Hi @Marvin, how to use the LiteralResult in the flow with multiple k8s pods running

It seems there might not be explicit documentation or relevant GitHub issues specifically addressing the use of

LiteralResult

in flows running with multiple Kubernetes pods. However, combining the general guidance from the Prefect documentation on using Kubernetes and handling flow results, the steps to use

LiteralResult

in such a setup can be summarized as follows: 1. Pod Configuration: Define your Kubernetes pod configuration using Prefect's Kubernetes support, ensuring you have the Kubernetes credentials set up. 2. Flow Execution: In your

Flow

, make use of the

LiteralResult

to store or return literal values. This can be useful for passing simple data between tasks without complex serialization. Here’s a basic example to illustrate:

python
from prefect import flow, task
from prefect.results import LiteralResult
from prefect_kubernetes.pods import KubernetesPod

@task
def my_task():
    # Perform your task logic and return a literal result
    return LiteralResult("Task completed successfully")

@flow
def my_flow():
    result = my_task()
    return result

# Define your Kubernetes pod configuration and deploy the flow
kubernetes_pod = KubernetesPod(
    name="my-flow-pod",
    image="your-docker-image",
    labels={"purpose": "execute-my-flow"}
)

if __name__ == "__main__":
    my_flow()

For more detailed setup and configuration, refer to the following Prefect documentation: - Using Kubernetes with Prefect: https://docs.prefect.io/latest/integrations/prefect-kubernetes/pods/ - Understanding Prefect Flows: https://docs.prefect.io/latest/concepts/flows/ Should you need deeper insights or examples, consider opening an issue or question on Prefect's GitHub repository for more community-specific advice.

Hi @Marvin what about using LiteralResult with run_deployment?

There doesn't seem to be an explicit example or documentation directly addressing the use of

LiteralResult

with

run_deployment

. However, combining the general understanding of Prefect's results handling and deployments, here's a summary of how you might combine the two: 1. Define a Flow with LiteralResult: Create a flow using Prefect, and make use of the

LiteralResult

to encapsulate simple return values. 2. Deploy the Flow: Use Prefect's deployment capabilities to deploy your flow, allowing it to be run with Kubernetes infrastructure if necessary. 3. Run the Deployment: Execute the deployed flow using

run_deployment

. Here is a basic example to illustrate:

python
from prefect import flow, task, get_run_logger
from prefect.results import LiteralResult
from prefect.deployments import Deployment
from prefect.filesystems import LocalFileSystem, FileSystem

@task
def my_task():
    # Perform your task logic and return a literal result
    return LiteralResult("Task completed successfully")

@flow
def my_flow():
    result = my_task()
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Task result: {result}")
    return result

# Define and deploy the flow
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    storage=LocalFileSystem(basepath="/path/to/flows"),
)

if __name__ == "__main__":
    deployment.apply()

    # To run the deployment, use the following
    from prefect.deployments import run_deployment

    run_deployment(name="my-deployment")

Some helpful links to better understand these concepts: - Results in Prefect: https://docs.prefect.io/latest/concepts/results/ - Deployments with Prefect: https://docs.prefect.io/latest/guides/upgrade-guide-agents-to-workers/ For more detailed configurations, adjustments might be necessary based on your specific environment and requirements. If further assistance is needed, consider reaching out on Prefect's GitHub repository for more tailored support.

Can I use LiteralResult as the flow result @Marvin?

Yes, you can use

LiteralResult

as the result of your flow in Prefect.

LiteralResult

is suitable for storing simple, inline values within a flow's result. Below is an example illustrating how you can achieve this:

python
from prefect import flow, task
from prefect.results import LiteralResult

@task
def my_task():
    return LiteralResult("Task completed successfully")

@flow
def my_flow():
    result = my_task()
    return result

if __name__ == "__main__":
    flow_result = my_flow()
    print(f"Flow result: {flow_result}")

In this example,

my_task

returns a

LiteralResult

, which is then returned by the flow itself. This allows you to use simple, literal values as the result of your flow. For more details on Prefect results, you can refer to this link: https://docs.prefect.io/latest/concepts/results/ If you need any more specific examples or have additional questions, feel free to ask!

Hi @Marvin , is there any way to store the result locally with KubernetesWorker and flow run separately with run_deployment

Yes, you can store the result locally while using a

KubernetesWorker

and running your flow with

run_deployment

. Here’s a step-by-step guide: 1. Define the Flow and Task: Create a Prefect flow and task that will generate a result to be stored locally. 2. Configure Result Storage: Use the

LocalFileSystem

to configure where the results should be stored locally. 3. Deploy the Flow: Use Prefect's deployment capabilities to deploy the flow, specifying your storage configuration. 4. Run the Deployment: Execute the deployed flow using

run_deployment

. Here is an example illustrating these steps:

python
from prefect import flow, task
from prefect.results import LocalResult
from prefect.deployments import Deployment
from prefect.filesystems import LocalFileSystem

# Step 1: Define the Task and Flow
@task
def my_task():
    return "Task completed successfully"

@flow
def my_flow():
    result = my_task().with_options(result=LocalResult(dir="/path/to/results"))
    return result

# Step 2: Configure Local Result Storage
# Step 3: Deploy the Flow
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-local-results-deployment",
    storage=LocalFileSystem(basepath="/path/to/flows"),
)

if __name__ == "__main__":
    deployment.apply()

    # Step 4: Run the Deployment
    from prefect.deployments import run_deployment

    flow_result = run_deployment(name="my-local-results-deployment")
    print(f"Flow result: {flow_result}")

Key Points: - The

LocalResult

class is used to specify local storage for the results. In the

my_task

definition, the result is configured to be stored locally. - The

LocalFileSystem

block is used to specify the local file system storage for the flow code during deployment. For more detailed instructions and additional considerations, you can refer to the following Prefect documentation links: - Kubernetes Worker: https://docs.prefect.io/latest/integrations/prefect-kubernetes/worker/ - Result Types: https://docs.prefect.io/latest/concepts/results/ - Deployments and Storage: https://docs.prefect.io/latest/getting-started/quickstart/ If you need further assistance or custom configurations, feel free to ask!