New RunLLM response generated: >>>*# Question* Hello community, I am executing registered flyte tasks by FlyteRemote: https://github.com/flyteorg/flytekit/blob/master/flytekit/remote/remote.py#L1176 Is there a way to have cache hit when having single (PythonPickle) blob as inputs? I have tried to add the Annotated HashMethod to the input types in the flyte task, but it is not working. # Answer To achieve cache hits with a single (PythonPickle) blob as inputs, you need to ensure that the

HashMethod

is correctly applied to the input types in your Flyte task. This involves defining a custom hash function for the PythonPickle object and using the

Annotated

type with

HashMethod

in your task signature. Here's an example of how to do this: 1. Define a Hash Function: Create a function that generates a hash for your PythonPickle object. 2. Annotate Task Input: Use the

Annotated

type with

HashMethod

to annotate the input type in your task.

from flytekit import task, workflow, HashMethod
from typing_extensions import Annotated
import pickle

def hash_pickle(obj) -> str:
    return str(hash(pickle.dumps(obj)))

PickleType = Annotated[object, HashMethod(hash_pickle)]

@task(cache=True, cache_version="1.0")
def process_data(data: PickleType) -> str:
    # Process the data
    return "Processed"

@workflow
def wf(data: PickleType) -> str:
    return process_data(data=data)

In this example: -

hash_pickle

is a custom hash function that generates a hash for the pickled object. -

PickleType

is an annotated type that uses

HashMethod

to specify the custom hash function. - The

process_data

task and

wf

workflow use

PickleType

for the input type to ensure caching works correctly. Make sure to register the tasks and workflows with Flyte and execute them using

FlyteRemote

as needed. Would you like more details on any specific part of this process? Sources: - Flyte Caching Documentation - Flyte Issue on Caching with HashMethod - Flyte Example on Task Caching Slack Link https://.slack.com/archives/CP2HDHKE1/p1728033245.602169 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.