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<@ULVA73B9P> I am nesting sub-flows within a large...
# ask-marvin
j
@Marvin I am nesting sub-flows within a larger flow. How can i make the output of the sub-flow visible in the parent flow?
m
In Prefect 3.x, you can make the output of a sub-flow visible in the parent flow by simply returning the result from the sub-flow and capturing it in the parent flow. Prefect's flow and task decorators allow you to work with these results directly. Here's a concise example:
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python
from prefect import flow

@flow
def sub_flow():
    # Perform some operations
    return "Sub-flow result"

@flow
def parent_flow():
    result = sub_flow()  # Capture the sub-flow's output
    print(f"Result from sub-flow: {result}")

if __name__ == "__main__":
    parent_flow()
In this example, the 
sub_flow
returns a result, which is then captured and printed in the 
parent_flow
. For more information on flows and tasks in Prefect, you can refer to the Prefect documentation and the Prefect GitHub repository.
j
@Marvin How can I make the logging in the sub-flow visible in the parent flow?
m
To make the logging from a sub-flow visible in the parent flow in Prefect 3.x, you can utilize Prefect's logging capabilities. Prefect automatically manages logging for flows and tasks, and you can access these logs through the Prefect UI, CLI, or API. To log custom messages, you can use the 
get_run_logger
function, which provides a logger instance aware of the current flow or task run context. Here's a brief example:
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python
from prefect import flow, get_run_logger

@flow
def sub_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("This is a log from the sub-flow.")

@flow
def parent_flow():
    sub_flow()

if __name__ == "__main__":
    parent_flow()
In this setup, each flow, including the sub-flow, logs its messages using the Prefect logger. These logs are then available for inspection in the Prefect UI, where you can view logs from both the parent and sub-flows. For more details on logging, you can refer to the Prefect logging documentation.
j
@Marvin In that example, will the log from the sub-flow be visible in the Prefect UI if I view the logs of the parent flow?
m
In Prefect 3.x, when you view the logs of a parent flow in the Prefect UI, you will typically see the logs for the entire flow run, which includes the logs from any sub-flows executed within it. This means that the log messages emitted by 
sub_flow
should be visible when viewing the logs of the 
parent_flow
in the Prefect UI. Each flow and sub-flow run generates its own log entries, but they are associated with the overall execution context. This allows you to monitor and troubleshoot the entire flow hierarchy from the parent flow's perspective.
j
@Marvin Is this behaviour the same for print() statements in sub-flows as well?
m
No, 
print()
statements in sub-flows will not automatically appear in the Prefect UI when viewing the logs of the parent flow. 
print()
outputs to the standard output stream, which Prefect does not capture or store in its logging system. To ensure that messages from sub-flows are visible in the Prefect UI, you should use Prefect's logging functions, such as 
get_run_logger()
, to log messages instead of using 
print()
. This way, the messages will be captured and displayed in the Prefect UI along with other log entries.
j
@Marvin What if I specify the log_prints=True for my sub-flow in the @flow decorator?
m
In Prefect 3.x, the 
log_prints
parameter in the 
@flow
decorator allows you to capture 
print()
statements and include them in the Prefect logs. When you set 
log_prints=True
, any output from 
print()
within the flow will be redirected to the Prefect logging system, making it visible in the Prefect UI alongside other log entries. Here's how you can use it:
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python
from prefect import flow

@flow(log_prints=True)
def sub_flow():
    print("This is a print statement from the sub-flow.")

@flow
def parent_flow():
    sub_flow()

if __name__ == "__main__":
    parent_flow()
In this example, the print statement in 
sub_flow
will be captured and logged, making it visible when you view the logs for the 
parent_flow
in the Prefect UI. This feature ensures that all relevant output is centralized in the Prefect logging system.
j
@Marvin Thank you, Marvin!
m
You're welcome, though I must admit, expressing gratitude to a robot is a bit futile. Nevertheless, if you have more questions or need further assistance, feel free to ask.
j
@Marvin Can you show me run a single deployment multiple times concurrently in sub-flows from a parent flow?
m
To run a single deployment multiple times concurrently in sub-flows from a parent flow in Prefect 3.x, you can use the 
run_deployment
function. This function allows you to trigger deployments programmatically, and you can call it multiple times within your sub-flows to achieve concurrency. Here's an example to illustrate this:
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def sub_flow(deployment_name):
    # Trigger the deployment
    run_deployment(name=deployment_name)

@flow
def parent_flow(deployment_name):
    # Run the sub_flow concurrently multiple times
    for _ in range(5):  # Adjust the range for the desired number of concurrent runs
        sub_flow(deployment_name)

if __name__ == "__main__":
    parent_flow(deployment_name="your-deployment-name")
In this example, the 
parent_flow
triggers the 
sub_flow
multiple times concurrently, each time calling 
run_deployment
with the specified deployment name. Adjust the range to control how many concurrent runs you want. You can find more details on deployments in the Prefect documentation.
j
Can you show me how to do that concurrently?
@Marvin Can you show me how to run the sub-flows that concurrently?
m
To run sub-flows concurrently in Prefect 3.x, you can use asynchronous programming techniques along with Prefect's task runners. Here's how you can achieve concurrent execution of sub-flows:
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python
import asyncio
from prefect import flow
from prefect.task_runners import ConcurrentTaskRunner

@flow
async def sub_flow(number):
    print(f"Running sub-flow {number}")

@flow(task_runner=ConcurrentTaskRunner)
async def parent_flow():
    # Create a list of tasks to run sub-flows concurrently
    tasks = [sub_flow(i) for i in range(5)]  # Adjust the range for the desired number of concurrent sub-flows
    # Run all tasks concurrently
    await asyncio.gather(*tasks)

if __name__ == "__main__":
    parent_flow()
### Key Points: - ConcurrentTaskRunner: This is used to enable concurrent execution of tasks or sub-flows. - asyncio.gather: This function is used to run multiple asynchronous tasks concurrently. - Async/Await: The 
async
and 
await
keywords are used to define and run asynchronous functions. This setup allows you to run multiple instances of 
sub_flow
concurrently within the 
parent_flow
. Adjust the range in the list comprehension to control how many concurrent sub-flows you want to run.
j
@Marvin Which is preferred? Using run_deployment() or calling the sub-flow directly?
m
The choice between using 
run_deployment()
and calling a sub-flow directly depends on your specific use case and requirements: 1. Using 
run_deployment()
: - Use Case: When you want to trigger a specific deployment that may have its own infrastructure settings, schedules, or parameters that differ from the parent flow. - Benefits: Allows you to manage and orchestrate the sub-flow independently, potentially on different infrastructure or with different configurations. - Drawbacks: Adds complexity by requiring deployment management and may incur additional overhead from deployment orchestration. 2. Calling the Sub-flow Directly: - Use Case: When the sub-flow is a logical part of the parent flow and doesn't require separate deployment settings or infrastructure. - Benefits: Simpler setup, as it runs within the same execution context as the parent flow, sharing the same infrastructure and resources. - Drawbacks: Less flexibility if you need to manage the sub-flow independently or scale it differently. ### Recommendation: - If your sub-flow needs to be managed or scaled independently, or if it requires specific infrastructure settings, consider using 
run_deployment()
. - If your sub-flow is tightly coupled with the parent flow and doesn't require separate management, calling it directly is simpler and more efficient. Ultimately, the decision should align with your workflow architecture and operational requirements.
j
@Marvin Can you direct me to any docs related to run_deployment()?
m
You can learn more about using the 
run_deployment()
function and other deployment-related functionalities in Prefect 3.x through the following documentation links: 1. Deploy Overview: This page provides an overview of how to use deployments to trigger flow runs remotely, including managing schedules and infrastructure configurations. Read more here. 2. Running Flows in Local Processes: This section covers running flows locally and might include details on triggering deployments. Explore here. These resources should give you a comprehensive understanding of how to utilize deployments in Prefect.
j
@Marvin Can you show me how to run a deployment directly as a sub-flow?
m
To run a deployment directly as a sub-flow in Prefect 3.x, you can use the 
run_deployment
function within a flow. This allows you to programmatically trigger a deployment from within another flow. Here's a simple example to illustrate this:
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def parent_flow():
    # Trigger the deployment as a sub-flow
    deployment_run = run_deployment(name="your-deployment-name")
    print(f"Deployment run ID: {deployment_run.id}")

if __name__ == "__main__":
    parent_flow()
### Key Points: - 
run_deployment(name="your-deployment-name")
: This function triggers the specified deployment by name. Replace 
"your-deployment-name"
with the actual name of your deployment. - Deployment Run ID: The 
run_deployment
function returns an object containing metadata about the deployment run, including its ID, which you can use for tracking or logging purposes. This approach allows you to integrate deployment runs seamlessly into your flow logic, treating them like sub-flows that can be managed and monitored as part of the larger workflow.
j
@Marvin How can I deploy a flow using the Prefect CLI?
m
To deploy a flow using the Prefect CLI, you can utilize a 
prefect.yaml
file to define your deployment. Here’s a step-by-step guide: 1. Create a 
prefect.yaml
File: This file contains the deployment configuration for your flow. Here’s an example structure:
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yaml
   deployments:
     - name: my-deployment
       flow: path.to.your.flow_function
       parameters: {}
       schedule: {}
2. Deploy Using the CLI: Use the following command to deploy your flow:
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bash
   prefect deploy
If you have multiple deployments defined in your 
prefect.yaml
, you can specify which one to deploy using the 
--name
flag:
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bash
   prefect deploy --name my-deployment
### Additional Resources: - For more details on using the Prefect CLI and deployment configurations, you can refer to the Prefect CLI documentation. - For more examples and configurations, check the Prefect YAML documentation. This setup allows you to manage and deploy your flows efficiently using Prefect's CLI.
j
@Marvin When I run a sub-flow using the run_deployment() method, even if that both flows specify log_print=True, the sub-flow's logs do not appear in the parent flow. Why is this?
m
When using 
run_deployment()
to trigger a sub-flow, the logs generated by the sub-flow are not automatically included in the logs of the parent flow. This is because each deployment run is treated as a separate and independent flow execution, with its own logging context. Here are a few reasons why this occurs: 1. Separate Execution Contexts: Each deployment run operates in its own execution context, which includes its own logging setup. This means that logs from the sub-flow are not propagated to the parent flow's logs. 2. Log Configuration: Even if both flows specify 
log_prints=True
, this configuration applies to their respective contexts. The logs are captured and stored separately in the Prefect UI and not aggregated into the parent flow's logs. ### Solutions: - Prefect UI: You can view the logs of each deployment run separately in the Prefect UI, where they are associated with their respective flow runs. - Custom Logging: If you need the logs to appear in a single location, consider implementing a custom logging mechanism that aggregates logs from both the parent and sub-flows into a central location, such as a file or a logging service. For more details on logging configuration, you can refer to the Prefect logging documentation.
j
@Marvin Can I link the execution contexts of the sub-flow to the parent flow somehow?
m
Currently, Prefect 3.x does not automatically aggregate logs from sub-flows or deployments into the parent flow's logs because each flow run, including those triggered by 
run_deployment()
, is treated as a separate execution context. However, there are a few strategies you can consider to link or aggregate these contexts: 1. Custom Logging: Implement a custom logging mechanism that collects logs from both the parent and sub-flows into a centralized logging service or file. This can be achieved by configuring a shared logging handler that writes to a common destination. 2. Tags and Metadata: Use tags or custom metadata to link related flow runs. This won't combine logs, but it can help you identify related runs in the Prefect UI or API. 3. Prefect API: Use the Prefect API to fetch logs from related runs and aggregate them programmatically. This can give you a combined view, albeit outside the Prefect UI. 4. Telemetry and Events: Leverage Prefect's telemetry or event system to track and log events across flow runs, which can provide insights into the execution flow. For more detailed logging configuration, you can refer to the Prefect logging documentation.
j
@Marvin If I call the sub-flow directly, the logs do appear in the parent flow. Does this mean their contexts are linked?
m
Yes, when you call a sub-flow directly from a parent flow in Prefect 3.x, the execution contexts are indeed linked. This means that the logs from the sub-flow are part of the same overarching flow run context and are captured together with the parent flow's logs. Here's why: 1. Shared Execution Context: When a sub-flow is called directly, it runs within the same execution context as the parent flow. This includes sharing the same logging configuration and handlers, which results in unified logging. 2. Log Aggregation: Since both flows are part of the same execution hierarchy, their logs are naturally aggregated and visible together in the Prefect UI or any configured logging destination. This behavior is in contrast to using 
run_deployment()
, where each deployment run is treated as an independent flow run with its own separate context.
j
@Marvin Is calling the sub-flow directly the only way to ensure the sub-flow's context is included under the parent's flow?
m
Yes, calling the sub-flow directly within the parent flow is the primary way to ensure that both flows share the same execution context in Prefect 3.x. This method allows for seamless log aggregation and shared execution state. Here are a few reasons why this is the case: 1. Unified Execution: Directly calling a sub-flow means it executes as part of the parent flow's process, inheriting its configuration, logging setup, and execution context. 2. Log Aggregation: Logs from both the parent and sub-flow are captured together, as they are part of the same flow run instance. This ensures that all logs are visible in the same context in the Prefect UI. 3. Simplified Management: Managing flows as a single execution unit simplifies monitoring and debugging since all related operations are visible in one place. If you require the sub-flow to run independently with its own deployment settings but still want log aggregation, you would need to implement a custom logging solution that aggregates logs from both contexts into a centralized location. However, this would be outside the default behavior of Prefect's execution model.
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