@Marvin how to download the prefect flow run log in prefect 2.14 and old prefect

To retrieve and download the flow run logs in Prefect, you can follow the instructions from the Prefect documentation and community resources: For Prefect 2.x (current as of version 2.14): - According to the Release Notes, as of version 2.7.5, you can use the CLI command

prefect flow-run logs <flow run UUID>

to get the logs for a flow run. For Prefect 1.x (the older version of Prefect): - Unfortunately, the search did not yield direct information on how to download flow run logs in Prefect 1.x specifically, but you might typically retrieve logs from the Prefect UI or use CLI commands in a similar fashion to Prefect 2.x. Please bear in mind that Prefect's interface and features can change over time, and it is advisable to check the official documentation or reach out to the Prefect community for the most current and detailed instructions. If you require more in-depth assistance with the older version of Prefect or cannot find the commands you need, you might consider searching the Prefect documentation directly or consulting with the Prefect community forums or GitHub issues.

@Marvin how to schedule flows using

client.create_flow_run_from_deployment

Scheduling flows can be accomplished through deployments in Prefect. The

client.create_flow_run_from_deployment

might not be the exact method you're looking for regarding scheduling, as this function is typically used to create a flow run from a deployment immediately. To schedule flows to run at specific times, you should use deployments with schedules defined. Here is a more relevant approach: 1. Define a deployment with a schedule. 2. Use the

run_deployment

function to initiate the schedule. From the Prefect documentation on deployments, here's an example of how to execute a deployment without blocking and link the scheduled flow run to a calling flow or task as a subflow:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def my_flow():
    # The scheduled flow run will not be linked to this flow as a subflow.
    run_deployment(name="my_other_flow/my_deployment_name", as_subflow=False)

def main():
    flow_run = run_deployment(name="my_flow_name/my_deployment_name")
    flow_run_id = flow_run.id
    # Use flow_run_id to retrieve flow run details later

In this example,

run_deployment

is called with

as_subflow=False

so the scheduled flow run is not linked to the calling flow, and you can retrieve information about the run after calling

run_deployment

by using the returned

FlowRun

object. For defining the schedule within a deployment, check Deployments Block-based Documentation for examples on how to set up a deployment with an active schedule. Please note that while I could fetch instructions for using

run_deployment

, you may need to refer to the documentation for specific examples on scheduling deployments. If you're using the Python client and referring to a method like

client.create_flow_run_from_deployment

, it's important to consult the client's API documentation or source code for the right usage pattern and parameters.

@Marvin how to pass value for scheduled_time in run_deploy example? which format and what are all options available.

When scheduling deployments in Prefect and passing values for

scheduled_time

, you have multiple options for defining the schedule using cron, interval, or RRule formats. Here's a summary of how to use each format in a Python deployment creation file: 1. Cron: - Use a

CronSchedule

argument to define the schedule when deploying a flow with

Deployment.build_from_flow

or editing the deployment definition YAML file directly. - The cron format allows you to schedule tasks using traditional cron syntax, specifying the minute, hour, day of the month, month, day of the week, and optional year. 2. Interval: - Use an

IntervalSchedule

to specify a simple interval at which the flow should run. - The interval is specified in a Python timedelta format (e.g.,

timedelta(hours=1)

for an hourly schedule). 3. RRule (Recurrence Rule): - Use an

RRuleSchedule

if you need more complex recurrence patterns. - RRule follows the iCalendar RFC 5545 specification and provides significant versatility but has a character limit of 6500 characters for the

rrulestr

. - Note that

COUNT

is not supported in RRule, and you should use

UNTIL

or the deployments runs API endpoint to schedule a fixed number of flow runs. Here is how you might specify these in a deployment definition:

python
from datetime import timedelta
from prefect import flow
from prefect.deployments import Deployment
from prefect.orion.schemas.schedules import CronSchedule, IntervalSchedule, RRuleSchedule

@flow
def my_flow():
    pass

# Deployment with cron schedule:
Deployment.build_from_flow(
    flow=my_flow,
    name='my_cron_schedule',
    schedule=CronSchedule(cron="0 8 * * *")  # Daily at 8:00 AM
)

# Deployment with interval schedule:
Deployment.build_from_flow(
    flow=my_flow,
    name='my_interval_schedule',
    schedule=IntervalSchedule(interval=timedelta(hours=24))  # Every 24 hours
)

# Deployment with RRule schedule:
Deployment.build_from_flow(
    flow=my_flow,
    name='my_rrule_schedule',
    schedule=RRuleSchedule(rrule="FREQ=DAILY;BYHOUR=8;BYMINUTE=0")  # Daily at 8:00 AM
)

You can also specify the time zone for your schedule to ensure that the scheduled flow runs according to the local time, as found in the Prefect blog post. For more detailed examples and options, please refer to the Prefect schedules documentation.

@Marvin in run_deployement how to pass current time plus 5 seconds? scheduled_time Optional[datetime] The time to schedule the flow run for, defaults to scheduling the flow run to start now.

It seems that the code example provided doesn't directly correspond to the use of

scheduled_time

with

run_deployment

. However, I can guide you on how to calculate a scheduled time that is 5 seconds from the current time, as the

scheduled_time

argument is expecting a datetime object. Here's a simple way to do it:

python
from datetime import datetime, timedelta
from prefect import flow
from prefect.deployments import run_deployment

@flow
def my_flow():
    pass

def main():
    # Calculate the scheduled time as the current time plus 5 seconds
    scheduled_time = datetime.now() + timedelta(seconds=5)
    
    # Use the calculated time as the scheduled_time argument
    flow_run = run_deployment(name="my_flow_name/my_deployment_name", scheduled_time=scheduled_time)
    
if __name__ == "__main__":
    main()

In this example, the

scheduled_time

parameter is set to the current time plus 5 seconds, using the

datetime.now()

function to get the current time and

timedelta(seconds=5)

to create a 5-second offset. Please ensure that the Prefect server's time zone matches your local time zone or use timezone-aware datetime objects to avoid potential scheduling issues related to time zones.

@Marvin it worked. Thanks. would like to know one more thing. if i want to call the same flow 100 times with different parameters in parallel. How to do? i used run_deployment and all 100 flows got triggered but some got completed , some were running , some were crashed/failed, some were pending. due to lack of pods/ worker availability. is there a way to run those 100 runs as batches? How to do?

Running batches of flow runs when there are resource constraints (like limited worker pods available) can require using orchestrated distributed computing systems or adjusting how Prefect manages work queues. Here are a few strategies you can consider to manage resource usage and batch execution: 1. Use Prefect-Managed Work Pools: Starting from Prefect 2.x, Prefect-managed work pools are available in beta. When a flow is deployed with a Prefect-managed work pool, Prefect can manage the execution of your code, handling the available compute resources for you. Prefect Changelog 2. Leverage Distributed Computing: Prefect integrates with distributed computing frameworks like Dask and Ray, which can help efficiently manage and scale out workflow execution across multiple workers. This doesn't directly create batches, but it can help manage workloads in a distributed manner. Blog Post Reference 3. Work Queue Tuning: You can tune Prefect work queues to control how many flow runs can be pulled for execution at once. Adjust concurrency limits based on your available workers to prevent overloading your system. Details on work queue configuration would typically be found in the Prefect documentation, but weren't directly returned from the search. 4. Programmatic Batch Execution: Instead of triggering all 100 flow runs at once, you can write a script that keeps track of active runs and only schedules new runs when resources are available. This would be custom code that you'd have to maintain, as opposed to Prefect's built-in options. Based on the search results, it seems Prefect offers the capability to manage your code execution on distributed computing resources, which should help when running multiple flow runs in parallel. For more precise control over batching and resource management, diving deeper into Prefect’s documentation or reaching out to the Prefect community for best practices on managing execution under resource constraints might be beneficial. If you are aiming for batched processing but can't find a built-in method, you might need to implement a custom solution where you monitor the resource usage and queue up the flow runs until resources are freed up by completed runs. Please note that specific features and best practices for this might evolve, so always check the latest Prefect documentation for up-to-date information and consider asking in the Prefect community for insights on managing parallel executions with resource constraints.

Thank you