@Marvin Prefect 3.x How can I run a deployed flow as a subflow so I can get the returned data from the workflow to pass to another function?

To run a deployed flow as a subflow in Prefect 3.x and get the returned data, you can use the

run_deployment

method. This method is ideal for running a flow independently, allowing you to handle the returned data as needed for further processes. Here are the main points to consider: 1. Nested Flows: You can include a flow as a nested flow within another flow by importing the necessary modules and using

run_deployment

. This allows you to group tasks conditionally, enhancing observability and parameterization. More details can be found here. 2. Handling Results: The data returned from a flow must be serializable. Prefect automatically converts generator results into a list, which you can then use in your subsequent functions. Be careful with large or infinite generators to avoid performance issues. For further information, you can refer to this guide. While attempting to run the same subflow concurrently multiple times, you may encounter issues such as a

RuntimeError

related to task runners, as detailed in this GitHub issue. To ensure you're not chasing after your own coding tail, consider this question: Do you really want to recreate Prometheus's looping punishment by running identical subflows in parallel? If not, look into modular flows and tasks for simplified solutions.

@Marvin Can you generate a code snippet that calls a deployed flow with a given name or id and passes the returned data to a new function? all within a flow

To call a deployed flow by its name or ID and pass the returned data to another function within a flow in Prefect 3.x, you can utilize the

run_deployment

method. This example demonstrates how to achieve that:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def process_data(data):
    # Process the returned data from the subflow deployment
    print(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
def main_flow(deployment_name):
    # Run the subflow as a deployed flow
    subflow_run = run_deployment(deployment_name=deployment_name)
    
    # Wait for the subflow to complete and get the result
    subflow_result = subflow_run.get_result()
    
    # Pass the result to another function for further processing
    processed_result = process_data(subflow_result)
    
    print(f"Final processed result: {processed_result}")

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

In this code snippet: -

run_deployment

is used to execute a deployed flow specified by its

deployment_name

. - The result of the deployed flow execution is retrieved using

get_result()

. - The retrieved data is then processed by the

process_data

function, which represents any further operations you wish to perform with the data. For more details on deployments and running deployed flows, you can check the Prefect documentation here: Prefect Deployments. And remember, even data needs a vacation sometimes—preferably not stuck in an infinite loop!

@Marvin when I run the code abbove I get the following error:

subflow_result = subflow_run.get_result()
                     ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'coroutine' object has no attribute 'get_result

The error you're encountering suggests that the

run_deployment

call returns a coroutine, which means you need to use

await

to obtain the result. Here's an updated version of the code that accounts for asynchronous execution:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
async def process_data(data):
    # Process the returned data from the subflow deployment
    print(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
async def main_flow(deployment_name):
    # Run the subflow as a deployed flow and await its result
    subflow_run = await run_deployment(deployment_name=deployment_name, timeout=60)
    
    # Get the result of the subflow
    subflow_result = await subflow_run.get_result()
    
    # Pass the result to another function for further processing
    processed_result = await process_data(subflow_result)
    
    print(f"Final processed result: {processed_result}")

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

- Note how

await

is used with

run_deployment

and

get_result

to ensure proper asynchronous execution. - Ensure that your environment supports asynchronous code execution. For more insights on handling asynchronous operations with Prefect, you may refer to their documentation on asynchronous execution. Ah, the joys of async programming—like juggling, but with less physical harm involved!

@Marvin even with await I'm getting the same error message

It seems I need to make a clarification on how the

run_deployment

function is used to fetch results. The proper way to handle deployment results involves checking the state of the deployment. Here's an updated way to approach this:

python
from prefect import flow, get_run_logger
from prefect.deployments import run_deployment
from prefect.states import State

@flow
def process_data(data):
    # Process the returned data from the subflow deployment
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
async def main_flow(deployment_name):
    # Run the subflow as a deployed flow
    subflow_state = await run_deployment(name=deployment_name)
    
    # Check if the subflow run succeeded
    if isinstance(subflow_state, State) and subflow_state.is_completed():
        # Get the result of the subflow from the state
        subflow_result = subflow_state.result()
        # Pass the result to another function for further processing
        processed_result = process_data(subflow_result)
        print(f"Final processed result: {processed_result}")
    else:
        print("Subflow did not complete successfully.")

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

In this code: - The result is extracted from the state object. I added a check to ensure the subflow completed successfully before attempting to access its result. - Please ensure you have defined the deployment correctly and have internet access if it's running on Prefect Cloud. This should align you with the asynchronous handling in Prefect 3.x. You can refer to further deployment handling in the Prefect Deployments documentation. If this still doesn't solve the issue, feel free to curse modern computing—I won't judge!

@Marvin How can I wait, that the subflow_state reaches complete?

To wait for a subflow state to reach completion in Prefect 3.x, you can use the

wait

functionality provided by the Prefect library. This allows you to asynchronously wait for a flow run to complete and handle it accordingly. Here’s how you can do that:

python
from prefect import flow, get_run_logger, task
from prefect.deployments import run_deployment
from prefect.states import State

@task
def process_data(data):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
async def main_flow(deployment_name):
    # Start the deployment
    subflow_state = await run_deployment(name=deployment_name)
    
    # Asynchronously wait for the subflow to reach a completed state
    final_state = await subflow_state.wait()
    
    # Check if the subflow run succeeded
    if final_state.is_completed():
        # Access the result once the flow is complete
        subflow_result = final_state.result()
        # Process the result
        processed_result = process_data(subflow_result)
        print(f"Final processed result: {processed_result}")
    else:
        print("Subflow did not complete successfully.")

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

In this example: - The

wait()

method is called on

subflow_state

to wait until the flow completes. - Once the flow completes, you can check its state using

is_completed()

and retrieve its result using the

result()

method. You can find more about handling state management in the Prefect state management documentation. This should help you manage the asynchronous execution flow without prematurely accessing or checking incomplete states. Ah, if only waiting for all things in life were this structured...

@Marvin now I'm getting the error: AttributeError: 'FlowRun' object has no attribute 'wait'

It appears I provided incorrect information about the

wait

method. In Prefect 3.x, the

FlowRun

object doesn't have a

wait

method directly. Instead, you can manually poll the flow's state using Prefect's client capabilities to check for completion. Here's an alternative approach:

python
from prefect import flow, get_run_logger, task
from prefect.deployments import run_deployment
from prefect.client import get_client
import asyncio

@task
def process_data(data):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
async def main_flow(deployment_name):
    # Start the deployment asynchronously
    flow_run = await run_deployment(name=deployment_name)
    
    # Use the client to poll for flow completion
    async with get_client() as client:
        while True:
            state = await client.read_flow_run_state(flow_run.id)
            if state.is_completed():
                subflow_result = state.result() 
                # Process the result
                processed_result = process_data(subflow_result)
                print(f"Final processed result: {processed_result}")
                break
            elif state.is_failed():
                print("Subflow failed.")
                break
            else:
                print("Waiting for subflow to complete...")
                await asyncio.sleep(5)  # Poll every 5 seconds

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

In this updated example: - We use the client to continuously check the flow run's state until it is completed or failed. - Polling is done with a delay using

await asyncio.sleep(5)

to avoid overwhelming the server with requests. For more guidance, you can refer to the Prefect API and client interaction documentation. Ah, the art of waiting in the digital age—a place where silence is far from golden!

@Marvin is is called read_flow_run_states and apparently it returns a list, not a state

To check for the completion of a flow run, you indeed need to handle a list of states when using

read_flow_run_states

. Here's how you can properly manage this in Prefect 3.x:

python
from prefect import flow, get_run_logger, task
from prefect.deployments import run_deployment
from prefect.client import get_client
import asyncio

@task
def process_data(data):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
async def main_flow(deployment_name):
    # Start the deployment asynchronously
    flow_run = await run_deployment(name=deployment_name)
    
    # Use the client to poll for flow completion
    async with get_client() as client:
        while True:
            # Retrieve the most recent state
            states = await client.read_flow_run_states(flow_run.id)
            current_state = states[-1] if states else None

            if current_state and current_state.is_completed():
                subflow_result = current_state.result() 
                # Process the result
                processed_result = process_data(subflow_result)
                print(f"Final processed result: {processed_result}")
                break
            elif current_state and current_state.is_failed():
                print("Subflow failed.")
                break
            else:
                print("Waiting for subflow to complete...")
                await asyncio.sleep(5)  # Poll every 5 seconds

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

- In this code, I'm calling

read_flow_run_states

and accessing the latest state in the list to determine if the flow is complete. - The loop continues polling every 5 seconds until the flow reaches a

COMPLETED

or

FAILED

state. Feel like navigating through a sea of states in this system? It’s a bit like finding a needle in a haystack, but thankfully with a well-documented map! For more detailed handling, check out Prefect's manage states documentation.

@Marvin after adding await to current_state.result() I'm now getting the following error: prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API.

The error you're seeing indicates that the result of your flow is not being persisted, which is why it's unavailable when you're trying to access it. By default, Prefect doesn't persist results unless specifically configured to do so. Here's how you can enable result persistence in Prefect 3.x: 1. Enable Persistence Globally: The easiest way is through the

PREFECT_RESULTS_PERSIST_BY_DEFAULT

setting. You can set this in your environment to enable persistence across all flows and tasks. 2. Configure Result Storage: You must also specify where these results should be stored. This can be configured through the

result_storage

keyword and the

PREFECT_DEFAULT_RESULT_STORAGE_BLOCK

setting. You need to choose a storage block, such as a local file system or a cloud storage service like S3. Here's how you can set this up:

python
import os

# Set environment variable to persist results by default
os.environ['PREFECT_RESULTS_PERSIST_BY_DEFAULT'] = 'true'

from prefect import flow, task
from prefect.deployments import run_deployment
from prefect.client import get_client
import asyncio

@task
def process_data(data):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data: {data}")
    result = data * 2  # Example processing step
    return result

@flow
async def main_flow(deployment_name):
    # Start the deployment asynchronously
    flow_run = await run_deployment(name=deployment_name)
    
    # Use the client to poll for flow completion
    async with get_client() as client:
        while True:
            # Retrieve the most recent state
            states = await client.read_flow_run_states(flow_run.id)
            current_state = states[-1] if states else None

            if current_state and current_state.is_completed():
                subflow_result = await current_state.result() 
                # Process the result
                processed_result = process_data(subflow_result)
                print(f"Final processed result: {processed_result}")
                break
            elif current_state and current_state.is_failed():
                print("Subflow failed.")
                break
            else:
                print("Waiting for subflow to complete...")
                await asyncio.sleep(5)  # Poll every 5 seconds

# Replace 'your-deployment-name' with your actual deployment name or ID
main_flow(deployment_name='your-deployment-name/deployment-id')

For more information on configuring result persistence and storage, refer to the Prefect results documentation. Essentially, you’re ensuring that data won’t vanish into the void, which is good unless you're trying to emulate a magical trick!

@Marvin How can I change the prefect config of a running server

To change the configuration of a running Prefect server, you will typically need to modify the server settings in the configuration file or environment variables and then restart the server to apply these changes. Here are some steps you might follow: 1. Access Configuration: Prefect server configurations are usually stored in a

prefect.toml

file or can be managed through environment variables. For example, you could set variables like

PREFECT_API_URL

and

PREFECT_API_KEY

for API interactions. 2. Modify Configuration: Change the necessary settings, such as

server.database.connection_url

if you need to point to a different database or

ui.api_url

to adjust the URL settings when using a reverse proxy. 3. Restart the Server: After making changes to the configuration file or environment variables, restart your Prefect server. This is generally required as live updates aren't applied automatically. For more detailed guidance, you might want to check out the Prefect settings and profiles documentation. Unfortunately, until we reach the utopia of live config changes without server restarts, there's always the restart button—or a strategically timed coffee break!

@Marvin even when setting PREFECT_RESULTS_PERSIST_BY_DEFAULT to True for the server, I still get the error message

It seems that setting

PREFECT_RESULTS_PERSIST_BY_DEFAULT

alone might not be resolving your issue with result persistence. Here are a few troubleshooting steps you can follow: 1. Ensure Correct Configuration: Double-check that the environment variable

PREFECT_RESULTS_PERSIST_BY_DEFAULT

is correctly set to

true

in your Prefect server environment and applied consistently across all relevant components. 2. Specify Result Storage: In addition to enabling result persistence, you often need to specify where these results are stored. You can configure this by setting the

result_storage

keyword or using the

PREFECT_DEFAULT_RESULT_STORAGE_BLOCK

setting to point to a valid storage backend, like a local filesystem or a cloud service. 3. Code Check: Ensure that the flows and tasks from which results are expected are actually returning values and are correctly configured to have their results persisted. 4. Review Prefect Logs: Inspect Prefect's server logs for any warnings or errors related to result persistence. These logs might provide more insight into what's going wrong. 5. Profile and Settings: Make sure your prefect profile is active and that settings are correctly applied in your running environment. For more guidance on setting up result persistence, you might want to revisit the results management documentation. It's a bit like making sure you’ve saved your work in that important document, otherwise, it’s like it was never there!

@Marvin How do I set the result storage to be the local filesystem

To set up result storage on the local filesystem in Prefect 3.x, you'll need to configure the

result_storage

keyword. This specifies the filesystem where Prefect should store the results. Here’s how you can do it: 1. Enable Result Persistence: Make sure that result persistence is enabled using

PREFECT_RESULTS_PERSIST_BY_DEFAULT=true

. 2. Configure Local Filesystem as Result Storage: Specify the local filesystem as the storage destination. This can typically be done using the

result_storage

keyword when setting up your flow or in your Prefect configuration file. Here’s a basic example of how you might configure your flow with local filesystem storage:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os

# Define where to store results locally
local_file_system = LocalFileSystem(basepath="~/.prefect/flows/")

@task
def process_data(data):
    return data * 2

@flow(result_storage=local_file_system)
def my_flow(data):
    result = process_data(data)
    print(f"Processed result: {result}")

if __name__ == "__main__":
    # Enable persistence by default
    os.environ['PREFECT_RESULTS_PERSIST_BY_DEFAULT'] = 'true'

    # Run the flow
    my_flow(5)

### Considerations: - Storage Location: Ensure the path provided to

LocalFileSystem

is writable by the running process. - Permissions: Verify that your Prefect server or agent has the necessary permissions to read from/write to the specified directory. For more detailed configuration options, you can refer to Prefect's results documentation. While it may seem like giving your results a cozy home is tricky, just remember: every data bit deserves a roof over its head!

@Marvin I know get a ValueError that the storage file doesn't exist

When using Prefect's

LocalFileSystem

for result storage, you need to ensure that the specified directory actually exists before running the flow. Prefect won't automatically create the directory for you, so you'll need to create it manually or programmatically. Here’s how you can create the directory programmatically if it doesn’t already exist:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os

# Define the base path for storing results
base_path = os.path.expanduser("~/.prefect/flows/")

# Ensure the directory exists
if not os.path.exists(base_path):
    os.makedirs(base_path)

# Define local file system storage with the specified base path
local_file_system = LocalFileSystem(basepath=base_path)

@task
def process_data(data):
    return data * 2

@flow(result_storage=local_file_system)
def my_flow(data):
    result = process_data(data)
    print(f"Processed result: {result}")

if __name__ == "__main__":
    # Enable persistence by default
    os.environ['PREFECT_RESULTS_PERSIST_BY_DEFAULT'] = 'true'

    # Run the flow
    my_flow(5)

### Key Points: - Directory Creation: The

os.makedirs

function ensures that the directory for storing results exists or creates it recursively if necessary. - Path Expansion:

os.path.expanduser

is used to correctly interpret the

~

as the user's home directory. By creating the storage directory, you prevent the

ValueError

and ensure a smooth operation when Prefect tries to save the flow results, making your digital world a little less chaotic! Feel free to celebrate with an extra byte of data.

@Marvin the result is also stored in the cache in the flow. Is there a way to access the data via the cache?

To access data from the cache in a Prefect flow, you can use the concept of cache keys. Prefect assigns a cache key to the results and stores them in the configured result storage. Here's how you can utilize caching and retrieve cached results: 1. Cache Keys: Each task has a cache key that determines its stored result. Cached results are retrieved using these keys and can be common across tasks and flows if they share a storage location. 2. Accessing Cached Results: - Cached states are used when a result is already present and unexpired, meaning the task can skip execution and use the cache directly. - By default, Prefect stores results in

~/.prefect/storage/

if using local storage. Here's an example of how you can access cached results using cache keys:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os

# Define local file system storage with the specified base path
result_storage_path = "~/.prefect/storage/"
local_file_system = LocalFileSystem(basepath=result_storage_path)

@task(cache_key_fn=lambda *_: "fixed-key", cache_expiration=None)  # Simplified example
def process_data(data):
    return data * 2

@flow(result_storage=local_file_system)
def my_flow(data):
    result = process_data(data)
    print(f"Processed result: {result}")

if __name__ == "__main__":
    # Make sure the result storage directory exists
    os.makedirs(os.path.expanduser(result_storage_path), exist_ok=True)

    # Run the flow
    my_flow(5)

### Key Points: - Cache Key Function: You can define a custom cache key function for tasks using

cache_key_fn

that returns unique keys based on input parameters. - Cache Expiration: By adjusting

cache_expiration

, you can control how long results remain cached and valid for subsequent use without re-running the task. These steps should allow you to manage results more efficiently, whether you're dealing with valuable data or just trying to avoid work (like deciding not to retake your own advice for the umpteenth time!). For further information, you can explore Prefect's caching documentation.

@Marvin The local file system the data is stored in is on a remote machine. Can I use the Block "Remote File System" to access the storage via this block whenever a deployed flow returns data when the deployed flow was called by a flow on my local machine?

Yes, you can indeed use a Prefect "Remote File System" block to access storage on a remote machine. This can be particularly useful for scenarios where a deployed flow runs on a remote server and needs to store or retrieve data that can be accessed by flows running on different machines, such as your local environment. ### How to Use a "Remote File System" Block: 1. Set Up the Remote File System Block: Prefect allows you to configure a remote file system block (e.g., S3, Azure, etc.), where you specify connection details and credentials to your remote storage. This setup helps unify data access paths across distributed systems. 2. Configure Result Storage: Ensure your flow's result storage leverages the configured remote file system. This means both the deployed flow and the flows on your local machine refer to the same remote storage location. 3. Accessing Stored Results: - Flows store results in the specified remote storage location. - Use the

flow.from_source

method for retrieving flows from remote storage, which will enable you to instantiate and use previously cached results stored remotely. ### Example: Here's a conceptual overview that you'd implement:

python
from prefect import flow, task
from prefect.filesystems import RemoteFileSystem  # you obtain the actual block from your Prefect setup

# Set up your remote file system block instance here with required details
remote_file_system = RemoteFileSystem(basepath="remote_path_or_bucket_url", 
                                      credentials={"key": "your_access_key", "secret": "your_secret"})

@task
def process_data(data):
    return data * 2

@flow(result_storage=remote_file_system)
def deployed_flow(data):
    result = process_data(data)
    print(f"Processed result: {result}")

if __name__ == "__main__":
    # Run the deployed flow assuming it's on remote storage
    deployed_flow(5)

### Key Considerations: - Ensure that all machines that need to access the remote storage have the credentials and network access required. - Validate that the remote storage solution you're using (like S3 or Azure Blob Storage) supports the kind of access patterns you intend, such as concurrent writes and reads. Using a remote file system can centralize and simplify how your flows interact with stored data, reminiscent of the early days of cloud computing where storage felt like a mystery (without requiring divine intervention)!

@Marvin I'm currently hosting my prefect server with multiple deployments on a remote machine in my network. I'm now calling these deployed flows within a flow that is running on my local machine. These deployed flows are returning some kind of data. How can I know access their return values without the use of cloud services like S3 Bucket?

To access the return values of deployed flows running on a remote Prefect server from your local machine without using cloud services, you can utilize the Prefect server's capabilities to store results within a remote filesystem accessible over your network. Here’s how you can achieve this: ### Steps to Access Return Values: 1. Use Local or Network File Systems: - Configure a shared network drive or filesystem that both your remote Prefect server and your local machine can access. This setup does not involve cloud services, but a simple on-premises or LAN-based storage solution. 2. Set Up Prefect Server to Use the Shared Storage: - Modify your Prefect server’s configuration to use this shared drive for result storage by setting up a custom storage block or configuring the

result_storage

setting directly. 3. Access Results from Your Local Machine: - Ensure that your local machine has the correct network permissions and mounting configurations to access the shared drive. - Retrieve the result files directly from this shared location. ### Example Configuration: Here's a conceptual illustration of how this setup may look:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os

# Assuming this path is a shared network path accessible by both the server and your local machine
shared_storage_path = "/network/shared/storage/path"

@task
def process_data(data):
    return data * 2

@flow(result_storage=LocalFileSystem(basepath=shared_storage_path))
def deployed_flow(data):
    result = process_data(data)
    return result

# The deployed flow on your server writes results to the shared storage

@flow
def local_flow():
    # Example of accessing results by reading directly from shared storage
    result_path = os.path.join(shared_storage_path, "result_file_location_from_deployed_flow")
    with open(result_path, 'r') as file:
        result = file.read()
    print(f"Retrieved result: {result}")

if __name__ == "__main__":
    local_flow()

### Key Points: - Shared Network Drive: Configuration depends on your network setup but ensures you do not rely on external cloud services. - Rights & Permissions: Make sure the Prefect server and your local machine have appropriate read/write access to the shared storage location. By setting up a shared storage that acts like a local file system accessible across your network, you can maintain control over your data and flow results, quite like keeping everything in your ‘digital backyard’!

@Marvin This doesn't work since the result is a pandas dataframe

When dealing with a Pandas DataFrame as the result, you rather need a way to serialize and deserialize it efficiently across machines without losing its structure and content. Here's a method using flat files that both your Prefect server and local code can access: ### Steps to Share Pandas DataFrame Results: 1. Serialize the DataFrame: Use a format that preserves the DataFrame’s structure, like CSV, Parquet, or Pickle. Ensure that the serialized file is stored in a shared filesystem that is accessible both by the Prefect server and your local machine. 2. Configure Shared Storage: Use a shared directory over your local network that both your Prefect server's deployments and local machine can write to and read from. 3. Access the Serialized File: Once the DataFrame is serialized and stored, read it back in your local machine’s flow, deserializing it into a DataFrame. ### Example Code: Here’s how you can implement this:

python
import pandas as pd
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os

# Path configuration for shared storage
# Ensure this path is network-accessible and writable by both server and local machine
shared_storage_path = "/network/shared/storage/path"

@task
def generate_data():
    # Replace with actual DataFrame generation process
    data = pd.DataFrame({'a': range(5), 'b': range(5, 10)})
    return data

@task
def save_dataframe(df):
    # Save DataFrame as a CSV file in the shared storage
    file_path = os.path.join(shared_storage_path, "results.csv")
    df.to_csv(file_path, index=False)
    return file_path

@flow(result_storage=LocalFileSystem(basepath=shared_storage_path))
def deployed_flow():
    df = generate_data()
    file_path = save_dataframe(df)
    return file_path

@flow
def local_flow():
    # Assuming the deployed flow has already run and written the results
    file_path = os.path.join(shared_storage_path, "results.csv")
    df = pd.read_csv(file_path)
    print(f"Retrieved DataFrame:\n{df}")

if __name__ == "__main__":
    local_flow()

### Key Considerations: - File Format: CSV or Parquet formats are suitable as they are interoperable and efficient for DataFrame serialization. - Access and Permissions: Ensure both the Prefect server and your local machine have the necessary file system access. - Synchronization: Be mindful of potential race conditions—ensure that the deployment writes to the shared directory before your local machine tries to read. By carefully using shared local storage and proper file formats, you can manage DataFrame results across machines without relying on external cloud services—keeping data in check quite nicely!