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<@ULVA73B9P> if i am using the serv method. how can i better eliminate late flow runs.
# ask-marvin
s
@Marvin if i am using the serv method. how can i better eliminate late flow runs.
m
To assist you better with the 
serv
method in Prefect and how to eliminate late flow runs, I need to know which version of Prefect you are using: Prefect 2.x or 3.x. Could you please specify the version?
s
currently using prefect 2x @Marvin
m
In Prefect 2.x, when dealing with late flow runs and using the 
serve
method, you can manage them by rescheduling or deleting them based on certain conditions. Here's a general approach on how you can handle late flow runs: 1. Reschedule Late Flow Runs: You can use a function like 
reschedule_late_flow_runs
to manage late runs. This function allows you to reschedule late flows by deleting the current late runs and optionally creating new ones from the same deployment. 2. Example Function: - You may define a function that takes in 
deployment_name
, 
delay
, 
most_recent_n
, and some other optional parameters. It filters flow runs in a "Late" state, optionally deletes them, and reschedules the most recent ones. - Here's a rough outline of what such a function could look like (based on available insights):
Copy code
python
     def reschedule_late_flow_runs(deployment_name: str, delay: timedelta, most_recent_n: int, delete_remaining: bool = True):
         async with get_client() as client:
             flow_runs = await client.read_flow_runs(
                 flow_run_filter=FlowRunFilter(
                     state=dict(name=dict(any_=["Late"])),
                     expected_start_time=dict(
                         before_=datetime.now(timezone.utc)
                     ),
                 ),
                 deployment_filter=DeploymentFilter(
                     name={'like_': deployment_name}
                 ),
                 sort=FlowRunSort.START_TIME_DESC,
                 limit=most_recent_n if not delete_remaining else None
             )
             
             for i, run in enumerate(flow_runs):
                 await client.delete_flow_run(flow_run_id=run.id)
                 if i < most_recent_n:
                     new_run = await client.create_flow_run_from_deployment(
                         deployment_id=run.deployment_id
                     )
                     # Adjust new run's start time based on `delay` here
3. Explore Further: Consider exploring UI features to pause/unpause schedules or utilize Prefect's API for detailed customizations. For more detailed instructions with code examples, you might want to check the Prefect 2.x documentation on handling late flow runs or learn more about deployments. If you want more tailored guidance, feel free to provide more specifics about your setup or any particular constraints you're facing.
s
@Marvin I am trying to have fewer late runs and i do somethng to my local server to help with this?
m
To reduce the number of late flow runs in your local Prefect setup, you can consider several strategies for optimizing the execution environment and configuration. Here are some suggestions: 1. Optimize Scheduling: - Review your flow schedule and ensure that it aligns with the resource availability on your local server. Avoid overloading your server during peak times which could cause delays. - Use the 
interval
or 
cron
parameters wisely when creating deployments to better fit your server's capacity. 2. Resource Management: - Ensure that your local server has enough system resources (CPU, memory, etc.) to handle the scheduled flows efficiently. Monitor resource usage to identify bottlenecks. - Consider running Prefect on a more capable machine or distributing the load across multiple machines if feasible. 3. Increase Timeout Settings: - Sometimes flows are marked late due to very strict timeout settings. Review these settings and increase them if necessary to allow for temporary slowdowns that do not impact overall performance critically. 4. Improve Flow Efficiency: - Review the design of your flows to ensure they are optimized for performance. Look for any tasks that are taking longer than expected and see if there are any improvements you can make. - Implement parallel execution for tasks within a flow where possible. 5. Upgrade Infrastructure: - If your local environment is consistently unable to keep up with the demand, consider moving your Prefect infrastructure to the cloud or to a more powerful environment which can provide the necessary scalability. 6. Local Server Settings: - Use scheduling solutions like Prefect Orion or process managers to ensure Prefect's orchestration processes run smoothly. By addressing these factors, you can help minimize late flow runs in your local environment. If you need more specific tailoring or face challenges with any of these strategies, feel free to share more details!
s
@Marvin does this work with the serv method PREFECT_AGENT_PREFETCH_SECONDS
m
Yes, the 
PREFECT_AGENT_PREFETCH_SECONDS
setting can be used to work with deployments executed via the 
serve
method. This setting allows you to configure the agent to begin submitting flow runs before they are scheduled to start. By default, this is set to 10 seconds. Here's how it works: - If your infrastructure requires more time to start (for example, starting up containers or virtual machines), you can increase the prefetch time. This means the agent will begin preparing for the flow run earlier, helping it start at the scheduled time. - You can set this option either via the command line option 
--prefetch-seconds
or by setting the 
PREFECT_AGENT_PREFETCH_SECONDS
environment variable on your server. Configuring this appropriately can help ensure that flows served with the 
serve
method start promptly, reducing the chances of late runs. For more details, you can refer to the Prefect 2.x documentation on agents and configuration. If you have further questions or need additional assistance, feel free to ask!
s
how can i append something to the random flow name @Marvin?
m
To customize or append to the flow run name in Prefect, you can define a function that generates the flow run name dynamically. Here’s how you can do it: 1. Define a Function for Naming: Create a function that returns a string as the desired flow run name. 2. Use the 
flow_run_name
Parameter: Pass this function to your flow using the 
flow_run_name
parameter. 3. Example: Here's an example on how to implement this customization:
Copy code
python
   import datetime
   from prefect import flow
   from prefect.runtime import flow_run

   def generate_flow_run_name():
       # Access any parameters or runtime information if needed
       flow_name = flow_run.flow_name
       # Example: append something like a timestamp or custom message
       return f"{flow_name}-{datetime.datetime.now().strftime('%Y%m%d-%H%M%S')}"

   @flow(flow_run_name=generate_flow_run_name)
   def my_flow():
       pass

   if __name__ == "__main__":
       my_flow()
In this example, each flow run name will include a timestamp, which helps in uniquely identifying each run. Using this method, you can easily append or customize the flow run names based on your requirements. For more information, you can refer to the Prefect 2.x documentation on flows. Let me know if you need further clarification!
s
i like the auto generated flow run names. i just want to add some stuff to them @Marvin
m
If you want to keep the automatically generated flow run names and simply append or modify them, you can achieve this by accessing the randomly generated name and appending your custom text. Here's an approach on how you can do it: 1. Access Runtime Information: Use the Prefect runtime context to get the current flow run name. 2. Modify the Name: Append or modify the flow run name as desired. Here’s an example implementation:
Copy code
python
import datetime
from prefect import flow
from prefect.runtime import flow_run

def modify_generated_flow_name():
    # Access the auto-generated flow run name
    auto_generated_name = flow_run.name
    # Append your custom modifications
    modified_name = f"{auto_generated_name}-custom-suffix-{datetime.datetime.now().strftime('%Y%m%d-%H%M')}"
    return modified_name

@flow(flow_run_name=modify_generated_flow_name)
def my_flow():
    # Some operations of your flow
    pass

if __name__ == "__main__":
    my_flow()
This setup will take the automatically generated flow run name and append a custom suffix (like a timestamp or any other string) to it. This way, you can maintain the ease of using the Prefect-generated names while adding your own custom information to them. Let me know if you need any more help!
s
can you give the decorator function parameters? @Marvin
m
In Prefect 2.x, the 
@flow
decorator can be customized by passing various parameters, of which 
flow_run_name
is directly relevant for specifying or modifying the flow run name. Here's how you can use the decorator, including specifying a custom flow run name function:
Copy code
python
from prefect import flow

def generate_flow_run_name():
    # Here, you can access run-specific parameters or append additional information
    return "custom-run-name"

@flow(name="My Sample Flow", flow_run_name=generate_flow_run_name)
def my_flow(param1: str, param2: int = 42):
    print(f"Running with {param1} and {param2}")

if __name__ == "__main__":
    my_flow("example")
### Parameters: 1. 
name
: This defines the name of the flow itself, useful for high-level organization and visibility in the Prefect UI. 2. 
flow_run_name
: A callable or static string that sets the name for each specific flow run instance. It can dynamically generate or modify flow run names. 3. 
version
: (Optional) Specify the version of the flow, useful for version control in deployment scenarios. 4. 
task_runner
: (Optional) Specify the task runner (e.g., concurrent, dask) for executing tasks within this flow. 5. 
validate_parameters
: (Optional) Set to True to enable automatic validation of input parameter types. Using these parameters together, you can precisely control both the behavior and identification of flow runs in Prefect, with 
flow_run_name
giving you the ability to programmatically adjust each run's name to include useful metadata or timestamps along with Prefect's auto-generated values.
s
Any ideas why my flow runs get held up when using the serv method and never finish and pretty much take down service for any future flow runs @Marvin
m
There can be several reasons why your flow runs get held up or stuck when using the 
serve
method in Prefect. Here are a few potential issues and solutions: 1. Concurrency Limits: As indicated in the GitHub issue #8566, tasks might remain in a "running" state even after cancellation if concurrency slots are not properly released. Check if you have any concurrency limits set and whether they are being respected. 2. Resource Allocation: Ensure that your server has enough resources (CPU, memory) to handle the flow runs you're executing. Lack of resources might cause processes to wait indefinitely. 3. Prefect Infrastructure Issues: Sometimes, the infrastructure setup might cause delays. Verify that your infrastructure is correctly configured to support flow execution, including networking, file paths, and dependencies. 4. Agent Configuration: The agents might be misconfigured or not properly linked to the Prefect server. Make sure your agent is active and properly polling the server for tasks. 5. Flow Logic: Review your flow code for any logic that might result in infinite loops or tasks that are waiting indefinitely for external resources or conditions. 6. Error Handling: Implement error handlers in your tasks or flows to catch exceptions and log details, helping you identify where a flow might be getting stuck. 7. Logs and Monitoring: Check the server and Prefect UI logs for any warnings or errors. This can give insights into what's going wrong. If these steps don't resolve the issue, try reaching out directly to the Prefect community for more tailored assistance, as they might have encountered similar scenarios. If you can share more specific error messages or logs, that might also help pinpoint the problem further.
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