@Marvin I have a task in a flow. How to rerun from scratch (no caching) the task if the flow is retrying I tried @task(cache_key_fn=static_cache_key, refresh_cache=True) but it did not work

It appears that you need more specific behavior for rerunning a task without using the cache when the flow is retrying. Prefect’s caching behavior is controlled by

cache_key_fn

and the

refresh_cache

parameter, but additional steps might be needed. ### Relevant Info from Prefect Docs: - How Caching Works: Caching uses

cache_key_fn

to determine if the task is considered the same as a previous one, and uses that state if found. - Refreshing the Cache: The task decorator has a

refresh_cache

parameter, but every retry might not reset this on its own. ### Possible Approach: 1. Custom Cache Key Function: Ensure the cache key includes information that forces a cache miss on retries. Example:

python
from prefect import task, Flow
from prefect.utilities.tasks import task_input_hash
from prefect.engine.signals import SUCCESS

def unique_retry_cache_key(context, parameters):
    return f"{context.task_run.flow_run_id}-{context.task_run.retry_count}-{task_input_hash(context, parameters)}"

@task(cache_key_fn=unique_retry_cache_key, refresh_cache=True, retries=3)
def my_task():
    print("Running task")
    return "result"

with Flow("my-flow") as flow:
    result = my_task()

flow.run()

- Explanation: The

unique_retry_cache_key

function includes the retry count in the cache key, ensuring it changes on each retry, effectively bypassing the cache. ### Summary Incorporate the retry count or other unique identifiers into your cache key can ensure that each retry starts fresh without using cached results. For more details, refer to the official Prefect documentation on Tasks. If there’s further nuance needed, consult the provided documentation for deeper customization options or potential updates. If you encounter unique issues still, they might not be well-documented or could be a bug, for which you should check new issues or make a report on the Prefect GitHub repository.

@Marvin

import os

os.environ["PREFECT_TASKS_REFRESH_CACHE"] = "true"

Does not work

There may be a misunderstanding regarding the environment variable

PREFECT_TASKS_REFRESH_CACHE

. According to the Prefect documentation, this variable does indeed enable cache refreshing globally for all tasks. However, bear in mind that changes to environment variables must be made before your Flow and tasks are registered or run. Here is an example of how to ensure tasks refresh their cache when the flow runs or retries: ### Setting the Environment Variable Effectively Ensure you set this environment variable before defining or executing your flows and tasks:

python
import os

# Setting the environment variable
os.environ["PREFECT_TASKS_REFRESH_CACHE"] = "true"

from prefect import task, Flow
from prefect.utilities.tasks import task_input_hash

def static_cache_key(context, parameters):
    return "static cache key"

@task(cache_key_fn=static_cache_key, refresh_cache=True, retries=3)
def my_task():
    print("Running task")
    return "result"

with Flow("my-flow") as flow:
    result = my_task()

# Running the flow
flow.run()

### Additional Example Using Flow Parameters You can also refresh cache for a specific task using the

with_options

method:

python
import os
from prefect import task, Flow
from prefect.task_runners import SequentialTaskRunner

def static_cache_key(context, parameters):
    return "constant cache key"

@task(cache_key_fn=static_cache_key, refresh_cache=False)
def simple_task():
    print("Task Execution")
    return "some_result"

with Flow("simple_flow", task_runner=SequentialTaskRunner()) as flow:
    t1 = simple_task()
    t2 = simple_task()
    t3 = simple_task.with_options(refresh_cache=True)()

flow.run()

### Ensuring Environment Variable is Active Set the environment variable properly in the same system/context where the flow is run, such as in a container, script, or environment manager.

bash
export PREFECT_TASKS_REFRESH_CACHE=true
python your_script.py

### Summary The

PREFECT_TASKS_REFRESH_CACHE

variable can globally refresh tasks caches. Use it carefully, setting it at the start of your Python script/environment to ensure it's active before the flow is run. If it still doesn't work, consider the method shown for

with_options

, which ensures specific task behavior. Refer to this link for more detailed documentation on this feature and additional examples. If you continue to face issues, they might be environment-specific, or a bug, so periodically check or open an issue on the Prefect GitHub repository.

@Marvin ".with_options(refresh_cache=True)" does not work

It appears there is an open issue on GitHub discussing the behavior that

refresh_cache

does not reset the

cache_expiration

but simply updates the cached value. The core of the problem is that once a

cache_expiration

is set, it cannot be changed or reset within Prefect's current caching mechanism. This limits the ability to fully override cached values upon retries or specific task runs. ### Workaround Based on the issue discussions and current Prefect functionalities, you can attempt to modify your caching strategy to include elements that force the cache to miss:

python
from prefect import task, Flow
from prefect.utilities.tasks import task_input_hash

def unique_cache_key_with_time(ctx, params):
    import time
    return f"{task_input_hash(ctx, params)}-{round(time.time() // 60)}"

@task(cache_key_fn=unique_cache_key_with_time, refresh_cache=True, retries=3)
def my_task():
    print("Running task")
    return "result"

with Flow("my-flow") as flow:
    result = my_task()

# Running the flow
flow.run()

### Explanation: - Dynamic Cache Key: This example uses the current time (rounded to the nearest minute) as part of the cache key. This forces a cache miss since the key will change every minute. - Cache Expiration: Adjusting

cache_expiration

dynamically (e.g., to a short duration) might help by forcing tasks to re-run more frequently. ### Next Steps: - Monitor GitHub Issue: Continue to monitor Prefect's Issue #10344 for updates as the latest versions might include fixes or improvements. - Prefect Discussions: Engage in the Prefect community or GitHub discussions for potential updates or alternative solutions. For further exploration and exact usage, refer to Prefect's Caching documentation. If the issue still persists, you might need to create an explicit workaround or reach out to Prefect’s community support channels for more tailored assistance.