Add utilities to serialize context to a dictionary and hydrate context from a dictionary

[desertaxle]

When running tasks on remote infrastructure like Dask and Ray with the task engine, we lose some of the context surrounding the current task run. This makes it difficult to link the task run back to its parent run. This can also make it difficult to ensure the correct settings are propagated to the remote infrastructure.

This PR introduces two mirror utilities: serialize_context and hydrated_context. When used in conjunction, we can serialize the current context to a pickleable dictionary, send it to the remote infrastructure, and recreate it. This helps to make the task engine highly portable and helps us maintain consistent dependency tracking.

The task run engine has been updated to accept a context kwargs, which consumes the output of serialize_context.

Example

Submit a task to Dask with the current context:

from uuid import uuid4

from distributed import Client

from prefect import flow, task
from prefect.context import serialize_context


@task
def square(x):
    return x**2


@flow
def my_flow():
    context = serialize_context()
    with Client() as client:
        future = client.submit(
            run_task,
            square,
            parameters={"x": 42},
            context=context,
        )

Checklist

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[cicdw]

I like how explicit this approach is vs. making context natively pickleable (which would probably lead to some confusion around __enter__ and __exit__ mechanics. Nice.