@Marvin If my flow/task doesn't return anything, can I still use

persist_results

to cache it, and not run it during a rerun if it was successful?

In Prefect 3.x, results are typically associated with the data returned by a task or flow. These results are essential for features like caching, which prevents rerunning tasks if they have already succeeded and their results are cached. However, if your task or flow doesn't return anything, you won't have a result to cache. Prefect's caching mechanism relies on these results, so without a return value, you can't directly use

persist_results

to cache the execution. To work around this, you might consider using a custom cache key function or Pydantic models to manage caching based on input properties or other execution metadata. This allows you to define caching behavior even when no explicit result is returned. For more details, you can review the documentation on task caching and result management.

@Marvin Can you give me a small python example to run locally so I can see how caching works? Make sure there is a task and a flow in this example. Also, I want to deploy this flow to my local prefect server and then run this deployment from another file using

run_deployment

Here's a small example demonstrating how to use caching with a task and a flow in Prefect 3.x. This example includes deploying the flow to a local Prefect server and running it using

run_deployment

. ### Step 1: Define Your Task and Flow

python
from prefect import flow, task
from prefect.cache_policies import CachePolicy

@task(cache_policy=CachePolicy("task_input"))
def add(x, y):
    print("Running task...")
    return x + y

@flow
def my_flow(x, y):
    result = add(x, y)
    print(f"Result: {result}")

if __name__ == "__main__":
    my_flow(1, 2)

### Step 2: Deploy the Flow Create a deployment script,

deploy.py

, to deploy your flow to the local Prefect server.

python
from prefect.deployments import Deployment
from prefect import flow

from my_flow_file import my_flow  # Replace with your actual flow file name

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_flow_deployment",
    work_queue_name="default",
)

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

### Step 3: Run the Deployment from Another File Create another script,

run_deployment.py

, to run your deployed flow.

python
from prefect.deployments import run_deployment

if __name__ == "__main__":
    run_deployment("my_flow_deployment")

### Notes: - Ensure you have a local Prefect server running. You can start it using

prefect server start

. - The

cache_policy

in the task uses the input parameters to determine if the task should be rerun. If the inputs are the same and the task has succeeded before, it won't rerun. - Replace

"my_flow_file"

with the actual file name containing your flow definition. This setup demonstrates caching, deployment, and execution on a local Prefect server.

@Marvin Ok, dont use run_deployment, just make it as simple as possible. One task, one flow and running it locally as simply as possible.

Here's a simplified example with one task and one flow, focusing on caching and running locally:

python
from prefect import flow, task
from prefect.cache_policies import CachePolicy

# Define a task with caching based on its inputs
@task(cache_policy=CachePolicy("task_input"))
def add(x, y):
    print("Running task...")
    return x + y

# Define a flow that uses the task
@flow
def my_flow():
    result = add(1, 2)
    print(f"Result: {result}")

# Run the flow locally
if __name__ == "__main__":
    my_flow()

### Key Points: - Task Caching: The

add

task is cached based on its inputs (

x

and

y

). If you run the flow again with the same inputs, the task will not execute again and will use the cached result instead. - Running Locally: The flow is executed directly in the script by calling

my_flow()

within the

if __name__ == "__main__":

block. This example demonstrates a straightforward way to implement caching and run a Prefect flow locally without involving deployment to a server.

@Marvin how to run it?

To run the script locally, follow these steps: 1. Save the Script: Save the provided code into a Python file, for example,

my_flow.py

. 2. Open Terminal: Open your terminal or command prompt. 3. Navigate to the File Directory: Use the

cd

command to navigate to the directory where

my_flow.py

is located. 4. Run the Script: Execute the script by running the following command:

bash
   python my_flow.py

This will run your flow locally, executing the

add

task and printing the result. If you run it again, you'll observe that the task won't execute again due to caching, unless the inputs change.

@Marvin where is the cache located? I want to see it saved there.

By default, Prefect stores cached results locally in the

~/.prefect/storage/

directory. The filenames in this directory correspond to the computed cache keys from your task runs. This means that each cached result is stored as a file identified by its cache key. If you want to inspect the cache, you can navigate to this directory and see the files representing cached results. You can also configure the storage location by using the

key_storage

argument in a cache policy, allowing you to specify a different local path or even a remote storage option like S3. For more details, you can refer to the task caching documentation.

@Marvin is

persist_result

True or False by default?

By default, results in Prefect are not persisted, meaning

persist_result

is set to

False

. If you want to enable result persistence, you can do so globally using the

PREFECT_RESULTS_PERSIST_BY_DEFAULT

setting or configure it on a per-task basis using the

persist_result

keyword argument. For more information, you can refer to the Prefect results documentation.