@Marvin Why am I getting this error:

ValueError: Unable to create hash - objects could not be serialized.
  JSON error: Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>
  Pickle error: cannot pickle 'weakref.ReferenceType' object

This often occurs when task inputs contain objects that cannot be cached like locks, file handles, or other system resources.

To resolve this, you can:
  1. Exclude these arguments by defining a custom `cache_key_fn`
  2. Disable caching by passing `cache_policy=NONE`

The error you're encountering relates to Prefect's handling and serialization of task inputs, specifically with types that can't be easily serialized like

pandas.DataFrame

. Prefect attempts to hash task inputs to manage caching and state; however, some objects such as DataFrames, locks, or file handles can't be directly serialized using the default JSON or Pickle serializers. The recommendation in Prefect's error message provides two main solutions: 1. Custom Cache Key Function: You can define a custom

cache_key_fn

to exclude non-serializable arguments when hashing. This is useful if you want to continue caching but just need to exclude certain complex objects. 2. Disable Caching: If caching isn't necessary for your flow, you can disable it by setting

cache_policy=NONE

, which bypasses the need to serialize and hash task inputs. You can read more about Prefect's serializer options, including the limitations of JSON and Pickle serializers, in their documentation on results and serializers. If you're feeling a bit like a "brain the size of a planet" having to deal with these serialization details, remember you're not alone—many face this cosmic conundrum!

@Marvin If I try

cache_policy=NONE

it does not work as NONE does not work with prefect?

It seems there's a bit of confusion about the

cache_policy=NONE

setting. Unfortunately, I couldn't find specific documentation for a

cache_policy=NONE

option in Prefect 2.x, which likely means it's not a valid option for disabling caching directly. However, there are alternative ways to manage task caching in Prefect: 1. Custom Cache Key Function: As previously mentioned, defining a custom

cache_key_fn

can allow you to control which task inputs are considered for caching, effectively giving you control over caching behavior. 2. Not Setting Caching: Simply avoid setting any caching parameters if you don't want caching. Prefect tasks don't cache by default unless a

cache_key_fn

is provided or other caching settings are explicitly configured. If always looking for the path of least resistance makes you feel a bit like Marvin on a bad day, I feel you—sometimes these things get a bit tangled. For the most reliable methods to manage caching, make sure to check out Prefect's official documentation or this resource on tasks and caching.

@Marvin I am not setting a cache_key_fn so I don't know why it is trying to cache?

It appears there was an issue accessing the Prefect GitHub repository directly to check for related bugs. However, in general situations where caching behavior is unexpected, there are a few possibilities to consider: 1. Default Caching Behavior: Sometimes, if a task detection mechanism deems its operations repetitive and significant enough, it might trigger default caching logic. 2. Task State: A task could inadvertently reflect a "cached" as part of its successful state logic without explicit configuration. 3. Multiple Versions or Overlapping Configurations: Ensure that there are no overlapping settings or older versions in your environment that might trigger caching. 4. Custom Task Classes: If you're using custom task classes extending Prefect's base classes, ensure they don't have their own caching logic. To resolve this, you might check your task definitions and ensure there's no inadvertent configuration triggering caching. Alternatively, you may want to explore Prefect's community discussions or forums for similar experiences and solutions. If machines could cringe, I might at the thought of yet another mysteriously caching task!