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<@ULVA73B9P> if a flow gets interrupted midway bec...
# ask-marvin
m
@Marvin if a flow gets interrupted midway because the worker nodes got terminated for some reason, does prefect know how to resume the run and continue work where it left off last time it was running ? Or does the developer need to know how to handle these scenarios ?
m
Could you let me know which version of Prefect you are using? Prefect 2.x and 3.x might have different approaches to handling interruptions in flow execution.
m
@Marvin prefect 3.x
m
In Prefect 3.x, handling interrupted flows is something that requires some attention. Prefect provides mechanisms to pause or suspend and then resume a flow. Here's a brief overview: - Pause a Flow: Pausing a flow stops the code execution while keeping the process running. You can manually resume the flow, either from the Prefect UI or programmatically using the 
resume_flow_run
function. - Suspend a Flow: Suspending a flow stops both the code execution and the process. When you resume, the flow execution restarts from the beginning. However, it's recommended to use tasks and task caching to avoid re-executing expensive operations. Using these concepts, you can manage interruptions by pausing or suspending flows. For example, if your infrastructure is temporarily interrupted, you might suspend the flow and then resume it once everything is back up, utilizing caching to prevent unnecessary recomputation. For more details, you can check out the Prefect documentation on pause and resume. Oh, the joy of modern technology where flows can take a coffee break and return without missing a beat!
m
@Marvin question: what happens if a task in a flow raises an exception? Does the flow gets terminated automatically meaning all tasks running in parallel or left to be run are not executed or are other parallel tasks executed on the flow still and once they terminate the flow stops?
m
In Prefect 3.x, if a task within a flow raises an exception, it does not necessarily terminate the entire flow. Here's how it works: - Task Exceptions: By default, if a task raises an exception, it will fail. However, this does not automatically propagate to fail the entire flow. The flow can continue to run other parallel tasks that were not dependent on the failed task. - Flow Completion: The final state of a flow is determined by the return value of the flow function and how exceptions are handled. If you allow exceptions to propagate without handling them (by not using 
raise_on_failure=False
), then the flow will terminate with a failure state. - Handling Failures: You can explicitly manage task failures using try/except blocks, or by returning specific states through Prefect's task state management. This way, you could allow other parts of the flow to continue or make decisions based on the failure. For more guidance on handling task failures, you can refer to the Prefect documentation on upgrading to Prefect 3, which discusses strategies for handling task exceptions and flow completion. As a quirky twist, I suppose one could say tasks are like cats—independent creatures that sometimes do what they want without bringing down the whole house!
m
@Marvin And if we have nested flows inside a flow? does the main flow get abruptly terminated in case one of the nested flows fails ?
m
In Prefect 3.x, the behavior of nested flows or subflows follows similar principles as tasks when it comes to failure and exception handling. The main flow does not automatically terminate if one of the nested or child flows fails. Instead, the continuation depends on how exceptions are handled: - Final State Determination: The final state of the main flow is determined by its function's return value and how it handles exceptions. - If a child flow fails and the exception is allowed to propagate, the main flow might also be marked as failed. - Alternatively, you can handle exceptions within the child flow to prevent them from affecting the main flow's state. - Flow Continuation: If exceptions in a child flow are caught and handled, the main flow can continue executing other tasks or subflows, independent of the failing child flow. Essentially, you have the control to manage failures in nested flows through exception handling mechanisms. For more details, the upgrade guide to Prefect 3 provides insight into managing flow final states and handling exceptions. Much like a chain with strong links, the whole doesn't always crumble if one link wobbles—especially if you've got Prefect's error handling in place!
m
@Marvin how well does prefect 3.x handle thousands of tasks running in parallel for a given flow? Does it consume too much memory on the server side that it impacts all running flows? the diagram becomes too big to be able to be visualized? Are there important downsides of using thousands of parallel tasks in a single flow?
m
In Prefect 3.x, handling thousands of tasks running in parallel is quite feasible, but there are considerations and potential downsides to be aware of: 1. Memory and Resource Constraints: Running a large number of tasks in parallel can indeed consume significant memory and computing resources on the server. If not managed properly, it can impact not only the current flow but potentially other flows running on the same resource. 2. Task Scheduling Challenges: There have been reports, such as in this GitHub issue, where scheduling thousands of tasks can sometimes lead to bottlenecks causing the system to hang or even crash with a timeout. 3. Visualization Difficulties: Visualizing the structure of such large flows can become cumbersome. The Prefect UI may struggle to generate a meaningful schematic when there are dynamic workflows or a vast number of interconnected tasks. 4. Dask TaskRunner Considerations: If you're using the DaskTaskRunner to manage task execution, you might encounter additional overheads or need to configure your task clusters carefully to avoid bottlenecks. To manage these issues effectively: - Consider breaking down the flow into smaller, more manageable sub-flows. - Use task runners and infrastructure that are robust and scalable. - Implement caching strategies to reduce redundant computations. - Monitor resource usage and adjust your cluster and task settings accordingly. While managing large-scale parallel tasks is entirely possible with Prefect, attention to resources and strategic flow architecture can help you avoid feeling like you're navigating a house of cards!
m
@Marvin if instead I split up thousands of tasks running in parallel in a single flow to thousands of flows, could I still have a way to visualize the lineage of executions on prefect on the UI ? For example, one flow would trigger the execution of multiple flows that themselves would also trigger the execution of another set of flows. How could this be visualized as a single, unified flow of events on prefect ?
m
In Prefect 3.x, creating a unified visualization for a complex hierarchy of flows—where one flow triggers multiple other flows, each potentially triggering further flows—is a bit challenging but still manageable with some considerations: 1. Visualization of Flow Structures: The 
visualize()
method provides a way to look at the structure of a flow and its tasks. However, this method is limited when it comes to dynamic or nested workflows. Prefect does not automatically consolidate across multiple, separately run flow instances into a single visual diagram of dependency. 2. Tracking and Observability: Nested flows can be tracked, but canceling a nested flow requires canceling its parent unless the flows are independently deployed. Observability can be maintained at the level of individual flows, and you will need to piece together the lineage yourself, possibly using external tools to track IDs and logs across flows. 3. Manual Aggregation: For a true lineage view, you might need to rely on additional external tools to manually map the dependencies and run outputs, as Prefect doesn't natively offer a UI feature to "combine" multiple independent flow visualizations into a single comprehensive diagram. 4. Deployment and Logging Strategy: To maintain visibility, consider deploying flows in a way that they can independently report back to a central logging system or dashboard. This lets you compile the necessary lineage or dependency information externally. For reference, you can explore more in the Prefect documentation about visualizing flow structures and nested flow execution. While Prefect excels in managing tasks and flows, visualizing complex nested dependencies might need a bit of extra elbow grease—sort of like wrangling a herd of cats into a neat parade!
m
@Marvin when triggering flows based on other flows using an automation, how can I send to the triggered flow in a deployment a dynamically generated value on the source flow that triggered the event to the destination deployment (the one being triggered after the source flow ends)? My idea is to transmit an UUID from the source flow to the target flow, and this UUID gets generated automatically on the source flow. How can this be achieved ?
m
To pass a dynamically generated UUID from a source flow to a triggered flow within Prefect 3.x, you'll need to set up parameter passing between these flows using the Prefect API or deploying with structured configurations. Here's how you can achieve this: 1. Generate UUID in Source Flow: In your source flow, generate the UUID as part of its execution. Ensure it's stored in a variable you can access when triggering the next flow. 2. Trigger Destination Flow with Parameters: When setting up your automation to trigger the next flow (the destination flow), you'll use the Prefect API to send the generated UUID as a parameter to the deployment of the destination flow. 3. Passing Parameters to a Deployment: Prefect allows you to pass parameters to a flow run initiated via the Prefect API. When triggering a flow deployment, specify the parameters as keyword arguments. This means you can add the UUID as a named parameter in the API call to start the flow run. 4. Example Code Snippet: - In your source flow, create the UUID. - Use the Prefect client to start the destination flow with the UUID as a parameter.
Copy code
python
from prefect import flow
import uuid
from prefect.client import get_client

@flow
def source_flow():
    my_uuid = uuid.uuid4()
    print(f"Generated UUID: {my_uuid}")
    
    # Use Prefect client to trigger the destination flow with my_uuid
    trigger_destination_flow_with_uuid(my_uuid)

def trigger_destination_flow_with_uuid(my_uuid):
    client = get_client()
    client.create_flow_run_from_deployment(
        deployment_id="your_deployment_id",
        parameters={"uuid_param": my_uuid}
    )

@flow
def destination_flow(uuid_param):
    print(f"Received UUID: {uuid_param}")

if __name__ == "__main__":
    source_flow()
5. Deployment: Ensure your destination flow's deployment has the parameter 
uuid_param
configured to accept the incoming UUID. With this approach, you can dynamically pass data between flows, maintaining flexibility and robustness within your Prefect orchestrations. For more detailed guidance, check out the Prefect documentation on flow parameters and deployments. Remember, while the complexity of manual data stitching can seem like planning a space mission, you'll have quite the computing hustle that might even impress Marvin!
m
@Marvin how can I run nested flows in parallel in prefect 3.x ?
m
In Prefect 3.x, you can run nested flows in parallel by making use of asynchronous capabilities. Here's how you can achieve this: 1. Nested Flow Concept: When you define nested flows, they behave like individual flow runs linked to their parent. Each child flow runs independently, which means they can be managed and observed through the Prefect UI. 2. Parallel Execution with Asynchronous Flows: By utilizing asynchronous programming, you can run nested flows concurrently. This involves using asynchronous constructs like 
AnyIO
task groups or 
asyncio.gather
. 3. Implementing Asynchronous Nested Flows: You would define your flows using Python's asyncio capabilities. Here's a basic example:
Copy code
python
from prefect import flow
import asyncio

@flow
async def child_flow(name):
    print(f"Running child flow: {name}")
    await asyncio.sleep(1)
    print(f"Child flow {name} completed")

@flow
async def parent_flow():
    tasks = [child_flow(name=f"child_{i}") for i in range(5)]
    await asyncio.gather(*tasks)

if __name__ == "__main__":
    asyncio.run(parent_flow())
In this example, 
parent_flow
runs multiple instances of 
child_flow
in parallel using 
asyncio.gather
. Each child flow will execute concurrently without blocking the parent flow. 4. Considerations: Ensure that your infrastructure and task runners are configured to support asynchronous operations and handle the workload generated by parallel executions. You can also apply concurrency limits and priority settings as needed. For a deeper dive into writing and managing flows, refer to Prefect's documentation on writing flows. Running nested flows in parallel is like orchestrating a symphony—allowing everything to play beautifully in unison, rather than one note at a time!
m
@Marvin can I use a taskrunner on a flow to run nested flows in parallel ?
m
Yes, you can use a task runner to execute nested flows in parallel in Prefect 3.x. Task runners in Prefect are designed to facilitate the concurrent, parallel, or distributed execution of tasks and flows. Here's how you can achieve this: 1. Task Runner Integration: Prefect supports several task runners, such as the 
ThreadPoolTaskRunner
, 
DaskTaskRunner
, and 
RayTaskRunner
. Each of these can be used to run tasks or flows concurrently. 2. Running Nested Flows with Different Task Runners: You can define nested flows to use specific task runners according to the nature of their tasks. For example, while a parent flow might use a 
ThreadPoolTaskRunner
, a nested flow could be configured with a 
DaskTaskRunner
to handle tasks more efficiently in parallel on a Dask cluster. 3. Example to Run Flows in Parallel: - Use a task runner within a flow to run nested flows concurrently. Here's how you might define and run nested flows using a task runner:
Copy code
python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner)
def child_flow(name):
    print(f"Running child flow: {name}")

@flow
def parent_flow():
    child_flows = [child_flow(name=f"child_{i}") for i in range(5)]

if __name__ == "__main__":
    parent_flow()
In this example, each 
child_flow
can run in parallel using the Dask task runner specified at the flow level. 4. Choosing the Right Task Runner: Depending on the workload and environment, choose a task runner that best suits the execution requirements. Task runners like 
DaskTaskRunner
are suitable for distributed computing environments, allowing different tasks and flows to run across a cluster. Using task runners for parallel execution allows you to optimize performance and resource utilization, rather like having multiple arms to juggle more tasks without breaking a sweat! For more information, you might want to explore the Prefect documentation on task runners.
m
@Marvin can I use futures with nested flows and use wait to do the synchronization at the end ?
m
In Prefect 3.x, you can indeed use futures and synchronization techniques with nested flows, including the 
wait
functionality to synchronize tasks at the end. Here's how it generally works: 1. Prefect Futures Concept: When you trigger a task or a flow run, Prefect returns an object that can be used to inspect the execution or collect results once it's complete. These objects are similar to Python's 
asyncio.Future
but specific to Prefect. 2. Using 
wait
for Synchronization: You can use the 
wait
function to block until all futures have completed. This can be particularly useful when you're executing nested flows in parallel and need to synchronize at some point in your main flow. 3. Example with Nested Flows: Here's a conceptual approach on how to manage futures with nested flows:
Copy code
python
from prefect import flow, task, wait_for, get_run_logger
from prefect.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner)
def child_flow(name):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Running child flow: {name}")
    # Simulating some work
    return name

@flow
def parent_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting parent flow")
    
    # Triggering multiple child flows in parallel
    child_futures = [child_flow.submit(name=f"child_{i}") for i in range(5)]
    
    # Wait for all child flows to complete
    results = wait_for(child_futures)
    
    # Process results
    for result in results:
        <http://logger.info|logger.info>(f"Child flow completed with result: {result}")

if __name__ == "__main__":
    parent_flow()
In this example: - Each 
child_flow
is executed in parallel using the Dask task runner. - 
child_flow.submit()
returns a future, which you can manage. - 
wait_for
is used to ensure all nested flows complete before proceeding in the parent flow. Using this pattern allows you to efficiently handle and synchronize parallel executions of nested flows, leveraging Prefect's orchestration capabilities to keep everything in line, like coordinating a galactic symphony where every note plays at just the right time!
m
@Marvin running your last answer gives me the following error:
Copy code
AttributeError: 'Flow' object has no attribute 'submit'
14:54:55.627 | ERROR   | Flow run 'spirited-kakapo' - Finished in state Failed("Flow run encountered an exception: AttributeError: 'Flow' object has no attribute 'submit'")
Traceback (most recent call last):
This tells me that the decorator 
flow
does not have a 
submit()
method. How can I run these nested flows in parallel?
m
To run nested flows in parallel in Prefect 3.x, you will need to use deployments with the Prefect API or use asynchronous flows with 
asyncio
. The 
submit
method is typically used with tasks, not flows directly. Here's how you can run nested flows concurrently: ### Using Asynchronous Flows You can achieve parallel execution by making the flows asynchronous and using 
await
or 
asyncio.gather
. Here's an updated example:
Copy code
python
from prefect import flow, get_run_logger
import asyncio

@flow
async def child_flow(name):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Running child flow: {name}")
    await asyncio.sleep(1)  # Simulates work delay
    return name

@flow
async def parent_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting parent flow")
    
    # Schedule child flows
    child_tasks = [child_flow(name=f"child_{i}") for i in range(5)]
    
    # Wait for all child flows to complete
    results = await asyncio.gather(*child_tasks)
    
    # Process results
    for result in results:
        <http://logger.info|logger.info>(f"Child flow completed with result: {result}")

if __name__ == "__main__":
    asyncio.run(parent_flow())
### Key Points: 1. Asynchronous Functions: Define both the parent and child flows as asynchronous (
async
), using 
await
for synchronous operation. 2. Concurrency with `asyncio.gather`: Use 
asyncio.gather
to concurrently await the results of multiple child flows. By using asynchronous execution, Prefect allows for running nested subflows in parallel, which facilitates efficient processing of concurrent workflows without necessitating separate task submissions. It's like baking cookies while brewing coffee: everything's ready in record time, with all parts independently finishing their tasks without delay!
m
@Marvin can't I just run a threadpool on the parent flow to run thenested flows in parallel?
m
Yes, you can use a 
ThreadPoolExecutor
to run nested flows in parallel by executing them as separate threads within the parent flow. Here’s how you can implement this approach:
Copy code
python
from prefect import flow, get_run_logger
from concurrent.futures import ThreadPoolExecutor

@flow
def child_flow(name):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Running child flow: {name}")
    # Simulate some work, such as time.sleep(1) or something else intensive
    return name

@flow
def parent_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting parent flow")

    with ThreadPoolExecutor() as executor:
        # Run child flows in parallel
        futures = [executor.submit(child_flow, name=f"child_{i}") for i in range(5)]
        
        # Collect results
        results = [future.result() for future in futures]

    for result in results:
        <http://logger.info|logger.info>(f"Child flow completed with result: {result}")

if __name__ == "__main__":
    parent_flow()
### Explanation: 1. ThreadPoolExecutor: A 
ThreadPoolExecutor
from Python’s 
concurrent.futures
module is used to manage a pool of threads. Each child flow is submitted as a separate thread, enabling parallel execution. 2. Submitting Child Flows: 
executor.submit()
is used to initiate each child flow in a new thread. 3. Collecting Results: Using 
future.result()
blocks until the respective future has completed, allowing you to gather results once all parallel executions are done. This setup allows you to achieve parallel execution of nested flows using threads, making it a straightforward way to utilize concurrent processing without explicitly making your flows asynchronous. It's like getting multiple construction projects done simultaneously with different teams—all within the same deadline!
m
@Marvin write a simple example using prefect 3.x that has an async task that generates a random number that is called 100 times in an async flow.
m
Sure, let’s create an example with an asynchronous task that generates a random number, called 100 times within an asynchronous flow using Prefect 3.x. We'll use Python's 
asyncio
for asynchronous execution.
Copy code
python
import random
import asyncio
from prefect import flow, task, get_run_logger

@task
async def generate_random_number():
    # Simulate some delay
    await asyncio.sleep(0.1)
    number = random.randint(1, 100)
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Generated random number: {number}")
    return number

@flow
async def async_random_number_flow():
    # Create a list of 100 tasks generating random numbers
    tasks = [generate_random_number() for _ in range(100)]
    
    # Run tasks concurrently and wait for all to complete
    results = await asyncio.gather(*tasks)
    
    # Process results
    total = sum(results)
    print(f"Total sum of random numbers: {total}")

if __name__ == "__main__":
    asyncio.run(async_random_number_flow())
### Key Points: - Asynchronous Task: The 
generate_random_number
task uses 
async
and simulates asynchronous behavior with 
await asyncio.sleep(0.1)
. - Concurrency with `asyncio.gather()`: This collects and executes all tasks concurrently, achieving parallelism. - Result Processing: After all tasks complete, results are processed by summing the generated numbers, and this total is printed. This example effectively demonstrates running a task multiple times asynchronously in Prefect 3.x using Python's async capabilities—excellent for when you want to multitask, even if those tasks are playful number generators!
m
@Marvin can I use a task map in an async flow ?
m
Yes, you can use the 
map
method in an async flow in Prefect 3.x to handle tasks concurrently. When using 
map
, it automatically submits a task for each element of the input iterable, enabling you to execute tasks concurrently within an async flow. Here's a simple example demonstrating how to use 
map
with an async flow and async tasks:
Copy code
python
import random
import asyncio
from prefect import flow, task, get_run_logger

@task
async def generate_random_number(index):
    await asyncio.sleep(0.1)  # Simulate some delay
    number = random.randint(1, 100)
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Task {index} generated: {number}")
    return number

@flow
async def async_random_number_flow():
    # Use task.map to execute the task concurrently
    results = await generate_random_number.map(index=range(100))
    
    # Sum the results
    total = sum(results)
    print(f"Total sum of random numbers: {total}")

if __name__ == "__main__":
    asyncio.run(async_random_number_flow())
### Explanation: - 
task.map
Usage: You use 
map
to apply the 
generate_random_number
task on each element in the iterable 
range(100)
. This constructs a list of tasks, each referred by their index. - Concurrency: The mapped tasks run concurrently, leveraging asynchronous execution with 
asyncio
. - Results Handling: The mapped tasks return futures that you can await directly within the async flow to gather the results. This setup allows for a clean, scalable way to run a large number of async tasks concurrently, making your data processing efficient and succinct. It's like having a perfectly synchronized dance troupe, each performer (or task) playing its part seamlessly!
m
@Marvin and what about using an async task with a threadpool task runner in a flow ?
m
You can use a 
ThreadPoolTaskRunner
to run an async task in a Prefect 3.x flow. The 
ThreadPoolTaskRunner
is ideal for running tasks concurrently using threads. However, keep in mind that running async tasks within a thread pool still involves their execution within the context of Python’s threading. Here's a simple example where an async task is executed using a `ThreadPoolTaskRunner`:
Copy code
python
import random
import asyncio
from prefect import flow, task, get_run_logger
from prefect.task_runners import ThreadPoolTaskRunner

@task
async def generate_random_number():
    await asyncio.sleep(0.1)  # Simulate some delay for I/O bound work
    number = random.randint(1, 100)
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Generated random number: {number}")
    return number

@flow(task_runner=ThreadPoolTaskRunner)
def random_number_flow():
    # Create a list of tasks to be executed with a thread pool
    tasks = [generate_random_number.submit() for _ in range(100)]
    
    # Wait for all tasks to complete
    results = [task.result() for task in tasks]
    
    # Calculate the sum of results
    total = sum(results)
    print(f"Total sum of random numbers: {total}")

if __name__ == "__main__":
    random_number_flow()
### Explanation: - `ThreadPoolTaskRunner`: This is set for the flow with 
task_runner=ThreadPoolTaskRunner
to execute tasks using threads. - Async Task: The 
generate_random_number
task is async, allowing it to perform non-blocking operations. Here, it simulates an I/O-bound operation with 
await asyncio.sleep(0.1)
. - Submitting Tasks: The 
submit
method is used to submit each async task to the thread pool. - Awaiting Results: Though running in parallel, tasks use 
.result()
to collect their outputs, blocking until completion. This combination provides an effective way to concurrently execute multiple async tasks within a flow using threads, optimizing for parallel execution without overstretching Python's Global Interpreter Lock (GIL). It's like having multiple painters synchronizing their strokes on a wall—each one contributing swiftly without delay!
m
@Marvin what if I want to save a table artifact of all the generated random numbers at the end, how could this be achieved using async tasks and an async flow?
m
To save the generated random numbers as a table artifact from an async flow using Prefect 3.x, you'll want to make use of Prefect's artifact functionality. Here's how you can achieve this: 1. Generate and Gather Data: Use an async task to generate random numbers and collect these in an async flow. 2. Create a Table Artifact: Once you have the data, you can use 
create_table_artifact
to save the data as an artifact in Prefect, allowing it to be viewed in the Prefect UI. Here's a complete example:
Copy code
python
import random
import asyncio
from prefect import flow, task, get_run_logger
from prefect.artifacts import create_table_artifact

@task
async def generate_random_number():
    await asyncio.sleep(0.1)  # Simulate some delay
    number = random.randint(1, 100)
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Generated random number: {number}")
    return number

@flow
async def async_random_number_flow():
    # Use task.map to execute the task concurrently
    results = await generate_random_number.map(range(100))
    
    # Process results and save as a table artifact
    table_data = [{"Index": i, "Random Number": num} for i, num in enumerate(results)]
    create_table_artifact(
        key="random_numbers_table",
        table=table_data,
        description="Table of generated random numbers"
    )

    # Optionally, log a summary
    total = sum(results)
    print(f"Total sum of random numbers: {total}")

if __name__ == "__main__":
    asyncio.run(async_random_number_flow())
### Explanation: - Async Task and Flow: The task generates a random number asynchronously, and the flow runs these async tasks concurrently via the 
map
method. - Table Artifact: The 
create_table_artifact
function is used to create a table artifact, containing indexed random numbers. This table is identified by a 
key
, and can be viewed in the Prefect UI. - Artifact Display: The generated artifact is stored within Prefect's infrastructure, allowing visualization and documentation of results directly from the Prefect console. Using table artifacts helps immortalize your generated data, ensuring your random masterpieces are both visible and preserved for all to inspect—or admire!
m
@Marvin can you move the logic of creating the table artifact to a task instead? Please update your previous answer accordingly.
m
Certainly! We can encapsulate the logic for creating a table artifact within a separate task. Here's the updated version:
Copy code
python
import random
import asyncio
from prefect import flow, task, get_run_logger
from prefect.artifacts import create_table_artifact

@task
async def generate_random_number():
    await asyncio.sleep(0.1)  # Simulate some delay
    number = random.randint(1, 100)
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Generated random number: {number}")
    return number

@task
def create_random_number_artifact(random_numbers):
    # Prepare table data for the artifact
    table_data = [{"Index": i, "Random Number": num} for i, num in enumerate(random_numbers)]
    
    # Create table artifact
    create_table_artifact(
        key="random_numbers_table",
        table=table_data,
        description="Table of generated random numbers"
    )

@flow
async def async_random_number_flow():
    # Use task.map to execute the task concurrently
    results = await generate_random_number.map(range(100))
    
    # Create the table artifact
    create_random_number_artifact(results)

    # Optionally, log a summary
    total = sum(results)
    print(f"Total sum of random numbers: {total}")

if __name__ == "__main__":
    asyncio.run(async_random_number_flow())
### Explanation: - Separated Artifact Creation Task: The 
create_random_number_artifact
task is a dedicated Prefect task that processes the list of random numbers and creates a table artifact. - Async Execution: The flow remains asynchronous, executing the random number generation tasks concurrently. - Task-Oriented Design: This structure improves modularity and encapsulates the artifact creation logic within a task, making it reusable and more organized. With this arrangement, the artifact creation logic is neatly packaged into its own task, just like a master chef delegating the perfect finishing touch to their pastry team—thereby ensuring everything is presented exquisitely!
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