summary Using static or class methods avoids needing 
self
, making them useful for scenarios where you don’t require instance-specific data. You still get to keep your methods within a class for better organization while making Prefect deployments work seamlessly. ### Running Deployment With these changes, build and apply the deployment as usual:
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bash
cd main_project_directory/project_1
prefect deployment build ./flows/wrapper.py:run_my_class_flow --apply
For detailed information or more examples, refer to: - Python Class Methods - Python Static Methods - Prefect Deployment Guide Feel free to reach out if you need more assistance!
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