@Marvin Give me an example of a flow that leverages Prefect 2.20.3 and the "task_runner" parameter in an effort to increase efficiency and performance.

For a Prefect 2.20.3 flow utilizing the

task_runner

parameter to boost efficiency and performance, you can leverage the

DaskTaskRunner

or

RayTaskRunner

. Below is a sample flow using

DaskTaskRunner

.

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def multiply(x, y):
    return x * y

@task
def add(x, y):
    return x + y

@flow(task_runner=DaskTaskRunner)
def math_flow(a, b):
    product = multiply.submit(a, b)
    sum_result = add.submit(a, b)
    return product.result(), sum_result.result()

if __name__ == "__main__":
    result = math_flow(3, 4)
    print(result)

This example creates a simple flow that calculates the product and sum of two numbers concurrently using Dask for distributed task execution. For more details, you can refer to the Prefect documentation on task runners: "https://docs-2.prefect.io/latest/concepts/task-runners/" "https://docs-2.prefect.io/latest/integrations/prefect-dask/task_runners/"

@Marvin, using the same method, provide an example of processing files in a directory.

Here's an example of a Prefect flow using

DaskTaskRunner

to process files in a directory. This setup will allow tasks like reading and processing files to run concurrently, thus improving efficiency.

python
from pathlib import Path
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def read_file(file_path: Path) -> str:
    """Read the content of a file."""
    with file_path.open('r') as file:
        content = file.read()
    return content

@task
def process_content(content: str) -> str:
    """Process the content of the file."""
    # Dummy processing: reverse the content
    return content[::-1]

@task
def save_processed_content(content: str, output_dir: Path, file_name: str) -> Path:
    """Save the processed content to a new file."""
    output_path = output_dir / file_name
    with output_path.open('w') as file:
        file.write(content)
    return output_path

@flow(task_runner=DaskTaskRunner)
def process_files_in_directory(input_dir: Path, output_dir: Path):
    """Flow to process all files in a directory."""
    output_dir.mkdir(exist_ok=True)

    for file_path in input_dir.glob('*.txt'):
        content = read_file.submit(file_path)
        processed_content = process_content.submit(content)
        save_processed_content.submit(processed_content, output_dir, file_path.name)

if __name__ == "__main__":
    input_directory = Path("input_files")
    output_directory = Path("output_files")
    process_files_in_directory(input_directory, output_directory)

In this example: 1. The

read_file

task reads the content of a file from the input directory. 2. The

process_content

task processes (reverses) the content. 3. The

save_processed_content

task saves the processed content to the output directory. 4. The

process_files_in_directory

flow manages the overall process, running the tasks concurrently using

DaskTaskRunner

. Refer to the documentation for more details and further customization options: "https://docs-2.prefect.io/latest/integrations/prefect-dask/" "https://docs-2.prefect.io/latest/concepts/task-runners/"

@Marvin can I use the task_runner parameter in the flow and in a task that the flow calls to create further parallelization?

Yes, you can use the

task_runner

parameter both in the flow and within tasks to achieve further parallelization with Prefect 2.20.3. This technique can improve the performance and efficiency of your workflows by leveraging nested parallel task execution. Here's an example of how to use

task_runner

in both the flow and a nested task:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def read_file(file_path):
    # Simulating reading file content
    return f"Content of {file_path}"

@task
def process_content(content):
    # Simulating content processing
    return content[::-1]

@task
def nested_flow(file_paths):
    # Define a nested flow with its own task runner
    @flow(task_runner=DaskTaskRunner)
    def inner_flow(paths):
        results = []
        for path in paths:
            content = read_file.submit(path)
            processed = process_content.submit(content)
            results.append(processed)
        return results

    # Execute the nested flow
    return inner_flow(file_paths)

@flow(task_runner=DaskTaskRunner)
def main_flow(directory):
    # Assume directory contains files: ['file1.txt', 'file2.txt', 'file3.txt']
    file_paths = [f"{directory}/file1.txt", f"{directory}/file2.txt", f"{directory}/file3.txt"]
    nested_result = nested_flow.submit(file_paths)
    return nested_result

if __name__ == "__main__":
    result = main_flow("/path/to/directory")
    print(result)

In this example: 1. The

main_flow

uses a

DaskTaskRunner

to run tasks concurrently at the top level. 2. A nested flow (

inner_flow

) within the

nested_flow

task also uses its own

DaskTaskRunner

to achieve further parallelization within the nested context. For more detailed explanations, refer to the Prefect documentation: "https://docs-2.prefect.io/latest/concepts/task-runners/" "https://docs-2.prefect.io/latest/integrations/prefect-dask/task_runners/" This approach maximizes efficiency by enabling parallelism not just at the flow level but also at deeper task execution levels.

@Marvin the inner_flow takes a parameter of paths. Is this inherently passed in based on the file_paths parameter on the nested flow?

Yes, in the example, the

inner_flow

is designed to take the

paths

parameter, which is explicitly passed from the

nested_flow

task. The

paths

parameter in

inner_flow

is populated directly by the

file_paths

argument that

nested_flow.submit(file_paths)

receives. Here's a step-by-step breakdown: 1. The

main_flow

defines a list of file paths (

file_paths

). 2. The

nested_flow.submit(file_paths)

call within

main_flow

starts the

nested_flow

task using these file paths. 3. The

inner_flow

nested within

nested_flow

is called with the

file_paths

parameter (passed as

paths

). To clarify this more concretely, here is the revised definition:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def read_file(file_path):
    # Simulating reading file content
    return f"Content of {file_path}"

@task
def process_content(content):
    # Simulating content processing
    return content[::-1]

@task
def nested_flow(file_paths):
    @flow(task_runner=DaskTaskRunner)
    def inner_flow(paths):
        results = []
        for path in paths:
            content = read_file.submit(path)
            processed = process_content.submit(content)
            results.append(processed)
        return results

    # Call inner_flow with the file_paths provided to nested_flow
    return inner_flow(file_paths)

@flow(task_runner=DaskTaskRunner)
def main_flow(directory):
    # Define file paths
    file_paths = [f"{directory}/file1.txt", f"{directory}/file2.txt", f"{directory}/file3.txt"]
    # Pass file_paths to nested_flow
    nested_result = nested_flow.submit(file_paths)
    return nested_result

if __name__ == "__main__":
    # Run the main flow, passing the directory containing input files
    result = main_flow("/path/to/directory")
    print(result.result())

In this code: 1.

main_flow

calls

nested_flow

with

file_paths

. 2.

nested_flow

receives

file_paths

and passes this list to

inner_flow

. 3.

inner_flow

processes each path concurrently using the

DaskTaskRunner

. This design ensures that

inner_flow

correctly receives and processes the

file_paths

as intended.

@Marvin, can the inner_flow use different or more parameters than paths or are the inner_flow parameters restricted to what the nested_flow provides?

The

inner_flow

can indeed use different or additional parameters than what

nested_flow

provides. The parameters of

inner_flow

are not restricted to what

nested_flow

provides; you just need to ensure that all required arguments for

inner_flow

are supplied when it is called. Here's an extended example where

inner_flow

uses additional parameters:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def read_file(file_path):
    return f"Content of {file_path}"

@task
def process_content(content, operation):
    if operation == "reverse":
        return content[::-1]
    elif operation == "uppercase":
        return content.upper()
    else:
        return content

@task
def nested_flow(file_paths, operation):
    @flow(task_runner=DaskTaskRunner)
    def inner_flow(paths, op):
        results = []
        for path in paths:
            content = read_file.submit(path)
            processed = process_content.submit(content, op)
            results.append(processed)
        return results

    # Call inner_flow with the file_paths and operation parameters
    return inner_flow(file_paths, operation)

@flow(task_runner=DaskTaskRunner)
def main_flow(directory, operation):
    file_paths = [f"{directory}/file1.txt", f"{directory}/file2.txt", f"{directory}/file3.txt"]
    nested_result = nested_flow.submit(file_paths, operation)
    return nested_result

if __name__ == "__main__":
    # Run the main flow, passing the directory and a processing operation
    result = main_flow("/path/to/directory", "reverse")
    print(result.result())

In this example: 1. The

main_flow

receives both a

directory

and an

operation

parameter. 2. It calls

nested_flow

with

file_paths

and the

operation

. 3. The

nested_flow

passes these parameters to

inner_flow

. 4. The

inner_flow

uses both

paths

(which maps to

file_paths

) and

op

(which maps to

operation

). This approach demonstrates that you can pass multiple and different parameters to inner flows, not restricted to what the outer nested flow directly provides. You just need to ensure all the necessary parameters are correctly propagated through the task and flow hierarchy. For more details, refer to the Prefect documentation: "https://docs-2.prefect.io/latest/concepts/task-runners/" "https://docs-2.prefect.io/latest/integrations/prefect-dask/task_runners/"

@Marvin to the quantity and order of parameters in nested_flow have to match the inner_flow?

The quantity and order of parameters in the call to

inner_flow

within

nested_flow

need to match what

inner_flow

expects. However,

nested_flow

itself can receive and handle additional parameters as needed, as long as it correctly maps those parameters to the call of

inner_flow

. Here's an extended example to demonstrate how you can manage parameters in nested flows:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def read_file(file_path):
    # Simulating a file read
    return f"Content of {file_path}"

@task
def process_content(content, operation):
    # Simulating content processing based on operation type
    if operation == "reverse":
        return content[::-1]
    elif operation == "uppercase":
        return content.upper()
    else:
        return content

@task
def nested_flow(file_paths, operation, additional_flag):
    # Define a nested flow with its own task runner and parameters
    @flow(task_runner=DaskTaskRunner)
    def inner_flow(paths, op, flag):
        results = []
        for path in paths:
            content = read_file.submit(path)
            processed = process_content.submit(content, op) if flag else content
            results.append(processed)
        return results

    # Call inner_flow with the required parameters
    return inner_flow(file_paths, operation, additional_flag)

@flow(task_runner=DaskTaskRunner)
def main_flow(directory, operation, additional_flag):
    # List files in the directory (simulated here)
    file_paths = [f"{directory}/file1.txt", f"{directory}/file2.txt", f"{directory}/file3.txt"]
    # Call nested_flow with all needed parameters
    nested_result = nested_flow.submit(file_paths, operation, additional_flag)
    return nested_result

if __name__ == "__main__":
    # Run the main flow with directory, operation type, and additional flag
    result = main_flow("/path/to/directory", "reverse", True)
    # Print results
    print(result.result())

In this example: 1.

main_flow

receives

directory

,

operation

, and

additional_flag

parameters. 2. It lists the files and then calls

nested_flow

with all the received parameters. 3.

nested_flow

accepts

file_paths

,

operation

, and

additional_flag

, passing them to

inner_flow

. 4.

inner_flow

processes the files concurrently, considering the

operation

and

flag

. This structure keeps

nested_flow

flexible for adding or modifying parameters without changing

inner_flow

, provided you map the parameters correctly. So, while `inner_flow`'s parameter order and quantity need to match when it's called,

nested_flow

can manage and transform additional parameters as needed to fit that call. This approach provides flexibility in managing more complex flows and parameter passing.