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<@ULVA73B9P> I deployed multiple flows locally usi...
# marvin-in-the-wild
a
@Marvin I deployed multiple flows locally using:
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Deployment.build_from_flow
and I want to deploy it with environment variables from my .env file. Is there a way to do it while creating the work pool? Or the only way to do it is with the 
job_variables
argument? what is the best practice of this case? This is how I create the work pool:
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prefect work-pool create localhost-tests --type process
this is my current deployment script:
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deployment = Deployment.build_from_flow(
    flow=flow,
    name=f"{flow.name}",
    work_pool_name="localhost-tests",
    job_variables={
        "env": dict(env.to_dict())
    },
)
deployment.apply()
m
Got it, you're asking if it's possible to deploy flows with environment variables specified in a 
.env
file directly within the work pool setup, or if you need to use the 
job_variables
argument when creating the deployment. Let's check the relevant resources. First, which version of Prefect are you using? This detail will help me search the correct documentation for you.
a
@Marvin I'm using prefect 2
m
Based on the info I found, it seems that the recommended approach for deploying with environment variables in Prefect 2 is indeed leveraging 
job_variables
or 
infra_overrides
during the deployment process. Here are a few key insights: 1. Using job_variables or infra_overrides: - You can specify environment variables directly in the 
job_variables
argument when using 
Deployment.build_from_flow
. This method allows you to dynamically set variables that your deployment can use, as shown in this guide on overriding job variables. - For example:
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python
     deployment = Deployment.build_from_flow(
         flow=flow,
         name=f"{flow.name}",
         work_pool_name="localhost-tests",
         job_variables={
             "env": {
                 "MY_ENV_VAR": "value",
                 "ANOTHER_ENV_VAR": "another_value"
             }
         }
     )
     deployment.apply()
2. Considerations for environment variables: - The environment variables must be set on the machine where the deployment script is being executed. If using 
os.environ
, ensure these variables are exported in your local environment before running the script. 3. Handling .env files: - You can load environment variables from a 
.env
file using Python libraries such as 
python-dotenv
and then pass them into 
job_variables
. This approach keeps your deployment script clean and separates configuration from code. - Example:
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python
     from dotenv import load_dotenv
     import os

     # Load environment variables from .env file
     load_dotenv()

     deployment = Deployment.build_from_flow(
         flow=flow,
         name=f"{flow.name}",
         work_pool_name="localhost-tests",
         job_variables={
             "env": {
                 "MY_ENV_VAR": os.getenv("MY_ENV_VAR"),
                 "ANOTHER_ENV_VAR": os.getenv("ANOTHER_ENV_VAR")
             }
         }
     )
     deployment.apply()
While it seems there isn't a direct method to set environmental variables on the work pool itself without using 
job_variables
or 
infra_overrides
, using these mechanisms still allows for flexibility in dynamically configuring your deployment environment. For more details, check these resources: - Deeper Dive: Overriding Work Pool Job Variables - Prefect Deployment Guides If you have any specific issues with how environment variables are not being respected, consider looking at relevant GitHub issues for potential bugs and workarounds: GitHub Issues. Hope this helps! Let me know if you need further details!
a
@Marvin is there a preferred way to deploy multiple flows?
m
To deploy multiple flows in Prefect 2, there are a few best practices you can follow to make the process efficient and manageable: 1. Single Deployment Script: - You can use a single deployment script to deploy multiple flows. This keeps everything centralized and easy to manage. - Example:
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python
     from prefect.deployments import Deployment
     from prefect import flow
     
     @flow
     def flow_one():
         print("This is Flow One")

     @flow
     def flow_two():
         print("This is Flow Two")

     # Define deployments
     deployment_one = Deployment.build_from_flow(
         flow=flow_one,
         name="Flow One Deployment",
         work_pool_name="my-work-pool",
     )

     deployment_two = Deployment.build_from_flow(
         flow=flow_two,
         name="Flow Two Deployment",
         work_pool_name="my-work-pool",
     )

     # Apply deployments
     deployment_one.apply()
     deployment_two.apply()
2. Using 
prefect.yaml
File: - You can define multiple deployments in a 
prefect.yaml
file to manage them declaratively. - Example:
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yaml
     deployments:
       - name: "flow-one-deployment"
         flow_name: "flow_one"
         work_pool: "my-work-pool"
       - name: "flow-two-deployment"
         flow_name: "flow_two"
         work_pool: "my-work-pool"
- You can then deploy them using the CLI:
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sh
     prefect deploy --all
3. Prefixing Deployments: - To avoid name conflicts and ensure clarity, you can prefix deployment names with their respective flow names.
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sh
     prefect deploy --name flow_one/deployment-1 --name flow_two/deployment-1
4. Using Remote Sources: - You can also deploy flows from remote sources alongside local flows using the 
from_source
method. - Example:
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python
     from prefect import deploy, flow
     
     @flow(log_prints=True)
     def local_flow():
         print("I'm a local flow!")

     if __name__ == "__main__":
         deploy(
             local_flow.to_deployment(name="local-deployment"),
             flow.from_source(
                 source="<https://github.com/org/repo.git>",
                 entrypoint="flows.py:remote_flow",
             ).to_deployment(
                 name="remote-deployment",
             ),
             work_pool_name="my-work-pool",
         )
These methods help you structure your deployments effectively and keep them organized. For more information, you can refer to the Prefect deployment guide: Deploying Flows. Let me know if you need more details or have any specific questions!
a
@Marvin each task in my flow saves a csv file, how can I make sure the files are saved locally on a specified path? (after deployment)
m
To ensure that each task in your Prefect flow saves a CSV file locally to a specified path, you can follow these steps: 1. Write to Local File System: - Directly use Python's built-in file handling capabilities to write the CSV files to your desired local path. 2. Specify Paths in Tasks: - Specify the paths within each task to ensure the files are saved in the correct directory. Here’s an example of how you could implement this in your flow:
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python
import os
from prefect import flow, task
import pandas as pd

# Task to save a dataframe as a CSV file
@task
def save_csv(data: pd.DataFrame, filename: str, directory: str):
    # Ensure directory exists
    os.makedirs(directory, exist_ok=True)
    
    # Construct the file path
    file_path = os.path.join(directory, filename)
    
    # Save data to CSV
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

# Flow definition
@flow
def my_flow():
    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})
    
    # Save CSV files
    save_csv(data1, "file1.csv", "./data/flow1")
    save_csv(data2, "file2.csv", "./data/flow1")

# Run the flow
if __name__ == "__main__":
    my_flow()
### Key Points 1. Task Function: - A task 
save_csv
is defined to handle the saving process. It ensures that the specified directory exists (
os.makedirs(directory, exist_ok=True)
) and then saves the DataFrame to a CSV file in the specified directory. 2. Flow Function: - In the flow 
my_flow
, sample data is created and 
save_csv
tasks are called with different filenames and directory paths. This ensures that each file is saved to the appropriate location. 3. Running the Flow: - By running the flow (
my_flow
), the tasks will execute, and the CSV files will be saved to your local file system. This method ensures that your files are saved locally in a specified path, and it leverages Python’s built-in capabilities for better control and simplicity. For additional information and best practices, refer to the Prefect documentation on saving data to disk. Let me know if you need more specific help or examples!
a
@Marvin I meant after deployment
m
Ensuring your tasks save CSV files locally to a specified path in a deployed Prefect flow is achievable with a bit of planning. Here’s how you can do it: ### Write to Local File System in Tasks You can use Python’s file handling capabilities to specify paths within each task, ensuring the files are saved in the correct directories. Here’s an example:
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python
import os
from prefect import flow, task
import pandas as pd

# Task to save a DataFrame as a CSV file
@task
def save_csv(data: pd.DataFrame, filename: str, directory: str):
    # Ensure directory exists
    os.makedirs(directory, exist_ok=True)
    
    # Construct the file path
    file_path = os.path.join(directory, filename)
    
    # Save data to CSV
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

# Flow definition
@flow
def my_flow():
    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})
    
    # Save CSV files
    save_csv(data1, "file1.csv", "./data/flow1")
    save_csv(data2, "file2.csv", "./data/flow1")

# Deployment
if __name__ == "__main__":
    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool",
        infra_overrides={
            "env": {"SOME_ENV_VAR": "value"}  # Set required environment variables
        },
    )
    deployment.apply()
### Key Points: 1. Use 
os.makedirs
: - Ensure the target directory exists before saving the file. 2. Specify Path in Task: - Define the directory path when calling the saving task to control where files are saved. 3. Deployment Handling: - Ensure that the environment where the deployment runs has the necessary permissions and paths accessible. ### Deploy the Flow: - Ensure your environment variables and paths are set correctly during deployment. This setup ensures the flow runs as expected when deployed. For further information about deployments, you can refer to the Prefect deployment guides: - Prefect Deployment Guide - Deployment Tutorial If your tasks are integrated to write results to cloud storage or other destinations, refer to the respective Prefect documentation for those integrations. Let me know if you need further assistance or have more specific questions!
a
@Marvin I specify the path .myfolder/mysource_name/output.csv and I expect it to be saved locally under my project path so the full path should be Desktop/myproject/.myfolder/mysource_name/output.csv But instead it saves everything under: /private/var/folders/fc/4ml0tmp94dg_1qrbdyhjvss40000gn/T/tmpp0tjnjhpprefect/.myfolder/mysource_name/output.csv
m
It seems that the confusion arises from the way Prefect deployments handle file paths. By default, when deploying flows, Prefect may use temporary directories for execution. Here are a few steps you can follow to ensure that your files are saved to a specific path: ### Configuring Path in Tasks Make sure that your task explicitly references an absolute path based on the desired root directory. This will ensure that files are not saved in a temporary directory. ### Example Here’s an updated example that ensures the file is saved in your desired directory structure:
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python
import os
from prefect import flow, task
import pandas as pd

# Task to save a dataframe as a CSV file
@task
def save_csv(data: pd.DataFrame, relative_path: str):
    # Ensure that the directory exists
    full_path = os.path.join(os.path.expanduser("~"), "Desktop/myproject", relative_path)
    directory = os.path.dirname(full_path)
    os.makedirs(directory, exist_ok=True)
    
    # Save data to CSV
    data.to_csv(full_path, index=False)
    print(f"File saved to {full_path}")

# Flow definition
@flow
def my_flow():
    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})
    
    # Save CSV files
    save_csv(data1, ".myfolder/mysource_name/output1.csv")
    save_csv(data2, ".myfolder/mysource_name/output2.csv")

# Deployment definition (if needed)
if __name__ == "__main__":
    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Explanation 1. Explicit Absolute Paths: The 
save_csv
task builds the absolute path by combining the base directory (
Desktop/myproject
) with the relative path (
.myfolder/mysource_name/output1.csv
). 2. Dynamic Path Creation: The task ensures that any necessary directories are created with 
os.makedirs(directory, exist_ok=True)
. 3. Consistent Saved Path: By enforcing the use of a consistent base directory, you can avoid having files saved in temporary deployment directories. ### Deployment Configurations When you deploy your flow, ensure that the deployment runs in an environment that has access to the required directories and paths. Verify that the base directory (
Desktop/myproject
) exists and has the correct permissions. ### Additional Information For more detailed customization, Prefect allows the use of blocks and storage options: - Prefect Blocks require appropriate filesystem libraries and can help manage configuration and storage outside the local filesystem. - Big Data Handling describes methods to save results directly to disk or cloud providers to reduce memory usage. Let me know if this helps or if you need further assistance!
a
@Marvin I made sure to add the absolute path when saving the files but it still saves everything under the tmp folders
m
It seems you’ve already ensured to use absolute paths, but the files still get saved in the temporary directories. This indicates that there might be environment-specific configurations affecting where files are saved during execution. Prefect’s deployment machinery can often isolate tasks in temporary directories for execution. Here are a few steps and considerations to ensure files are saved in the desired local path: ### Ensuring Correct Working Directory To ensure the task runs in the correct working directory, explicitly set the working directory within your tasks or at the beginning of your flow. This can help override any default behaviors that isolate directory contexts. ### Using 
os.chdir
in Tasks You can set the current working directory to the desired directory at the beginning of your flow or within individual tasks. ### Example with 
os.chdir
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python
import os
from prefect import flow, task
import pandas as pd

# Task to set working directory
@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

# Task to save a dataframe as a CSV file
@task
def save_csv(data: pd.DataFrame, filename: str):
    data.to_csv(filename, index=False)
    print(f"File saved to {os.path.abspath(filename)}")

# Flow definition
@flow
def my_flow():
    # Change working directory
    set_working_directory("/Users/username/Desktop/myproject")

    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})

    # Save CSV files
    save_csv(data1, ".myfolder/mysource_name/output1.csv")
    save_csv(data2, ".myfolder/mysource_name/output2.csv")

# Deployment definition (if needed)
if __name__ == "__main__":
    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Explanation: 1. set_working_directory Task: This task ensures that the working directory is explicitly set to 
/Users/username/Desktop/myproject
at the start of the flow. Change this directory path to match your actual desired path. 2. save_csv Task: This task saves the CSV file in the specified directory. ### Other Considerations: 1. Environment Variables and Configuration: - Ensure your environment variables or virtual environment configurations are not redirecting file paths. - You can override job variables such as 
env
to control the execution environment more precisely as mentioned here. 2. Check Prefect’s Working Directory Configuration: - Understand how your Prefect deployment environment handles working directories. Check if additional configurations are redirecting file paths. 3. Use The Prefect UI to Debug: - Utilize the Prefect UI to monitor and debug the paths being used during task executions. If you still face issues, consider creating an issue on the Prefect GitHub or consulting Prefect’s support, detailing your deployment configuration and environment settings. If you need further assistance, feel free to ask!
a
@Marvin I think this is the key: Check Prefect’s Working Directory Configuration: - Understand how your Prefect deployment environment handles working directories. Check if additional configurations are redirecting file paths. is there anything I need to configure so the files wont be saved under temporary folder?
m
To ensure your Prefect deployments don't save files in temporary folders, you need to explicitly configure the working directory Prefect uses during the deployment process. Here are key points and examples gathered from the documentation and issues related to setting and managing working directories in Prefect deployments: ### Set Working Directory in Deployment 1. Using 
os.chdir
in Your Flow/Tasks: Ensure the working directory is set explicitly in your task or at the beginning of your flow. This method can help but might still be overridden by deployment environments. 2. Using Deployment Configuration: Prefect allows specifying the working directory directly in your deployment configuration using the 
prefect.yaml
file or within your deployment script. ### Example Prefect Deployment Here’s how to set the working directory in your 
prefect.yaml
configuration file to avoid temporary directories: 1. prefect.yaml Configuration:
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yaml
   name: my_project
   prefect-version: 2.0.0

   build: null

   push: null

   pull:
      - prefect.deployments.steps.set_working_directory:
          directory: "/Users/username/Desktop/myproject"

   deployments:
     - name: my_flow_deployment
       entrypoint: flows/my_flow.py:my_flow
       work_pool:
         name: my-work-pool
         job_variables:
           image: prefecthq/prefect:2.0.0
           image_pull_policy: Always
           service_account_name: prefect-service
           finished_job_ttl: 300
2. Using Deployment Script: - Ensure your deployment script references the correct working directory.
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python
   from prefect.deployments import Deployment
   from prefect.deployments.steps import set_working_directory
   from flows.my_flow import my_flow

   directory = "/Users/username/Desktop/myproject"

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="My Flow Deployment",
       work_pool_name="my-work-pool",
       steps=[
           set_working_directory(directory=directory)
       ]
   )
   deployment.apply()
3. Task with Specific Directory: If necessary, every task can also specify the directory:
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python
   import os
   from prefect import flow, task
   import pandas as pd

   @task
   def save_csv(data: pd.DataFrame, relative_path: str):
       directory = "/Users/username/Desktop/myproject"
       os.makedirs(os.path.join(directory, os.path.dirname(relative_path)), exist_ok=True)
       file_path = os.path.join(directory, relative_path)
       data.to_csv(file_path, index=False)
       print(f"File saved to {file_path}")

   @flow
   def my_flow():
       data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
       save_csv(data1, ".myfolder/mysource_name/output1.csv")

   if __name__ == "__main__":
       my_flow()
### Debugging Steps: - Check Deployment Logs: Ensure your deployments show the correct working directory being set by reviewing logs in the Prefect UI or deployment logs. - Explicit Directories: Always specify absolute paths rather than relying on the current working directory. By configuring the working directory through deployment configurations and explicitly in tasks when necessary, you can ensure that files are saved to your desired directories and not the temporary directories used by the deployment environment. If you still encounter issues, it could be an environment-specific configuration that needs further debugging based on your deployment setup.
a
@Marvin got this error when I added steps to my deployment: pydantic.error_wrappers.ValidationError: 1 validation error for Deployment steps extra fields not permitted (type=value_error.extra)
m
The issue you're encountering is due to using an incorrect way to specify steps in your Prefect 2 deployment configuration. In Prefect 2, deployments don't support the 
steps
field directly; steps should be specified within the 
prefect.yaml
configuration file or as part of your deployment script in a structured way. Here’s a corrected approach to explicitly set the working directory using the 
prefect.yaml
configuration and the deployment script without encountering validation errors: ### Correct Approach Using 
prefect.yaml
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yaml
name: my_project
prefect-version: 2.0.0

pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/Users/username/Desktop/myproject"

deployments:
  - name: my_flow_deployment
    version: latest
    entrypoint: "flows/my_flow.py:my_flow"
    work_pool:
      name: my-work-pool
    description: "My flow deployment"
### Correct Approach Using Deployment Script For your deployment script, ensure configurations are properly applied. Here’s the approach without the 
steps
field directly in the deployment: 1. Script to Set Current Working Directory - Set the working directory at the start of your script or within tasks to ensure the correct working setup. 2. Flow Script
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python
   import os
   from prefect import task, flow
   import pandas as pd

   # Ensure you set the working directory at the start of your flow
   @task
   def set_working_directory(path: str):
       os.makedirs(path, exist_ok=True)
       os.chdir(path)
       print(f"Changed working directory to {os.getcwd()}")

   @task
   def save_csv(data: pd.DataFrame, relative_path: str):
       file_path = os.path.join(relative_path)
       data.to_csv(file_path, index=False)
       print(f"File saved to {file_path}")

   @flow
   def my_flow():
       # Change working directory
       set_working_directory("/Users/username/Desktop/myproject")

       # Sample data
       data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
       save_csv(data1, ".myfolder/mysource_name/output1.csv")

   if __name__ == "__main__":
       from prefect.deployments import Deployment

       deployment = Deployment.build_from_flow(
           flow=my_flow,
           name="My Flow Deployment",
           work_pool_name="my-work-pool"
       )
       deployment.apply()
### Explanation: 1. Using 
prefect.yaml
: Including 
prefect.deployments.steps.set_working_directory
in the 
pull
section of the Prefect configuration YAML. 2. Flow Script: Implementing a 
set_working_directory
task to change the directory at the flow level. Tasks like 
save_csv
then use this directory. ### Tips: - Workflow Verification: After setting the working directory, verify by adding debug statements or print statements to ensure subsequent tasks operate within the correct directory. - Environment Setup: Ensure that the directory you are pointing to exists and is accessible by the running Prefect agent and deployment nodes. If you still encounter issues, share more details about your current deployment configuration and script setup for further assistance.
a
@Marvin I added this task to my flow re-deployed it:
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@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")
it prints this path: Current Working Directory: /private/var/folders/fc/4ml0tmp94dg_1qrbdyhjvss40000gn/T/tmp0i14ym5nprefect How can I override this path and my own workdir?
m
To ensure that files are saved in your specified working directory and not in a temporary directory, you should explicitly set the working directory at the start of your flow or tasks. Here’s a more structured approach based on what you've shared: ### Ensuring Working Directory in Your Tasks 1. Ensure the Working Directory is Set Correctly: Use 
os.chdir
to change the current working directory. ### Example Implementation Ensure the working directory is set correctly, and all paths are relative to this directory.
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python
import os
from prefect import flow, task
import pandas as pd

# Task to set working directory
@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

# Task to save a dataframe as a CSV file
@task
def save_csv(data: pd.DataFrame, relative_path: str):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)  # Ensure the directory exists
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

# Task to check current working directory (for debugging)
@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

# Flow definition
@flow
def my_flow():
    # Set working directory at the beginning of your flow
    set_working_directory("/Users/username/Desktop/myproject")

    # Check working directory
    check_working_directory()

    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})

    # Save CSV files
    save_csv(data1, ".myfolder/mysource_name/output1.csv")
    save_csv(data2, ".myfolder/mysource_name/output2.csv")

# Deployment of the flow
if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Explanation: 1. 
set_working_directory
Task: This task sets the working directory at the beginning of the flow to ensure subsequent tasks run within the desired directory. 2. 
check_working_directory
Task: Included for debugging purposes to confirm the working directory is correctly set. 3. Using Paths Correctly: Paths are constructed relative to the set working directory to ensure consistency. ### Deploy and Run the Flow Deploy and run this deployment to ensure everything works as expected. ### Alternative Approach: Environment Variables You could also use environment variables to set the directory globally, but setting the directory explicitly inside the flow/tasts ensures greater control and consistency. ### Debugging Tips: - Print Statements: Keep the 
check_working_directory
task to verify the directory multiple times within the flow. - Deployment Logs: Check the Prefect UI and logs to understand if and when the directory changes unexpectedly. By ensuring that 
os.chdir
is called at the start and using absolute paths, you should be able to override the temporary directory and use your specified working directory instead.
a
@Marvin I have to add it to the flow? Can't I define it in the deployment process?
m
Yes, you can set the working directory during the deployment process using the Prefect deployment configuration. Although Prefect deployments don't directly allow setting a working directory through a 
steps
field in the deployment script (since it's not supported directly within the deployment creation), you can achieve this through the 
prefect.yaml
file. Here’s how to explicitly set the working directory as part of the deployment process: ### Using 
prefect.yaml
Configuration You can use steps such as 
set_working_directory
to set your desired path in the deployment process. Example 
prefect.yaml
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yaml
name: my_project
prefect-version: 2.0.0

pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/Users/username/Desktop/myproject"

deployments:
  - name: my_flow_deployment
    entrypoint: "flows/my_flow.py:my_flow"
    work_pool:
      name: my-work-pool
    description: "My flow deployment"
In this 
prefect.yaml
configuration: - 
pull
section: Use 
prefect.deployments.steps.set_working_directory
to set the working directory. ### Deployment Script Using Modified 
prefect.yaml
Similarly, if you are using a deployment script, you can ensure it points to this 
prefect.yaml
configuration or define it programmatically: Flow Script with Deployment
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python
import os
from prefect import flow, task
import pandas as pd

# Task to set working directory (as a fallback if needed)
@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

@task
def save_csv(data: pd.DataFrame, relative_path: str):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)  # Ensure the directory exists
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

@flow
def my_flow():
    # Optionally, ensure the working directory if needed
    set_working_directory("/Users/username/Desktop/myproject")
    
    check_working_directory()

    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})

    # Save CSV files
    save_csv(data1, ".myfolder/mysource_name/output1.csv")
    save_csv(data2, ".myfolder/mysource_name/output2.csv")

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Summary: 1. 
prefect.yaml
Configuration: Use the 
pull
section to set the working directory using 
set_working_directory
.
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yaml
    pull:
      - prefect.deployments.steps.set_working_directory:
          directory: "/Users/username/Desktop/myproject"
2. Fallback in Flow: Optionally, set the working directory in the flow/tasks if needed for redundancy. ### Control Through Deployment By configuring these settings in the 
prefect.yaml
or in the deployment script, you ensure that the files are saved in your specified directory rather than any temporary directory used by Prefect during deployment. Ensure the deployment is created with:
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sh
prefect deployment build flows/my_flow.py:my_flow -n my_flow_deployment --work-pool my-work-pool --prefect-file path/to/your/prefect.yaml
This approach helps keep the setup clean and consistent without the need to repeatedly redefine it in the flow itself. If further issues arise, examining the deployment logs or settings in Prefect's UI can provide additional insights into directory management.
a
@Marvin what is the best way to cache the file results?
m
### Caching Task Results in Prefect 2 To cache file results and efficiently use cached values in Prefect, you can leverage task caching mechanisms. Prefect 2 offers robust support for caching task outputs based on a variety of conditions. Here’s how you can set up caching for tasks and specifically handle file results: ### Using 
task_input_hash
for Caching Tasks You can use Prefect’s built-in 
task_input_hash
to cache tasks based on their inputs. Here’s an example:
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python
import os
import pandas as pd
from datetime import timedelta
from prefect import flow, task
from prefect.tasks import task_input_hash

# Task to set the working directory
@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

# Task to save a dataframe as a CSV file, with caching
@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)  # Ensure the directory exists
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

# Task to check the current working directory
@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

# Flow definition
@flow
def my_flow():
    # Set the working directory
    set_working_directory("/Users/username/Desktop/myproject")
    
    # Check working directory
    check_working_directory()

    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})

    # Save CSV files with caching
    save_csv(data1, ".myfolder/mysource_name/output1.csv")
    save_csv(data2, ".myfolder/mysource_name/output2.csv")

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Explanation: 1. Caching with 
task_input_hash
: - The 
save_csv
task uses 
task_input_hash
to determine the cache key based on its inputs. The cache expiration is set to one hour with 
cache_expiration=timedelta(hours=1)
. 2. Result Storage: - You can specify result storage (local, cloud storage like S3, etc.) if needed to control where the cached results are stored, but the basic caching mechanism using 
task_input_hash
should suffice for many use cases. ### Adjusting Cache Storage and Policy: You can configure cache policies for more control over storage location and isolation level:
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python
from prefect import task
from prefect.cache_policies import INPUTS, TASK_SOURCE
from prefect.transactions import IsolationLevel

@task(
    cache_policy=(INPUTS + TASK_SOURCE).configure(
        storage="~/.prefect/cache",
        locks="~/.prefect/locks",
        isolation_level=IsolationLevel.SERIALIZABLE,
    ),
    cache_key_fn=task_input_hash, 
    cache_expiration=timedelta(hours=1)
)
def save_csv(data: pd.DataFrame, relative_path: str):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)  # Ensure the directory exists
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")
### Key Points: 1. 
cache_key_fn
and 
cache_expiration
: - Use these parameters to control caching behavior based on task inputs and set expiration times. 2. Custom Cache Policies: - By configuring cache policies, you can specify storage locations, isolation levels, and locking mechanisms to have more granular control over caching. 3. Debug and Verify: - Use tools like 
check_working_directory
task or Prefect’s UI to verify that caching behaves as expected during flow execution.
This setup will ensure efficient caching of your task results, preventing redundant computations and improving the efficiency of your flow runs. For more information, refer to Prefect's Task Caching Documentation. Let me know if you need more assistance or details on specific configurations!
a
@Marvin what if I want to run the flow but I want to remove the cache of one of the tasks so I will get new results?
m
To invalidate the cache of a specific task in Prefect so that it generates new results, you need to manually clear the cache associated with that task. Prefect does not provide a direct built-in command for clearing specific task caches, so you need to manage this through custom logic. ### Options for Cache Invalidation 1. Manual Cache Invalidation by Changing Inputs: - One simple approach is to change the inputs to the task, which will generate a new unique cache key and force a re-run of the task. 2. Using a Dynamic Cache Key: - Use a dynamic component in your 
cache_key_fn
to invalidate the cache manually when needed. 3. Deleting Cached Data (if using a specific storage backend such as S3): - Manually delete cached files from the storage backend. ### Example of Manual Cache Invalidation by Changing Inputs: ### Approach 1: Add a Non-Functional Parameter Add a parameter to the task that is only used to control the cache key:
Copy code
python
import os
import pandas as pd
from datetime import timedelta
from prefect import flow, task
from prefect.tasks import task_input_hash

# Task to set the working directory
@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

# Task to save a dataframe as a CSV file, with caching and a non-functional cache control parameter
@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, cache_buster: int = 0):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)  # Ensure the directory exists
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

# Task to check the current working directory
@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

# Flow definition
@flow
def my_flow(cache_buster: int = 0):
    # Set the working directory
    set_working_directory("/Users/username/Desktop/myproject")
    
    # Check working directory
    check_working_directory()

    # Sample data
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    data2 = pd.DataFrame({'x': [7, 8, 9], 'y': [10, 11, 12]})

    # Save CSV files with caching and cache_buster
    save_csv(data1, ".myfolder/mysource_name/output1.csv", cache_buster)
    save_csv(data2, ".myfolder/mysource_name/output2.csv", cache_buster)

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Explanation: 1. 
cache_buster
Parameter: A non-functional parameter 
cache_buster
is added to the 
save_csv
task and the 
my_flow
flow. By changing the value of 
cache_buster
, you can force the cache key to change, which will invalidate the cache and re-run the task. 2. Updating 
cache_buster
: When you want to invalidate the cache and get new results, you can run the flow with a different value for 
cache_buster
. ### Running the Flow with Cache Invalidation: To invalidate the cache, simply change the 
cache_buster
value when running the flow:
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sh
# First run
prefect deployment run "My Flow Deployment" -p cache_buster=0

# Second run, which will invalidate the cache and re-run the tasks
prefect deployment run "My Flow Deployment" -p cache_buster=1
This way, changing the 
cache_buster
value will ensure that the cache is invalidated, and the tasks will produce new results. If you prefer a more manual approach, you can also directly access and delete specific cached files based on your cache storage backend. This, however, requires custom handling depending on the storage method used (local, S3, etc.). Let me know if you need more details or further assistance with this approach!
a
@Marvin this is my current custom cache key function:
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def custom_cache_key_fn(task, parameters):
    process_name = parameters.get('process_name')
    task_name = task.task.name if not process_name else f"{task.task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks')
    if refresh_cache_list:
        task.task.refresh_cache = refresh_cache_list == "all" or task_name in refresh_cache_list.split(',')

    return f"{task_name}-{parameters.get('run_date')}"
is the refresh cache good enough?
m
Your custom cache key function is a good approach, and the 
refresh_cache
logic looks like it can effectively handle cache invalidation based on specific conditions. However, the way you're handling the 
refresh_cache
could be expanded to more explicitly invalidate the cache, ensuring it won't be used when 
refresh_cache
is true. ### Enhancing Cache Invalidation Here's an enhanced version of your cache key function that incorporates the cache invalidation logic more explicitly: 1. Include cache invalidation directly in the key computation. 2. Use an additional flag or a dynamic component in the cache key to forcefully invalidate cache when needed. ### Example
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python
from datetime import timedelta
from prefect import task

def custom_cache_key_fn(task, parameters):
    process_name = parameters.get('process_name')
    task_name = task.task.name if not process_name else f"{task.task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks')
    
    # Check if cache needs to be refreshed
    if refresh_cache_list == "all" or (refresh_cache_list and task_name in refresh_cache_list.split(',')):
        # Append a unique value to invalidate the cache, for example, a timestamp or hash
        return f"{task_name}-{parameters.get('run_date')}-{task_name}-refresh-{task.task_run_id}"
    
    return f"{task_name}-{parameters.get('run_date')}"

# Example task using the custom cache key function
@task(cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")
### Explanation: 1. Unique Invalidation Suffix: - When 
cache_refresh_tasks
indicates that the cache should be refreshed (either "all" or listing specific tasks), the function appends a unique identifier such as 
task.task_run_id
to the cache key. This ensures that even with the same inputs, the cache key will be different, effectively invalidating the cache for that run. 2. Dynamic Cache Key: - Using 
parameters.get('run_date')
and the task's name ensures the cache key is consistent for specific parameters unless explicitly invalidated. 3. Explicit Cache Control: - Introducing an explicit mechanism to invalidate the cache makes sure that any cache checks are bypassed precisely when needed. ### Flow and Deployment Example Here's how this can be incorporated into a flow and deployment: ```python import os import pandas as pd from prefect import flow, task @task def set_working_directory(path: str): os.makedirs(path, exist_ok=True) os.chdir(path) print(f"Changed working directory to {os.getcwd()}") @task(cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1)) def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None): file_path = os.path.abspath(relative_path) directory = os.path.dirname(file_path) os.makedirs(directory, exist_ok=True) data.to_csv(file_path, index=False) print(f"File saved to {file_path}") @task def check_working_directory(): print(f"Current Working Directory: {os.getcwd()}") @flow def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None): set_working_directory("/Users/username/Desktop/myproject") check_working_directory() data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks) if name == "__main__": from prefect.deployments import Deployment deployment = Deployment.build_from_flow( flow=my_flow, name="My Flow Deployment", work_pool_name="my-work-pool" )
deployment.apply()
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### Running with Cache Invalidation

To run the flow with the cache invalidation in place, specify the `cache_refresh_tasks` parameter:

```sh
# Run without invalidation (normal cache use)
prefect deployment run "My Flow Deployment" -p process_name="process1" -p run_date="2023-10-01"

# Invalidate cache for specific tasks
prefect deployment run "My Flow Deployment" -p process_name="process1" -p run_date="2023-10-01" -p cache_refresh_tasks="save_csv_process1"
### Summary - Enhanced Cache Key Function: The custom cache key function now includes a mechanism to forcefully invalidate the cache by appending a unique identifier. - Explicit Control: The inclusion of the 
cache_refresh_tasks
parameter gives you fine-grained control over when to invalidate the cache. This setup should ensure that you can effectively manage task caching and force cache invalidation when needed. Let me know if this meets your requirements or if further refinements are necessary!
a
@Marvin what happens to the old cache when I invalidate the cache? and won't the fact that I return this: return f"{task_name}-{parameters.get('run_date')}" can cause a problem?
m
When you invalidate the cache by changing the cache key, the new cache key generates new cached results, but the old cached results still remain. Prefect doesn't automatically clean up old cached values, which can lead to an accumulation of unused cache files or data. ### Addressing Old Cache Accumulation 1. Manual Cache Management: - Regularly clean up old cache data manually from your storage location. 2. Automated Cache Pruning: - Implement a process to automatically prune old cache entries based on access or modification times. ### Ensuring Unique Cache Keys Your concern about returning 
f"{task_name}-{parameters.get('run_date')}"
potentially causing problems is valid. It's crucial to ensure that the cache key generated is unique enough to avoid collisions and unintended cache hits. ### Improving Cache Key Function Here’s an enhanced approach for cache keys: 1. Include Relevant Context: Ensure all necessary context is included in the cache key to avoid collisions. 2. Cache Expiration and Cleanup: Implement periodic checks or a mechanism to delete old cache files. ### Example Let’s refine the custom cache key function and add some guidance on cache cleanup. #### Custom Cache Key Function
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python
from datetime import timedelta
from prefect import task, Task

def custom_cache_key_fn(task: Task, parameters: dict):
    process_name = parameters.get('process_name')
    task_name = task.name if not process_name else f"{task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks')

    # Append a refresh flag to force cache invalidation
    refresh_flag = "refresh" if (refresh_cache_list == "all" or (refresh_cache_list and task_name in refresh_cache_list.split(','))) else "no_refresh"

    # Ensure a unique and informative cache key
    return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}"

# Example task using the custom cache key function
@task(cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")
### Automated Cache Cleanup Depending on your storage setup (local filesystem, S3, etc.), you may need different strategies to clean up old cache files. #### Local Filesystem Cleanup Example If you're caching locally, you can implement a cleanup task:
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python
import os
import time
from prefect import task, flow

@task
def cleanup_old_cache(cache_dir: str, max_age_seconds: int):
    now = time.time()
    for root, dirs, files in os.walk(cache_dir):
        for file in files:
            file_path = os.path.join(root, file)
            # Delete files older than max_age_seconds
            if os.stat(file_path).st_mtime < now - max_age_seconds:
                os.remove(file_path)
                print(f"Deleted old cache file: {file_path}")

@flow
def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None):
    cache_dir = "/path/to/cache/dir"  # Set your cache directory path
    cleanup_old_cache(cache_dir, max_age_seconds=3600*24*30)  # For example, cleanup cache older than 30 days

    set_working_directory("/Users/username/Desktop/myproject")
    check_working_directory()
    
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks)

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Summary
- Cache Key Enhancement: Ensure the cache key includes all relevant context and a refresh flag to avoid unintended cache hits. - Manual and Automated Cleanup: Regularly clean up old cache entries either manually or using automated tasks, especially when caching on a local filesystem or a cloud storage solution like S3. By implementing these strategies, you can effectively manage your task caching in Prefect, ensuring both efficient cache use and regular cleanup of old, unused cache entries.
a
@Marvin cant I simply delete the old cache when I want?
m
Yes, you can manually delete the old cache when you want. Prefect doesn’t provide direct functions to manage the cache storage entries like deletion natively, but you can implement this using Python’s file handling capabilities. ### Manual Cache Deletion To manually delete cache files from your specified storage directory or location, you can: 1. List the cache keys or stored files. 2. Delete the specific cache entries you no longer need. ### Example: Manual Cache Deletion Let's assume you are storing cache in a local directory. Here’s an example of how you can delete specific cache files manually: #### Function to Delete Specific Cache Files
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python
import os
import glob

def delete_cache_files(cache_dir: str, task_name: str):
    # Pattern matching for cached files to delete
    cache_pattern = os.path.join(cache_dir, f"{task_name}*")
    cache_files = glob.glob(cache_pattern)

    if not cache_files:
        print(f"No cache files found for task: {task_name}")
        return

    for cache_file in cache_files:
        try:
            os.remove(cache_file)
            print(f"Deleted cache file: {cache_file}")
        except Exception as e:
            print(f"Failed to delete cache file: {cache_file}. Reason: {e}")

# Example usage
delete_cache_files("/path/to/cache/dir", "save_csv_process1")
#### Integrating Cache Deletion in Flow You can integrate this into your Prefect flow as a task, allowing you to delete the cache before running the flow:
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python
import os
import glob
from prefect import flow, task
import pandas as pd

@task
def delete_cache_files(cache_dir: str, task_name: str):
    # Pattern matching for cached files to delete
    cache_pattern = os.path.join(cache_dir, f"{task_name}*")
    cache_files = glob.glob(cache_pattern)

    if not cache_files:
        print(f"No cache files found for task: {task_name}")
        return

    for cache_file in cache_files:
        try:
            os.remove(cache_file)
            print(f"Deleted cache file: {cache_file}")
        except Exception as e:
            print(f"Failed to delete cache file: {cache_file}. Reason: {e}")

@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

@task(cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

@flow
def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None):
    cache_dir = "/Users/username/.prefect/cache"  # Customize your cache directory as needed

    # Optionally delete cache files for the specific task
    if cache_refresh_tasks:
        delete_cache_files(cache_dir, f"save_csv_{process_name}")

    set_working_directory("/Users/username/Desktop/myproject")
    check_working_directory()
    
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks)

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
#### Explanation: 1. 
delete_cache_files
Task: Deletes cache files matching the task name pattern from the specified cache directory. 2. Flow Integration: The flow optionally deletes the cache before proceeding with the task executions if 
cache_refresh_tasks
indicates the cache should be refreshed. ### Triggering Cache Deletion:
You can run the flow and specify the cache refresh logic:
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sh
# Run and delete cache for save_csv_process1
prefect deployment run "My Flow Deployment" -p process_name="process1" -p run_date="2023-10-01" -p cache_refresh_tasks="save_csv_process1"
This setup enables you to manage your cache explicitly, ensuring that old cache files are deleted precisely when needed. Let me know if you need more details or further assistance!
a
@Marvin refreshing cache is like deleting the cache?
m
Refreshing the cache in the context of your task would involve invalidating the cache, so the task is re-executed, and new results are generated. This process does not delete the old cache files; it simply bypasses them by generating a new cache key, ensuring that the task’s new execution produces new results. To directly address your concern: 1. Refreshing Cache: - Involves invalidating the old cache key so that the task re-executes and caches new results. - Does not delete the old cache entries but creates a new cache entry. 2. Deleting Cache: - Physically removes the cached data from the storage location. - Ensures that no stale data occupies space or is accidentally used. ### Example: Refresh vs Delete Cache 1. Cache Invalidation (Refreshing): - You can control cache invalidation by changing a parameter or using a unique cache key as shown in the earlier example. 2. Deleting Cache Files: - Physically removing cache files from the storage backend to free up space and ensure cache is not used. ### Implementation of Both Approaches Let’s merge both approaches to give you a robust way to manage caches effectively. #### Cache Key Function (Cache Refreshing) Here's a refined cache key function to handle refreshing:
Copy code
python
from datetime import timedelta
from prefect import task, Task

def custom_cache_key_fn(task: Task, parameters: dict):
    process_name = parameters.get('process_name')
    task_name = task.name if not process_name else f"{task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks', '')

    # Append a refresh flag to force cache invalidation
    refresh_flag = "refresh" if (refresh_cache_list == "all" or (refresh_cache_list and task_name in refresh_cache_list.split(','))) else "no_refresh"

    # Ensure a unique and informative cache key
    return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}"

# Example task using the custom cache key function
@task(cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")
#### Cache Deletion Task ```python import os import glob from prefect import task, flow import pandas as pd @task def delete_cache_files(cache_dir: str, task_name: str): # Pattern matching for cached files to delete cache_pattern = os.path.join(cache_dir, f"{task_name}*") cache_files = glob.glob(cache_pattern) if not cache_files: print(f"No cache files found for task: {task_name}") return for cache_file in cache_files: try: os.remove(cache_file) print(f"Deleted cache file: {cache_file}") except Exception as e: print(f"Failed to delete cache file: {cache_file}. Reason: {e}") @task def set_working_directory(path: str): os.makedirs(path, exist_ok=True) os.chdir(path) print(f"Changed working directory to {os.getcwd()}") @task def check_working_directory(): print(f"Current Working Directory: {os.getcwd()}") @flow def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None): cache_dir = "/Users/username/.prefect/cache" # Customize your cache directory as needed # Optionally delete cache files for the specific task if cache_refresh_tasks: delete_cache_files(cache_dir, f"save_csv_{process_name}") set_working_directory("/Users/username/Desktop/myproject") check_working_directory() data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks) if name == "__main__": from prefect.deployments import Deployment
deployment = Deployment.build_from_flow( flow=my_flow, name="My Flow Deployment", work_pool_name="my-work-pool" ) deployment.apply()
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### Explanation:

1. *Cache Key Function with Refresh Flag*:
   - Ensures the generation of new cache keys when `cache_refresh_tasks` indicates a refresh.

2. *Cache Deletion Task*:
   - Explicitly deletes all cache files matching the task name pattern before running the flow.

### Running the Flow

To run the flow and delete the cache files or refresh them:

- *Deleting Cache Files*:
  ```sh
  prefect deployment run "My Flow Deployment" -p process_name="process1" -p run_date="2023-10-01" -p cache_refresh_tasks="save_csv_process1"
This command deletes the cache before running the flow. - Refreshing Cache (without Deletion): Use the 
cache_refresh_tasks
parameter to ensure new results are saved, which also effectively invalidates the cache:
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sh
  prefect deployment run "My Flow Deployment" -p process_name="process1" -p run_date="2023-10-01" -p cache_refresh_tasks="all"
By incorporating both cache invalidation and deletion, you have robust control over your caching strategies, ensuring efficient use of resources and accurate task execution. Let me know if you need further details or assistance!
a
@Marvin can I control and set the location of where prefect is saving the cache?
m
Yes, you can control and set the location where Prefect saves the task cache by specifying the result storage location. This can be done through configuration in your Prefect settings and using result storage blocks. Prefect allows you to store task results in various backends like local files, cloud object storage (e.g., S3, GCS), and more. ### Configuring Results Storage 1. Local File System: - You can use Prefect's 
LocalFileSystem
block to store results in a specific directory on your local file system. 2. Cloud Storage: - Prefect supports cloud storage providers like AWS S3, Google Cloud Storage, and Azure Blob Storage for result storage. ### Example Using Local File System Let’s configure a task to store its results in a specific local directory. #### Step 1: Define Result Storage Block First, define a result storage block that points to your desired directory.
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python
from prefect.filesystems import LocalFileSystem
from prefect.blocks.system import Default

# Define local file system block
local_file_block = LocalFileSystem(basepath="/Users/username/.prefect/cache")
local_file_block.save("local-cache")

# Alternatively, use Default block to specify the result location in the prefect configuration
default_config_block = Default.default_config_update("local_cache", {"result_store.basepath": "/Users/username/.prefect/cache"})
default_config_block.save("local-cache-config")
#### Step 2: Use Result Storage in Task Update the task to use this result storage block for caching.
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python
import os
from datetime import timedelta
import pandas as pd
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
from prefect.results import PersistedResult

# Load the local file system block
local_file_system = LocalFileSystem.load("local-cache")

def custom_cache_key_fn(task, parameters):
    process_name = parameters.get('process_name')
    task_name = task.name if not process_name else f"{task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks', '')

    refresh_flag = "refresh" if (refresh_cache_list == "all" or task_name in refresh_cache_list.split(',')) else "no_refresh"
    return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}"

@task(result=PersistedResult(storage=local_file_system), cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

@flow
def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None):
    set_working_directory("/Users/username/Desktop/myproject")
    check_working_directory()

    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks)
#### Step 3: Running the Deployment When you deploy and run this flow, the task results will be stored in the specified local directory. ### Summary 1. Result Storage Block: Define and use a result storage block to control where task results are saved. 2. Local or Cloud: You can use local file systems or cloud storage depending on your requirements. 3. Configuration: Ensure that tasks are configured to use the specified result storage for caching. This setup allows you to control and set the location of where Prefect saves the cache, making it easier to manage and access cached results. Let me know if you need further assistance or more examples!
a
@Marvin but where can I define it to save the cache?
m
To define where Prefect saves the cache, you need to configure the result storage for your tasks. You can do this by: 1. Defining a result storage block (e.g., using 
LocalFileSystem
for local disk storage). 2. Specifying the result storage in your tasks. Here's a step-by-step guide to configure and use result storage in Prefect, allowing you to specify where the cache should be saved. ### Step 1: Define a Result Storage Block First, define the result storage block. For local filesystem storage, you can create a 
LocalFileSystem
block.
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python
from prefect.filesystems import LocalFileSystem

# Define a LocalFileSystem block pointing to the desired cache directory
local_file_system = LocalFileSystem(basepath="/Users/username/.prefect/cache")
local_file_system.save("local-cache")
### Step 2: Update Task to Use Result Storage Update your tasks to use the defined result storage block for caching results. When defining your tasks, specify the 
result
argument.
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python
import os
from datetime import timedelta
import pandas as pd
from prefect import flow, task
from prefect.results import PersistedResult
from prefect.filesystems import LocalFileSystem

# Load the LocalFileSystem block
local_file_system = LocalFileSystem.load("local-cache")

def custom_cache_key_fn(task, parameters):
    process_name = parameters.get('process_name')
    task_name = task.name if not process_name else f"{task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks', '')

    refresh_flag = "refresh" if (refresh_cache_list == "all" or task_name in refresh_cache_list.split(',')) else "no_refresh"
    return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}"

@task(result=PersistedResult(storage=local_file_system), cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

@task
def set_working_directory(path: str):
    os.makedirs(path, exist_ok=True)
    os.chdir(path)
    print(f"Changed working directory to {os.getcwd()}")

@task
def check_working_directory():
    print(f"Current Working Directory: {os.getcwd()}")

@flow
def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None):
    set_working_directory("/Users/username/Desktop/myproject")
    check_working_directory()

    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks)

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Task Caching with Configured Storage 1. PersistedResult with Storage: - The 
PersistedResult
class is used to indicate where the task results will be stored. - The 
storage
argument specifies the use of the defined 
LocalFileSystem
block. 2. Custom Cache Key: - The cache key is managed through 
custom_cache_key_fn
. ### Running the Deployment Deploy and run your flow using the specified storage for caching:
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sh
# Deploy the flow
prefect deployment build my_flow.py:my_flow -n my_flow_deployment

# Apply the deployment
prefect deployment apply my_flow_deployment-deployment.yaml

# Run the deployment
prefect deployment run my_flow_deployment
### Summary - Define Storage Block: Create a storage block to specify where results should be cached. - Use Storage in Task: Update tasks to use the 
PersistedResult
with the defined storage. - Deploy and Run: Deploy and run the flow with the correctly specified cache location.
This setup ensures that Prefect saves the cached results in the specified directory, allowing you to control and manage cache storage effectively. Let me know if you need further assistance or details!
a
@Marvin what is the default path where the cache is saved?
m
The default path where Prefect stores cached task results depends on your storage configuration and the context in which Prefect is running. By default, Prefect stores results in a local temporary directory unless otherwise specified. ### Default Local Storage Path If you haven't specified a custom storage backend or result storage, Prefect typically uses a local path in the user's home directory: - Default Directory: 
~/.prefect
### Checking Default Path You can check or confirm the default path being used by Prefect for caching and other directories using the Prefect configuration. 1. Default Directory Setup: Prefect uses the home directory 
~/.prefect
to store various files, including cache and logs. 2. Actual Path: The actual path may look like this: - On Unix-based systems (Linux, macOS): 
/Users/username/.prefect
- On Windows: 
C:\Users\username\.prefect
### Changing the Default Directory You can override the default cache directory by specifying result storage, as detailed in the previous examples. However, here’s an overview of how you can configure Prefect to use a different directory or storage backend. ### Example: Viewing and Configuring the Cache Path By default, Prefect should save cache files in the 
~/.prefect/results
directory. You can confirm and change it as needed. #### Check Current Configuration
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python
from prefect.settings import PREFECT_HOME
print(PREFECT_HOME.value())  # This will print the path to the Prefect home directory
#### Changing the Default Path If you want to change the default path where Prefect saves cache files, you can set a custom result storage block. Here is a quick summary of how to do this with LocalFileSystem as an example: ### Using 
LocalFileSystem
Storage Block (Extended Example) 1. Define a result storage block with 
LocalFileSystem
.
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python
from prefect.filesystems import LocalFileSystem

# Define a LocalFileSystem block pointing to the desired cache directory
local_file_system = LocalFileSystem(basepath="/Users/username/custom_prefect_cache")
local_file_system.save("custom-local-cache")
2. Use the result storage block in your tasks.
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python
import os
from datetime import timedelta
import pandas as pd
from prefect import flow, task
from prefect.results import PersistedResult
from prefect.filesystems import LocalFileSystem

# Load the LocalFileSystem block
local_file_system = LocalFileSystem.load("custom-local-cache")

def custom_cache_key_fn(task, parameters):
    process_name = parameters.get('process_name')
    task_name = task.name if not process_name else f"{task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks', '')

    refresh_flag = "refresh" if (refresh_cache_list == "all" or task_name in refresh_cache_list.split(',')) else "no_refresh"
    return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}"

@task(result=PersistedResult(storage=local_file_system), cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1))
def save_csv(data: pd.DataFrame, relative_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    print(f"File saved to {file_path}")

# Example flow
@flow
def my_flow(process_name: str, run_date: str, cache_refresh_tasks: str = None):
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    save_csv(data1, ".myfolder/mysource_name/output1.csv", process_name, run_date, cache_refresh_tasks)

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Summary - Default Path: Prefect results and cache are typically stored in 
~/.prefect/results
by default.
- Custom Path: You can set a custom path using result storage blocks like 
LocalFileSystem
. - Configuration Check: Use Prefect's configuration and settings management (
PREFECT_HOME
) to check and customize paths. ### Checking and Customizing Path Check your home directory and existing settings to understand the paths being used and customize them if needed. If you need more details or have further questions, please let me know!
a
@Marvin let's try and do something like this: my flow have 3 tasks: download_data, deduplication, adapt_data each task saves a csv file in designated location: /Users/username/projects/my_project/.data/download_data/download_data.csv /Users/username/projects/my_project/.data/download_data/deduplication.csv /Users/username/projects/my_project/.data/download_data/adapted_data.csv I want that the cache file of each task will be saved under the appropriate path, how can I achieve that?
m
Based on the documentation and issues found, we can set the cache location for each task using custom result storage. Here's how you can save cache files in a specified directory for each task using Prefect's result storage mechanisms: ### Example Setup: Storing Cache Files in Specific Directories We'll define three tasks (
download_data
, 
deduplication
, 
adapt_data
) and set up the caching mechanism to store cache files under their respective directories. 1. Define Local File System Storage Blocks: 2. Configure Tasks to Use Custom Result Storage: #### Step-by-Step Implementation ### Step 1: Define Result Storage Blocks First, define separate storage blocks for each task.
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python
from prefect.filesystems import LocalFileSystem

# Define LocalFileSystem blocks for each task's cache directory
download_data_storage = LocalFileSystem(basepath="/Users/username/projects/my_project/.data/download_data")
deduplication_storage = LocalFileSystem(basepath="/Users/username/projects/my_project/.data/deduplication")
adapt_data_storage = LocalFileSystem(basepath="/Users/username/projects/my_project/.data/adapted_data")

download_data_storage.save("download-data-storage")
deduplication_storage.save("deduplication-storage")
adapt_data_storage.save("adapt-data-storage")
### Step 2: Update Tasks to Use Custom Result Storage Specify the result storage for each task using the storage blocks defined above. #### Updated Tasks ```python import os import pandas as pd from prefect import flow, task from datetime import timedelta from prefect.results import PersistedResult from prefect.filesystems import LocalFileSystem # Load the LocalFileSystem blocks for each task download_data_storage = LocalFileSystem.load("download-data-storage") deduplication_storage = LocalFileSystem.load("deduplication-storage") adapt_data_storage = LocalFileSystem.load("adapt-data-storage") def custom_cache_key_fn(task, parameters): process_name = parameters.get('process_name') task_name = task.name if not process_name else f"{task.name}_{process_name}" refresh_cache_list = parameters.get('cache_refresh_tasks', '') refresh_flag = "refresh" if (refresh_cache_list == "all" or task_name in refresh_cache_list.split(',')) else "no_refresh" return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}" @task(result=PersistedResult(storage=download_data_storage), cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1)) def download_data(data: pd.DataFrame, run_date: str, cache_refresh_tasks: str = None): file_path = "/Users/username/projects/my_project/.data/download_data/download_data.csv" directory = os.path.dirname(file_path) os.makedirs(directory, exist_ok=True) data.to_csv(file_path, index=False) print(f"File saved to {file_path}") @task(result=PersistedResult(storage=deduplication_storage), cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1)) def deduplication(data: pd.DataFrame, run_date: str, cache_refresh_tasks: str = None): file_path = "/Users/username/projects/my_project/.data/deduplication/deduplication.csv" directory = os.path.dirname(file_path) os.makedirs(directory, exist_ok=True) data.to_csv(file_path, index=False) print(f"File saved to {file_path}") @task(result=PersistedResult(storage=adapt_data_storage), cache_key_fn=custom_cache_key_fn, cache_expiration=timedelta(hours=1)) def adapt_data(data: pd.DataFrame, run_date: str, cache_refresh_tasks: str = None): file_path = "/Users/username/projects/my_project/.data/adapted_data/adapted_data.csv" directory = os.path.dirname(file_path) os.makedirs(directory, exist_ok=True) data.to_csv(file_path, index=False) print(f"File saved to {file_path}") @flow def my_flow(run_date: str, cache_refresh_tasks: str = None): data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) download_data(data1, run_date, cache_refresh_tasks)
# Use result of download_data; for simplicity, passing the same data deduplication(data1, run_date, cache_refresh_tasks) # Use result of deduplication; for simplicity, passing the same data adapt_data(data1, run_date, cache_refresh_tasks) if name == "__main__": from prefect.deployments import Deployment deployment = Deployment.build_from_flow( flow=my_flow, name="My Flow Deployment", work_pool_name="my-work-pool" ) deployment.apply()
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### Running the Deployments

Deploy and execute your flow:

```sh
# Deploy the flow
prefect deployment build my_flow.py:my_flow -n my_flow_deployment

# Apply the deployment
prefect deployment apply my_flow_deployment-deployment.yaml

# Run the deployment
prefect deployment run my_flow_deployment -p run_date="2023-10-01"
### Summary - Custom Result Storage: We defined custom 
LocalFileSystem
storage blocks for each task to control where the cache files are saved. - Task Configuration: Each task uses the 
PersistedResult
class with the relevant storage block, ensuring the results are stored in the specified directories. - Execution: Deploy and run the flow, which will save cache and task results to the specified paths. This approach guarantees that each task's cache files are saved in the appropriate directory specified by you. If you have any further questions or need more details, feel free to ask!
a
@Marvin can we make it more dynamic?
m
To make the setup more dynamic and configurable based on runtime parameters, you can construct the paths for result storage at runtime, allowing for greater flexibility. Here's how you can achieve this: ### Dynamic Path Configuration 1. Use Parameters to Define Paths: Pass the paths as parameters to your tasks and flows. 2. Create Storage Blocks Dynamically: Define and use 
LocalFileSystem
blocks dynamically within the tasks based on the parameters. ### Example: Dynamic Result Storage Path #### Step 1: Define and Use Dynamic Storage Blocks We will define tasks that dynamically set their result storage paths based on provided parameters.
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python
import os
import pandas as pd
from datetime import timedelta
from prefect import flow, task
from prefect.results import PersistedResult
from prefect.filesystems import LocalFileSystem

# Helper function to create a LocalFileSystem block dynamically
def create_local_file_system(basepath: str):
    return LocalFileSystem(basepath=basepath)

def custom_cache_key_fn(task, parameters):
    process_name = parameters.get('process_name')
    task_name = task.name if not process_name else f"{task.name}_{process_name}"
    refresh_cache_list = parameters.get('cache_refresh_tasks', '')

    refresh_flag = "refresh" if (refresh_cache_list == "all" or task_name in refresh_cache_list.split(',')) else "no_refresh"
    return f"{task_name}-{parameters.get('run_date')}-{refresh_flag}"

@task
def save_csv(data: pd.DataFrame, relative_path: str, storage_path: str, process_name: str, run_date: str, cache_refresh_tasks: str = None):
    # Create a dynamic LocalFileSystem block
    storage_block = create_local_file_system(basepath=storage_path)
    
    # Use PersistedResult with the dynamically created storage block
    result = PersistedResult(storage=storage_block)
    
    # Save CSV to the specified path
    file_path = os.path.abspath(relative_path)
    directory = os.path.dirname(file_path)
    os.makedirs(directory, exist_ok=True)
    data.to_csv(file_path, index=False)
    
    # Log and return result
    print(f"File saved to {file_path}")
    return result

@flow
def my_flow(run_date: str, cache_refresh_tasks: str = None):
    data1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})

    # Define paths
    download_data_path = "/Users/username/projects/my_project/.data/download_data"
    deduplication_path = "/Users/username/projects/my_project/.data/deduplication"
    adapt_data_path = "/Users/username/projects/my_project/.data/adapted_data"

    # Save CSVs
    save_csv(data1, f"{download_data_path}/download_data.csv", download_data_path, "download_data", run_date, cache_refresh_tasks)
    save_csv(data1, f"{deduplication_path}/deduplication.csv", deduplication_path, "deduplication", run_date, cache_refresh_tasks)
    save_csv(data1, f"{adapt_data_path}/adapted_data.csv", adapt_data_path, "adapt_data", run_date, cache_refresh_tasks)

if __name__ == "__main__":
    from prefect.deployments import Deployment

    deployment = Deployment.build_from_flow(
        flow=my_flow,
        name="My Flow Deployment",
        work_pool_name="my-work-pool"
    )
    deployment.apply()
### Explanation: 1. Helper Function: The 
create_local_file_system
helper function dynamically creates a 
LocalFileSystem
block based on the specified base path. 2. Dynamic Storage in Task: In the 
save_csv
task, a 
LocalFileSystem
block is created dynamically when the task is invoked. This block is then used for the task's result storage. 3. Parameterized Paths: Paths for storing results are passed as parameters, making the setup dynamic and flexible based on the runtime configuration. ### Running with Dynamic Paths Deploy and run the flow, specifying any necessary parameters: ```sh # Deploy the flow prefect deployment build my_flow.py:my_flow -n my_flow_deployment # Apply the deployment prefect deployment apply my_flow_deployment-deployment.yaml # Run the deployment with dynamic paths
prefect deployment run my_flow_deployment -p run_date="2023-10-01" ``` This approach allows for a flexible configuration where you can define and change storage paths dynamically, providing a high degree of control over where task results are cached. If you need more specifics or further adjustments, please let me know!
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