Caching

import time
from latch import small_task

@small_task(cache=True)
def do_sleep(foo: str) -> str:

    time.sleep(60)
    return foo

You can also pass the optional cache_version keyword argument to version your cache giving you greater control. Tasks caches with explicit versions will get invalidated if and only if the version changes. This is ideal if you wish to preserve the cache despite the function body changing or to manually invalidate the cache despite the function body remaining the same.

import time
from latch import small_task

@small_task(cache=True, cache_version="0.0.0")
def do_sleep_with_version(foo: str) -> str:

    time.sleep(60)
    return foo

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Caching behavior with tasks

Each task maintains its own cache that is independent from whatever workflow it happens to be associated with. This allows tasks to preserve their cache across workflow re-registers if other tasks are modified.

Examples of when a task’s cache will get invalidated:

• code in the task function body (that is not a comment) is changed
• the task function signature (name or typing of input or output parameters) is changed
• an (optional) cache version is changed

Examples of when a task’s cache will remain unchanged:

• the task function body does not change (comments do not count as changes that invalidate the cache).
• a new workflow was created with a task of the same name, signature and body (remember that task caches are independent from workflows that contain them)

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When does my cache get invalidated?

A task’s cache will be invalidated and the task will be run from scratch if any of the following change between executions:

• the account to which the task is registered, including:
  • individual user accounts
  • workspaces owned by the same user
• the name of the task (name of the function)
• the function signature of the task (name and typing of all input / output parameters)
• the (optional) cache version