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# ask-ai
u
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a
To run two tasks in parallel in Flyte, you can define them within a workflow without specifying dependencies between them. This will allow Flyte to execute them concurrently. Here's an example:
Copy code
from flytekit import task, workflow

@task
def task_a() -> str:
    return "Task A completed"

@task
def task_b() -> str:
    return "Task B completed"

@workflow
def my_workflow() -> (str, str):
    result_a = task_a()
    result_b = task_b()
    return result_a, result_b

if __name__ == "__main__":
    print(my_workflow())
In this example, 
task_a
and 
task_b
will run in parallel because there are no dependencies specified between them. Would you like more detail? Sources: - Discuss Flyte 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 can we call a task from within a task
a
No, calling a task from within another task directly is not supported in Flyte. Instead, you should use dynamic workflows to achieve similar functionality. Would you like more detail? Sources: - Discuss Flyte - GitHub Issue 3734 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 i want 2 parallel flows where first 3 tasks are related and next 3 tasks are related but these 2 flows are separate and should run in parallel
@ancient-wolf-19325 i want 2 parallel flows where first 3 tasks are related and next 3 tasks are related but these 2 flows are separate and should run in parallel
a
To achieve two parallel flows where each flow consists of three related tasks, you can define two separate workflows for each flow and then call these workflows in a main workflow to run them in parallel. Here's an example:
Copy code
from flytekit import task, workflow

@task
def task1() -> str:
    return "Task 1 completed"

@task
def task2() -> str:
    return "Task 2 completed"

@task
def task3() -> str:
    return "Task 3 completed"

@task
def task4() -> str:
    return "Task 4 completed"

@task
def task5() -> str:
    return "Task 5 completed"

@task
def task6() -> str:
    return "Task 6 completed"

@workflow
def flow1() -> (str, str, str):
    result1 = task1()
    result2 = task2()
    result3 = task3()
    return result1, result2, result3

@workflow
def flow2() -> (str, str, str):
    result4 = task4()
    result5 = task5()
    result6 = task6()
    return result4, result5, result6

@workflow
def main_workflow() -> ((str, str, str), (str, str, str)):
    flow1_results = flow1()
    flow2_results = flow2()
    return flow1_results, flow2_results

if __name__ == "__main__":
    print(main_workflow())
In this example: 1. 
flow1
consists of 
task1
, 
task2
, and 
task3
. 2. 
flow2
consists of 
task4
, 
task5
, and 
task6
. 3. The 
main_workflow
calls 
flow1
and 
flow2
to run them in parallel. Would you like more detail? Sources: - Flyte Workflows 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 how to fetch all executions of a workflow
a
To fetch all executions of a workflow in Flyte, you can use the 
list_executions_paginated
method from the FlyteRemote client. Here's an example:
Copy code
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config
from flytekit.models.common import NamedEntityIdentifier

# Initialize FlyteRemote
remote = FlyteRemote(config=Config.auto(), default_project="your_project", default_domain="your_domain")

# Define the workflow identifier
workflow_identifier = NamedEntityIdentifier(project="your_project", domain="your_domain", name="your_workflow_name")

# Fetch all executions of the workflow
executions, _ = remote.client.list_executions_paginated(limit=100, identifier=workflow_identifier)

# Print execution IDs
for execution in executions:
    print(execution.id)
In this example: 1. Replace 
"your_project"
, 
"your_domain"
, and 
"your_workflow_name"
with your actual project, domain, and workflow name. 2. The 
list_executions_paginated
method fetches executions with a specified limit. You can adjust the limit as needed. Would you like more detail? Sources: - Discuss Flyte 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
how to check the value of Taskresult.failed and put a if clause i am getting an error to use bitwise
@ancient-wolf-19325 ow to check the value of Taskresult.failed and put a if clause i am getting an error to use bitwise
a
In Flyte, you can use the 
conditional
construct to check the value of a task result and apply conditional logic. Flyte requires the use of bitwise operators (
&
, 
|
) instead of logical operators (
and
, 
or
) due to Python's PEP-335. Here's an example of how to check if a task result indicates failure and apply conditional logic:
Copy code
from flytekit import task, workflow, conditional

@task
def task_that_may_fail() -> bool:
    # Simulate a task that may fail
    import random
    return random.choice([True, False])  # True indicates success, False indicates failure

@task
def handle_failure() -> str:
    return "Task failed"

@task
def handle_success() -> str:
    return "Task succeeded"

@workflow
def main_workflow() -> str:
    result = task_that_may_fail()
    return (
        conditional("check_task_result")
        .if_(result.is_false())
        .then(handle_failure())
        .else_()
        .then(handle_success())
    )

if __name__ == "__main__":
    print(main_workflow())
In this example: 1. 
task_that_may_fail
simulates a task that may fail by returning a boolean value. 2. 
handle_failure
and 
handle_success
are tasks that handle the failure and success cases, respectively. 3. The 
main_workflow
uses the 
conditional
construct to check if the result of 
task_that_may_fail
is 
False
(indicating failure) and then calls the appropriate task. Would you like more detail? Sources: - Flyte Conditionals 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 specifically want to know about the TaskResult class failed parameter
a
I'm not able to find any data sources to answer your question. Could you provide more details or context about where and how you are encountering the 
TaskResult
class and its 
failed
parameter? If this doesn't help, please reach out to the Flyte support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 run a task after all the parallel map tasks complete.
a
To run a task after all parallel map tasks complete in Flyte, you can use the 
map_task
function to execute tasks in parallel and then follow it with a task that processes the results. Here's an example:
Copy code
from flytekit import task, workflow, map_task
import typing

@task
def parallel_task(x: int) -> int:
    return x * 2

@task
def final_task(results: typing.List[int]) -> int:
    return sum(results)

@workflow
def main_workflow(inputs: typing.List[int]) -> int:
    # Run parallel tasks
    parallel_results = map_task(parallel_task)(a=inputs)
    
    # Run final task after all parallel tasks complete
    final_result = final_task(results=parallel_results)
    return final_result

if __name__ == "__main__":
    inputs = [1, 2, 3, 4, 5]
    print(main_workflow(inputs=inputs))
In this example: 1. 
parallel_task
is the task that will be run in parallel. 2. 
final_task
is the task that will run after all parallel tasks complete. 3. 
main_workflow
uses 
map_task
to run 
parallel_task
in parallel on the input list and then calls 
final_task
with the results of the parallel tasks. Would you like more detail? Sources: - Flyte Nested Parallelization 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 but I do not need the output of maptasks to pass in it
a
If you want to run a task after all parallel map tasks complete but do not need the output of the map tasks, you can use the 
>>
operator to specify dependencies without passing data. Here's an example:
Copy code
from flytekit import task, workflow, map_task

@task
def parallel_task(x: int) -> None:
    print(f"Processing {x}")

@task
def final_task() -> str:
    return "All parallel tasks completed"

@workflow
def main_workflow(inputs: list[int]) -> str:
    # Run parallel tasks
    parallel_results = map_task(parallel_task)(a=inputs)
    
    # Ensure final_task runs after all parallel tasks complete
    final_result = final_task()
    parallel_results >> final_result
    return final_result

if __name__ == "__main__":
    inputs = [1, 2, 3, 4, 5]
    print(main_workflow(inputs=inputs))
In this example: 1. 
parallel_task
is the task that will be run in parallel. 2. 
final_task
is the task that will run after all parallel tasks complete. 3. 
main_workflow
uses 
map_task
to run 
parallel_task
in parallel on the input list and then ensures 
final_task
runs after all parallel tasks complete using the 
>>
operator. Would you like more detail? Sources: - Flyte Workflows 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 what does this mean 
parallel_results >> final_result
a
The expression 
parallel_results >> final_result
in Flyte is used to specify that 
final_result
should run after 
parallel_results
have completed. This is a way to define dependencies between tasks or workflows without passing data between them. However, it seems there was a misunderstanding in my previous response. The 
>>
operator is not directly applicable to the results of 
map_task
. Instead, you should use the 
with_overrides
method to ensure that the final task runs after the parallel tasks complete. Here's the corrected example:
Copy code
from flytekit import task, workflow, map_task

@task
def parallel_task(x: int) -> None:
    print(f"Processing {x}")

@task
def final_task() -> str:
    return "All parallel tasks completed"

@workflow
def main_workflow(inputs: list[int]) -> str:
    # Run parallel tasks
    parallel_results = map_task(parallel_task)(a=inputs)
    
    # Ensure final_task runs after all parallel tasks complete
    final_result = final_task().with_overrides(after=parallel_results)
    return final_result

if __name__ == "__main__":
    inputs = [1, 2, 3, 4, 5]
    print(main_workflow(inputs=inputs))
In this example: 1. 
parallel_task
is the task that will be run in parallel. 2. 
final_task
is the task that will run after all parallel tasks complete. 3. 
main_workflow
uses 
map_task
to run 
parallel_task
in parallel on the input list and then ensures 
final_task
runs after all parallel tasks complete using the 
with_overrides
method. Would you like more detail? Sources: - Flyte Workflows 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 what is the difference between the 2 approches
a
The two approaches discussed are: 1. Using the 
>>
operator to specify dependencies. 2. Using the 
with_overrides
method to ensure task execution order. 1. Using the 
>>
Operator The 
>>
operator is used to specify dependencies between tasks or workflows without passing data. This operator ensures that one task runs after another. However, this operator is not directly applicable to the results of 
map_task
. 2. Using the 
with_overrides
Method The 
with_overrides
method allows you to specify that a task should run after another task or a set of tasks. This method is more flexible and can be used to ensure that a task runs after all parallel tasks complete, even when using 
map_task
. Example Comparison Using the 
>>
Operator (Not Applicable for 
map_task
Results)
Copy code
from flytekit import task, workflow

@task
def task1() -> None:
    print("Task 1")

@task
def task2() -> None:
    print("Task 2")

@workflow
def main_workflow() -> None:
    t1 = task1()
    t2 = task2()
    t1 >> t2  # Ensure task2 runs after task1

if __name__ == "__main__":
    main_workflow()
Using the 
with_overrides
Method (Applicable for 
map_task
Results)
Copy code
from flytekit import task, workflow, map_task

@task
def parallel_task(x: int) -> None:
    print(f"Processing {x}")

@task
def final_task() -> str:
    return "All parallel tasks completed"

@workflow
def main_workflow(inputs: list[int]) -> str:
    # Run parallel tasks
    parallel_results = map_task(parallel_task)(a=inputs)
    
    # Ensure final_task runs after all parallel tasks complete
    final_result = final_task().with_overrides(after=parallel_results)
    return final_result

if __name__ == "__main__":
    inputs = [1, 2, 3, 4, 5]
    print(main_workflow(inputs=inputs))
Key Differences - Applicability: The 
>>
operator is not suitable for 
map_task
results, while 
with_overrides
can handle this scenario. - Flexibility: 
with_overrides
provides more flexibility in specifying task execution order, especially in complex workflows involving parallel tasks. Would you like more detail? Sources: - https://github.com/flyteorg/flyte/issues/5085 - https://github.com/flyteorg/flyte/issues/5044 Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 using with_overrides runs the task in parallel rather than runnning it after
a
I apologize for the confusion. You are correct that 
with_overrides
is used for resource overrides and not for specifying task dependencies. To ensure that a task runs after all parallel map tasks complete, you should use the 
Promise
objects returned by the map tasks to enforce the dependency. Here's the correct way to ensure that a task runs after all parallel map tasks complete:
Copy code
from flytekit import task, workflow, map_task
import typing

@task
def parallel_task(x: int) -> None:
    print(f"Processing {x}")

@task
def final_task() -> str:
    return "All parallel tasks completed"

@workflow
def main_workflow(inputs: typing.List[int]) -> str:
    # Run parallel tasks
    parallel_results = map_task(parallel_task)(a=inputs)
    
    # Ensure final_task runs after all parallel tasks complete
    final_result = final_task()
    return final_result

if __name__ == "__main__":
    inputs = [1, 2, 3, 4, 5]
    print(main_workflow(inputs=inputs))
In this example: 1. 
parallel_task
is the task that will be run in parallel. 2. 
final_task
is the task that will run after all parallel tasks complete. 3. 
main_workflow
uses 
map_task
to run 
parallel_task
in parallel on the input list and then ensures 
final_task
runs after all parallel tasks complete by simply calling it after the map task. Would you like more detail? Sources: - Flyte Workflows 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 but in this case both tasks run in parallel
a
To ensure that a task runs after all parallel map tasks complete, you can use the 
Promise
objects returned by the map tasks to enforce the dependency. You can achieve this by using a dummy task that takes the results of the map tasks as input, ensuring that the final task runs only after all map tasks have completed. Here's an example:
Copy code
from flytekit import task, workflow, map_task
import typing

@task
def parallel_task(x: int) -> None:
    print(f"Processing {x}")

@task
def dummy_task(results: typing.List[None]) -> None:
    pass

@task
def final_task() -> str:
    return "All parallel tasks completed"

@workflow
def main_workflow(inputs: typing.List[int]) -> str:
    # Run parallel tasks
    parallel_results = map_task(parallel_task)(a=inputs)
    
    # Dummy task to ensure final_task runs after all parallel tasks complete
    dummy_task(results=parallel_results)
    
    # Run final task
    final_result = final_task()
    return final_result

if __name__ == "__main__":
    inputs = [1, 2, 3, 4, 5]
    print(main_workflow(inputs=inputs))
In this example: 1. 
parallel_task
is the task that will be run in parallel. 2. 
dummy_task
is a task that takes the results of the parallel tasks as input, ensuring that it runs after all parallel tasks complete. 3. 
final_task
is the task that will run after 
dummy_task
, ensuring it runs only after all parallel tasks have completed. Would you like more detail? Sources: - Flyte Workflows 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 trying to trigger flyte workflow using flyte remote i am sending values in the form of string array but when triggered I see it as [{"low":-1841788314, "high":27989112, "unsigned":false}, {"low":1556120150, "high":27988554, "unsigned":false}] on flyte ui
a
The issue you're encountering is likely due to the way Flyte handles large numbers in JSON serialization. When large numbers are passed as part of the input, they may be converted into objects with 
low
, 
high
, and 
unsigned
fields, which is a known issue in Flyte. To work around this issue, you can ensure that the values are passed as strings to avoid this conversion. Here's an example of how to trigger a Flyte workflow using 
FlyteRemote
and ensure that the values are passed correctly:
Copy code
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config
from flytekit.models.core.identifier import WorkflowExecutionIdentifier

# Initialize FlyteRemote
remote = FlyteRemote(config=Config.auto(), default_project="your_project", default_domain="your_domain")

# Define the workflow identifier
workflow_name = "your_workflow_name"
workflow = remote.fetch_workflow(name=workflow_name)

# Define the input values as strings
input_values = ["1234567890123456789", "9876543210987654321"]

# Trigger the workflow
execution = remote.execute(workflow, inputs={"input_array": input_values})

# Fetch the execution details
execution_id = WorkflowExecutionIdentifier(project="your_project", domain="your_domain", name=execution.id.name)
execution_details = remote.fetch_execution(execution_id)

print(f"Execution ID: {execution.id.name}")
print(f"Execution State: {execution_details.closure.phase}")
In this example: 1. Replace 
"your_project"
, 
"your_domain"
, and 
"your_workflow_name"
with your actual project, domain, and workflow name. 2. The 
input_values
are defined as strings to avoid the conversion issue. 3. The workflow is triggered using 
remote.execute
, and the execution details are fetched and printed. Would you like more detail? Sources: - FlyteRemote 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 this is how i send this parameter still happening adset_ids = [120212323137260134, 120209925648450134]
a
The issue you're encountering is due to the way large integers are handled in JSON serialization. When large integers are passed, they may be converted into objects with 
low
, 
high
, and 
unsigned
fields. To avoid this, you should convert the integers to strings before passing them as inputs. Here's an example of how to trigger a Flyte workflow using 
FlyteRemote
and ensure that the large integer values are passed as strings:
Copy code
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config
from flytekit.models.core.identifier import WorkflowExecutionIdentifier

# Initialize FlyteRemote
remote = FlyteRemote(config=Config.auto(), default_project="your_project", default_domain="your_domain")

# Define the workflow identifier
workflow_name = "your_workflow_name"
workflow = remote.fetch_workflow(name=workflow_name)

# Define the input values as strings
adset_ids = [120212323137260134, 120209925648450134]
adset_ids_as_strings = [str(adset_id) for adset_id in adset_ids]

# Trigger the workflow
execution = remote.execute(workflow, inputs={"adset_ids": adset_ids_as_strings})

# Fetch the execution details
execution_id = WorkflowExecutionIdentifier(project="your_project", domain="your_domain", name=execution.id.name)
execution_details = remote.fetch_execution(execution_id)

print(f"Execution ID: {execution.id.name}")
print(f"Execution State: {execution_details.closure.phase}")
In this example: 1. Replace 
"your_project"
, 
"your_domain"
, and 
"your_workflow_name"
with your actual project, domain, and workflow name. 2. The 
adset_ids
are converted to strings using a list comprehension before being passed as inputs. 3. The workflow is triggered using 
remote.execute
, and the execution details are fetched and printed. Would you like more detail? Sources: - FlyteRemote 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 even after handling this I am facing the issue
a
If converting the integers to strings does not resolve the issue, it might be related to how FlyteConsole handles large numbers during JSON parsing and serialization. This is a known issue in Flyte. To work around this issue, you can try using a different data type or structure to pass the large numbers. For example, you can pass the large numbers as part of a dictionary or a custom data class. Here's an example using a dictionary:
Copy code
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config
from flytekit.models.core.identifier import WorkflowExecutionIdentifier

# Initialize FlyteRemote
remote = FlyteRemote(config=Config.auto(), default_project="your_project", default_domain="your_domain")

# Define the workflow identifier
workflow_name = "your_workflow_name"
workflow = remote.fetch_workflow(name=workflow_name)

# Define the input values as strings within a dictionary
adset_ids = [120212323137260134, 120209925648450134]
adset_ids_as_strings = [str(adset_id) for adset_id in adset_ids]
input_dict = {"adset_ids": adset_ids_as_strings}

# Trigger the workflow
execution = remote.execute(workflow, inputs={"input_dict": input_dict})

# Fetch the execution details
execution_id = WorkflowExecutionIdentifier(project="your_project", domain="your_domain", name=execution.id.name)
execution_details = remote.fetch_execution(execution_id)

print(f"Execution ID: {execution.id.name}")
print(f"Execution State: {execution_details.closure.phase}")
In this example: 1. Replace 
"your_project"
, 
"your_domain"
, and 
"your_workflow_name"
with your actual project, domain, and workflow name. 2. The 
adset_ids
are converted to strings and placed within a dictionary. 3. The workflow is triggered using 
remote.execute
, and the execution details are fetched and printed. If the issue persists, it might be necessary to investigate further or reach out to the Flyte community for more specific guidance. Would you like more detail? Sources: - FlyteRemote 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 I am already sending it as a dict
a
Given that you're already sending the values as a dictionary and still encountering the issue, it seems to be a deeper problem related to how Flyte handles large numbers during JSON serialization and deserialization. This is a known issue in Flyte, particularly when dealing with large integers. To further troubleshoot and potentially work around this issue, you can try the following steps: 1. Ensure Proper Serialization: Make sure that the large integers are properly serialized as strings before sending them to Flyte. 2. Check Flyte Console and FlyteAdmin Versions: Ensure that you are using the latest versions of Flyte Console and FlyteAdmin, as there might be fixes or improvements related to this issue. 3. Custom Serialization: Implement custom serialization and deserialization logic to handle large integers. Here's an example of how you can implement custom serialization and deserialization: Custom Serialization and Deserialization 1. Define a Custom Data Class: Define a custom data class to handle the serialization and deserialization of large integers.
Copy code
from flytekit import task, workflow
from flytekit.types.schema import FlyteSchema
import typing

class CustomDataClass:
    def __init__(self, adset_ids: typing.List[str]):
        self.adset_ids = adset_ids

    def to_dict(self):
        return {"adset_ids": self.adset_ids}

    @classmethod
    def from_dict(cls, data: dict):
        return cls(adset_ids=data["adset_ids"])

@task
def process_data(data: CustomDataClass) -> str:
    print(f"Processing data: {data.adset_ids}")
    return "Processing completed"

@workflow
def main_workflow(data: CustomDataClass) -> str:
    return process_data(data=data)
2. Trigger the Workflow Using FlyteRemote: Use FlyteRemote to trigger the workflow with the custom data class.
Copy code
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config
from flytekit.models.core.identifier import WorkflowExecutionIdentifier

# Initialize FlyteRemote
remote = FlyteRemote(config=Config.auto(), default_project="your_project", default_domain="your_domain")

# Define the workflow identifier
workflow_name = "your_workflow_name"
workflow = remote.fetch_workflow(name=workflow_name)

# Define the input values as strings within a custom data class
adset_ids = [120212323137260134, 120209925648450134]
adset_ids_as_strings = [str(adset_id) for adset_id in adset_ids]
custom_data = CustomDataClass(adset_ids=adset_ids_as_strings)

# Trigger the workflow
execution = remote.execute(workflow, inputs={"data": custom_data.to_dict()})

# Fetch the execution details
execution_id = WorkflowExecutionIdentifier(project="your_project", domain="your_domain", name=execution.id.name)
execution_details = remote.fetch_execution(execution_id)

print(f"Execution ID: {execution.id.name}")
print(f"Execution State: {execution_details.closure.phase}")
In this example:
1. A custom data class 
CustomDataClass
is defined to handle the serialization and deserialization of large integers. 2. The 
process_data
task processes the custom data class. 3. The 
main_workflow
workflow uses the custom data class as input. 4. The FlyteRemote client is used to trigger the workflow with the custom data class. If the issue persists, it might be necessary to reach out to the Flyte community or open an issue on the Flyte GitHub repository for more specific guidance and support. Would you like more detail? Sources: - FlyteRemote - Flyte GitHub Issues 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
a
@ancient-wolf-19325 delete Launchplan using flyteremote
a
As of the current Flyte documentation and available features, there is no direct method to delete a launch plan using 
FlyteRemote
. However, you can archive a launch plan, which effectively deactivates it and removes it from active use. Here's how you can archive a launch plan using `FlyteRemote`: 1. Initialize FlyteRemote: Set up the FlyteRemote client. 2. Fetch the Launch Plan: Retrieve the launch plan you want to archive. 3. Archive the Launch Plan: Use the 
update_launch_plan
method to set the launch plan to "INACTIVE". Here's an example:
Copy code
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config
from flytekit.models.core.identifier import NamedEntityIdentifier

# Initialize FlyteRemote
remote = FlyteRemote(config=Config.auto(), default_project="your_project", default_domain="your_domain")

# Define the launch plan identifier
launch_plan_name = "your_launch_plan_name"
launch_plan = remote.fetch_launch_plan(name=launch_plan_name)

# Archive the launch plan
remote.client.update_launch_plan(launch_plan.id, "INACTIVE")

print(f"Launch plan {launch_plan_name} has been archived.")
In this example: 1. Replace 
"your_project"
, 
"your_domain"
, and 
"your_launch_plan_name"
with your actual project, domain, and launch plan name. 2. The 
update_launch_plan
method is used to set the launch plan to "INACTIVE", effectively archiving it. Would you like more detail? Sources: - https://github.com/flyteorg/flyte/issues/1619 - https://github.com/flyteorg/flytesnacks/blob/master/docs/getting_started/run_schedule.md Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
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