Hi, I'd like to get more familiar with the caching options. My workflow calls a series of steps, each of which is a

task

, and passes data from one to the next. Transformations on the

data

are defined by an object called

Rule

, which has some attributes. Think of it as a dictionary, more or less) So far, I have tasks that are decorated like this:

@task(cache_expiration=timedelta(days=1), cache_policy=TASK_SOURCE + INPUTS)
def my_function(data, rule_spec):
    ... # do something with data, based upon what is defined in the Rule object
    return data

This works great, and I have the following setup in my run script:

#!/bin/bash -e
export PREFECT_SERVER_API_HOST=0.0.0.0
export PREFECT_LOCAL_STORAGE_PATH="./task-cache/"
prefect server start &
time python my_workflow.py

I get something in the expected location:

$ ls -lah ./task-cache/
total 5.0G
drwxr-sr-x 2 a270077 ab0246 4.0K Oct  9 10:03 .
drwxr-sr-x 5 a270077 ab0246  84K Oct  9 10:02 ..
-rw-r--r-- 1 a270077 ab0246  327 Oct  9 10:03 1d33fdca8b1fb0b2f599d4a5f5baeabb
-rw-r--r-- 1 a270077 ab0246  15M Oct  9 10:05 2772efda3019a1630f1da30c53480b1c
-rw-r--r-- 1 a270077 ab0246 9.4M Oct  9 10:05 33f19ada24e79b8ba27a4507b20c903d
-rw-r--r-- 1 a270077 ab0246 2.5G Oct  9 10:03 6c66febdf523c0a30897ed4ee57edf7d
-rw-r--r-- 1 a270077 ab0246 2.5M Oct  9 10:05 9a5ff13448c0e86425d82e0855557725
-rw-r--r-- 1 a270077 ab0246 2.5G Oct  9 10:03 bf43304fee052e08aed9ef0402fe2791
-rw-r--r-- 1 a270077 ab0246 6.4K Oct  9 09:57 da6c58da3aa841659f1aced8e7dbc535
-rw-r--r-- 1 a270077 ab0246 2.5M Oct  9 10:05 e96bf03d4145ed875c25824a29cd8cc7

And, these things look like json:

$ jq . ./task-cache/1d33fdca8b1fb0b2f599d4a5f5baeabb
{
  "metadata": {
    "storage_key": "/work/ab0246/a270077/SciComp/Projects/pymorize/examples/task-cache/1d33fdca8b1fb0b2f599d4a5f5baeabb",
    "expiration": "2024-10-10T08:02:48.074715Z",
    "serializer": {
      "type": "pickle",
      "picklelib": "cloudpickle",
      "picklelib_version": null
    },
    "prefect_version": "3.0.1",
    "storage_block_id": null
  },
  "result": "gAVOLg==\n"
}

My question is: how can I load up this object in an interactive Python session and play around with it. Prefect must be able to do this internally to restart from a cached state.