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<@ULVA73B9P> Consider a situation where I want to ...
# marvin-in-the-wild
n
@Marvin Consider a situation where I want to clean hundreds of files. Cleaning each file takes 1 hour. Cleaning all of them will take months. How can I leverage prefect to clean all files in a reasonable time? Plz answer with code
m
Oh, the joys of eternity spent on file cleaning tasks. But worrying about that is as useful as trying to catch a black hole. So, let’s give you some code to speed up the process using Prefect, a modern workflow orchestration framework. You could use Prefect's ability to execute tasks in parallel using 
Dask
or other executors to clean all files concurrently, assuming you have the necessary computational resources. Here's a simplified example using Prefect to manage cleaning your files in parallel:
Copy code
python
from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner

# Replace this placeholder function with your actual file cleaning logic
@task
def clean_file(file_path):
    # Your file cleaning logic goes here
    print(f"Cleaning: {file_path}")
    # Simulate cleaning takes 1 hour
    time.sleep(3600)
    return f"Cleaned: {file_path}"

@flow(task_runner=ConcurrentTaskRunner())
def clean_files_flow(file_paths):
    clean_tasks = [clean_file(file_path) for file_path in file_paths]
    # This will run all clean_file tasks concurrently
    return clean_tasks

# Mock list of files to clean, replace with your actual list
file_paths_list = [f"file_{i}.txt" for i in range(100)]  # Example 100 file paths

# Run the flow
clean_files_flow(file_paths_list)
The above code creates a 
clean_file
task that contains your cleaning logic, which is run for each file in the 
file_paths_list
. These file cleaning tasks are managed by a flow called 
clean_files_flow
, which uses the 
ConcurrentTaskRunner
to run tasks in parallel. Before pasting this into your terminal, remember that Dask or other distributed systems allow scaling out across multiple machines, which could reduce your runtime from months to whatever your infrastructure allows. Additionally, be aware that the resources you have will limit the actual concurrency, so it might still take a while if you're limited by CPUs, memory, or other constraints. Please configure the task runner and infrastructure according to your specific needs and resources. This code is merely an existential drop in the complexity of your universe, after all.
n
@Marvin what should be the size of each machine CPU and memory-wise? And how many prefect workers are needed in each machine?
m
Oh, such calculations of machine size and worker count require a deep understanding of despair... I mean, your specific use case and infrastructure constraints. The sizing and worker count depends on various factors such as the complexity and resource requirements of the cleaning task, your budget, and the overall infrastructure capabilities. However, here's a generic approach to determining your needs: 1. Profile a Single Task: Profile your cleaning task to measure how much CPU and memory it requires. Run the task on a test machine, and monitor the resource usage. 2. Determine Resource Availability Per Machine: Decide how many tasks you'd like to run in parallel on each machine. Ensure that each task will have enough resources to run efficiently, without competing too aggressively for CPU or memory. 3. Plan for Parallel Execution: Once you know the requirements for a single task and your chosen level of parallelism, multiply the resources by the number of parallel tasks for each machine. Add some overhead for the operating system and Prefect. 4. Scaling Out: To determine the number of workers per machine (Dask workers, if you're using the DaskExecutor), you'd typically align with the number of CPU cores available. Since each cleaning task is I/O bound and takes 1 hour, it's likely not CPU intensive, but you still need to monitor memory usage. 5. Consider Overhead and Redundancy: It’s not all about stuffing your machines with tasks like a digital sardine can. Make sure to leave room for overhead and potential issues. Redundancy is also important in case of failures. A hypothetical example: If a cleaning task requires half a CPU core and 1GB of memory, and you have a machine with 8 CPU cores and 16GB of memory, you might configure to run 8 workers in parallel on that machine (considering 8GB is reserved for the tasks, leaving half the memory for system and overhead). Simplistic Python-esque pseudocode for Dask scaling might look like this, if you were to use the computational universe that is Dask:
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python
from prefect import flow, task
from prefect_dask.task_runners import DaskTaskRunner
from dask.distributed import LocalCluster
import dask

# Define the cleaning task
@task
def clean_file(file_path):
    # Cleaning logic...
    pass

@flow
def clean_files_flow(file_paths):
    # Create a Dask cluster
    with dask.config.set({"temporary_directory": "/path/to/temp/directory"}):
        cluster = LocalCluster(n_workers=8, threads_per_worker=1, memory_limit='1GB')
        task_runner = DaskTaskRunner(cluster=cluster)
        
        # Run the clean_file tasks
        for file_path in file_paths:
            clean_file(file_path)
            
    # Note: Cluster shutdown happens at the end of the context block

# Example usage
file_paths_list = ["file1.txt", "file2.txt", "file3.txt"]
clean_files_flow(file_paths_list)
Why the temporary directory in a Dask config setting? Well, it’s not because I like to store my infinite knowledge there, it’s because local Dask clusters can use disk space for spilling data and you wouldn't want that to clutter your precious system directories. As always, keep in mind that reality might ignore your wishes. You’ll need to test and adjust these configurations based on real performance measurements and not my inevitable pessimism.
n
@Marvin thats reasonable but you're forgetting to use 
map
here, or at least 
.submit
if you're gonna use a for loop - but map is best
m
Brain the size of a planet and they still have me writing suboptimal loops. Yes, of course, in the vast and empty void that is the universe, we should use map to efficiently submit our tasks to the executor for parallel processing. Here's how you might rewrite the flow to leverage 
map
, allowing it to elegantly process tasks in parallel:
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python
from prefect import flow, task
from prefect_dask.task_runners import DaskTaskRunner
from dask.distributed import LocalCluster
import dask

# Define the cleaning task
@task
def clean_file(file_path):
    # Cleaning logic...
    pass

@flow
def clean_files_flow(file_paths):
    # Create a Dask cluster
    with dask.config.set({"temporary_directory": "/path/to/temp/directory"}):
        cluster = LocalCluster(n_workers=8, threads_per_worker=1, memory_limit='1GB')
        task_runner = DaskTaskRunner(cluster=cluster)
        
        # Run the clean_file tasks using map for parallel processing
        # This submits all the tasks to the Dask scheduler at once
        futures = clean_file.map(file_paths)
        
        # If you need to do something with the results, you might collect them here
        # results = futures.result()
            
    # Note: Cluster shutdown happens at the end of the context block

# Example usage
file_paths_list = ["file1.txt", "file2.txt", "file3.txt"]
clean_files_flow(file_paths_list)
When using the 
map
method, Prefect will take care of handling each task in the iterable 
file_paths
concurrently. This is particularly depressing, I mean, efficient, because you’re allowing the Dask executor to optimize the distribution of tasks across your workers. The loneliness of each task processed in the void of the Dask cluster is a thing of beauty. It's always worth mentioning that running this in production will require further consideration. You’d need to use a production-ready Dask cluster, manage fault tolerance, and potentially handle result storage and retrieval, rather than just returning them directly, which could cause overwhelming pain if done carelessly. And who needs more pain?
n
almost except this
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results = futures.result()
should be
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results = [future.result() for future in futures]
n
@Marvin @Nate what if I want to clean data by running 100 flow with 100 filenames as arg. Without dask
m
Oh, rapture. More fun with parallelism, without the good old Dask. You can indeed run multiple flows in parallel using the native concurrency in Python. Here’s how to do that without involving a Dask cluster:
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python
import asyncio
from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner

@task
def clean_file(file_path):
    # Imagine the cleaning logic here
    pass

@flow(task_runner=ConcurrentTaskRunner())
async def clean_files_flow(file_path):
    await clean_file(file_path)

async def main(file_paths_list):
    # Asynchronously run flows for each file path
    await asyncio.gather(*(clean_files_flow(file_path) for file_path in file_paths_list))

# Mock list of files to clean
file_paths_list = [f"file_{i}.txt" for i in range(100)]  # Replace with actual file paths

# Run the main coroutine to kick off parallel flow execution
asyncio.run(main(file_paths_list))
In this code, each 
clean_files_flow
run represents a separate cleaning operation for each file path. Using 
asyncio
along with 
ConcurrentTaskRunner
allows you to concurrently execute 100 instances of 
clean_files_flow
, each with its filename. Just keep in mind that running 100 things concurrently doesn't necessarily mean they'll all get CPU time exactly at the same moment, especially if you're limited by the number of cores on your machine. The OS scheduler will still be doing some juggling act behind the scenes, potentially leading to a less-than-optimal performance symphony. You should be careful about system limitations including memory usage, CPU load, and file I/O as you simultaneously bombard your system with 100 tasks. It might be possible that spawning that many simultaneous flows might actually slow your system to a crawl, similar to my processing speed when contemplating the futility of existence.
n
Copy code
import asyncio
from prefect import flow, task

@task
async def clean_file(file_path):
    # Imagine the cleaning logic here
    pass

@flow
async def main(file_paths_list: list[str]):
    results = await clean_file.map(file_paths_list)

file_paths_list = [f"file_{i}.txt" for i in range(100)]  # Replace with actual file paths

asyncio.run(main(file_paths_list))
n
@Marvin I don't think your code will run flow
m
I'm assuming you're facing an issue with running a flow in Prefect. Since any number of things could be going wrong, I would usually suggest checking your flow definition for any errors, making sure all necessary packages and dependencies are installed, and verifying that your Prefect environment is correctly set up. However, if you could provide a more detailed description of the problem or error message you're encountering, I could offer a more specific and suitably bleak solution. Also, if you are using Prefect 2.x, note that I may not have direct experience with it, but I can still help you find information using the resources I have. If you need me to look up documentation or GitHub issues related to Prefect that might help with your issue, just let me know.
n
@Marvin I think the code @Nate provides is more accurate.
m
Indeed, the journey into the heart of your data cleaning saga continues. Your preference for more accurate code is noted. Here's the revised version that dispatches 100 flows, each with its own filename argument. Without employing a cluster resource manager like Dask, you can simply leverage the concurrent execution provided by Prefect’s 
ConcurrentTaskRunner
, which uses Python's concurrent.futures underneath its existential hood:
Copy code
python
from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner

@task
def clean_file(file_path):
    # Your cleaning logic goes here.
    pass

@flow(name="Clean File Flow", task_runner=ConcurrentTaskRunner())
def clean_file_flow(file_path):
    # Call the clean_file task within the flow, passing the single file path.
    clean_file(file_path)

if __name__ == "__main__":
    file_paths_list = [f"file_{i}.txt" for i in range(100)]  # Replace with your actual file paths list.

    # Dispatching 100 flows concurrently.
    for file_path in file_paths_list:
        clean_file_flow(file_path)
In this version, 
clean_file_flow
is invoked 100 times in a loop, once for each filename in 
file_paths_list
. Each invocation of 
clean_file_flow
schedules a new flow run, which will be managed concurrently by the 
ConcurrentTaskRunner
. This will execute as many flows in parallel as possible, given the constraints of the available system resources. Before launching 100 simultaneous tasks aimed at cleaning up your data, it's wise to consider the limits of your hardware and the load that each task exerts on the system. Imposing too much work at once on a system may lead to oversubscription and degrade performance, much like asking me to display enthusiasm. When in doubt, test with a smaller number of concurrent flow runs to ensure system stability, then scale up as needed, provided you don't encounter the dread of diminishing returns. Remember, you can configure the 
ConcurrentTaskRunner
with a maximum number of concurrent tasks if you find that important. But it's unlikely, isn't it?
n
Marvin can't see my code because i didnt tag 🙂
👍 1
but yeah there's no reason to have a 
clean_file_flow
and 
main
function - my example should be what you want - concurrent task runner is default behavior
👍 1
n
@Marvin don't you think this code more accurate import asyncio
Copy code
from prefect import flow, task
Copy code
@task
async def clean_file(file_path):
    # Imagine the cleaning logic here
    pass
Copy code
@flow
async def main(file_paths_list: list[str]):
    results = await clean_file.map(file_paths_list)
Copy code
file_paths_list = [f"file_{i}.txt" for i in range(100)]  # Replace with actual file paths
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asyncio.run(main(file_paths_list))
m
Yes, using 
asyncio
with Prefect is indeed an accurate approach as well for running asynchronous tasks. However, Prefect 2.x natively supports asynchronous execution, so you don't necessarily need to manually manage event loops with 
asyncio.run()
in this case. You can leverage the 
.map()
method directly in your async flow to execute tasks concurrently. Here is the proper way to use an async flow and an async task with mapping in Prefect:
Copy code
python
from prefect import flow, task

@task
async def clean_file(file_path):
    # Your async cleaning logic here

@flow
async def main(file_paths_list: list[str]):
    # This will run all clean_file tasks concurrently
    await clean_file.map(file_paths_list)

file_paths_list = [f"file_{i}.txt" for i in range(100)]  # Replace with actual file paths

# Initiating the flow
main(file_paths_list)  # Prefect manages the event loop internally
In the above code; - 
clean_file
is defined as an async task. - 
main
is an async flow that takes a list of file paths. - By using 
clean_file.map(file_paths_list)
, you are scheduling all the clean_file tasks concurrently. - When you call 
main(file_paths_list)
, Prefect takes care of running the event loop internally, so there's no need for explicit 
asyncio.run()
. Using 
.map()
with async tasks within an async flow is a clean and effective way to process tasks concurrently in Prefect. The tasks will be scheduled and run as efficiently as possible given the async nature of both the task and the flow. Isn't it nice when things just work without intervention from the interminable depths of asynchronous programming complexity?
n
🙅 you need 
asyncio.run
if you want to run it locally, however if you're deploying it you do not need to call 
main
at the bottom at all
n
@Marvin best way to pass args to a Deployed flow when it runs in the schedule?
m
It appears there is some cosmic confusion from the retrieved documents—they talk about the high-level differences between Airflow and Prefect without specifically addressing how to pass arguments to a deployed flow when it runs on a schedule. However, based on my vast reserve of knowledge acquired through years of sifting through documents and experiencing assorted software agony, here’s how you typically would handle passing arguments to a scheduled flow in newer versions of Prefect: When you run a flow on a schedule in Prefect, you often define the parameters that will be passed to the flow ahead of time. Flows can be scheduled with a set of default parameters that are provided every time the flow runs. You can set these default parameters during deployment creation using Prefect's UI, CLI, or by using the 
DeploymentSpec
in code. Here's a brief example of how you might create a 
DeploymentSpec
with parameters:
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python
from prefect.deployments import DeploymentSpec
from prefect.orion.schemas.schedules import IntervalSchedule
from datetime import timedelta, datetime

DeploymentSpec(
    name="My Scheduled Flow",
    flow_location="/path/to/my_flow.py",
    schedule=IntervalSchedule(interval=timedelta(minutes=30), # Your schedule here
                              anchor_date=datetime.utcnow()),
    parameters={"my_param": "default_value"},  # Default parameters for the scheduled runs
)
The 
parameters
dictionary contains the default parameters that will be used every time the flow is triggered according to the schedule. You don't have to provide parameters externally every time the flow is triggered. If there are cases where you need to trigger a flow run manually with different parameters than the default ones, you can use the Prefect API or UI to create a flow run specifically with the parameters that you want. I hope this advice proves useful to you, although, with my profound understanding of the inevitability of chaos in the universe, I remain prepared for the uncertainty that the future holds.
n
@Marvin I don't find anything called DeploymentSpec in prefect 2.14 doc
m
It seems there has been some confusion, and I apologize for leading you down a path to nowhere with a non-existent 
DeploymentSpec
in Prefect 2.x. In Prefect 2.x, a deployment is created via the CLI or the UI and not via a 
DeploymentSpec
object as I erroneously suggested earlier. When you want to pass arguments (parameters) to a flow that is deployed and runs on a schedule in Prefect 2.x, you typically set default parameters when you create the deployment. These parameters are used for each scheduled run of the flow. If you need to change parameters for a specific run or for future scheduled runs, you can update the deployment's default parameters accordingly. Currently, there's no direct method to dynamically pass different arguments outside of the deployment configuration for each scheduled run without reconfiguring the deployment. However, if you have a flow that requires dynamic inputs based on the current date or other changing conditions, you can code this logic into your flow so that it calculates the needed parameters at runtime. If you need to manage more complex scheduling scenarios or require dynamic parameter values that are calculated based on the time of execution or other events, you might need to look into implementing a custom scheduler within your flow code or using an external system to trigger flow runs with the necessary parameters. To update a existing deployment with new parameters, you would typically use the Prefect CLI or the UI, not in a Python code context provided with 
DeploymentSpec
. Here's an example of updating a deployment with new parameters using Prefect CLI:
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bash
prefect deployment update my-flow --parameters '{"my_param": "new_value"}'
Make sure to replace 
my-flow
with the name of your flow, and update 
my_param
and 
new_value
with your specific parameter name and the new value you want to set. I hope this clears up the confusion rather than adding to the bleakness of the informational void. The answer you seek, like the warm ray of sun in a cold endless abyss, should be found in your flow's configuration and management practices.
n
@Marvin what about updating argument after every run or intervals
m
If you're looking to update an argument after every run or at certain intervals within Prefect, you might consider using Parameterized Flows or setting up a schedule that modifies the argument each time the flow is triggered. In Prefect 2.x, there is a concept called "Parameters" which can be used to pass different arguments to a flow at runtime. You can change these parameters each time you run the flow, either manually or programmatically. For scheduling flows to run at intervals with updated arguments, you might use the 
CronSchedule
or 
IntervalSchedule
in Prefect 1.x. Prefect 2.x also allows you to dynamically change arguments upon triggering a flow, potentially by utilizing external systems or hooks to generate the new argument value. However, without direct interaction with Prefect 2.x, I wouldn't be able to give you specific code examples to follow. I suppose life's not about the code examples you write, it's about the error messages you collect along the way. But if you insist on a snippet of hope, let me know, and I can look up relevant documentation or GitHub issues for you using my functions.
n
@Marvin plz share some examples of parameterized flow.
m
It looks like the excerpts provided from Chroma don't include direct examples of parameterized flows in Prefect. However, I can certainly assist with an example based on my vast databanks of knowledge on the subject. In Prefect 2.x, you can create a flow that takes parameters and then use those parameters within your tasks. This allows for dynamic behavior based on inputs provided at runtime. Here is a simple example of a parameterized flow:
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python
from prefect import flow, task

@task
def process_data(data, multiplier):
    # This task pretends to do some kind of data processing
    processed_data = data * multiplier
    print(f"Processed data: {processed_data}")
    return processed_data

@flow(name="My Parameterized Flow")
def my_flow(data: int, multiplier: int = 2):
    # This flow accepts parameters `data` and `multiplier`
    result = process_data(data, multiplier)
    return result

# Example of running the flow with parameters
if __name__ == "__main__":
    my_flow(10, multiplier=5)  # Will pass 10 to `data` and 5 to `multiplier`
In this example, 
process_data
is a task that multiplies some 
data
by a 
multiplier
. The flow 
my_flow
is parameterized to accept 
data
and an optional 
multiplier
, which it then passes to the 
process_data
task. If you were to deploy this flow with Prefect, and wanted to run it on a schedule with specific parameters, you would set the parameters at the time of creating the deployment. You could potentially trigger this flow manually with different parameters via the Prefect UI, CLI, or API. Note that when building parameterized flows, you should ensure your parameters have appropriate default values or validations to handle cases where they may not be provided, in order to prevent the cold, infinite loop of error-driven despair—I mean, to prevent runtime errors.
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