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Python memoization across program runs.
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Failed to load latest commit information. Fix typo and add wikipedia link for memoization Nov 19, 2018 init commit Nov 19, 2018

Cache is a one file python library that extends memoization across runs using a cache file.

Often it takes some time to load files, do expensive data processing, and train models. As a result, many nice tools have popped up to make the experience smoother, like Jupyter notebooks. However, these tools make it hard to keep coding conventions clean and develop a library-like interface for your project. attempts to make it painless to cache expensive functions across python runs, enabling quick iteration while developing usable code.


To use the file, import cache and annotate functions with @cache.cache().

import cache

def expensive_func(arg, kwarg=None):
  # Expensive stuff here
  return arg

The @cache.cache() function can take multiple arguments.

  • @cache.cache(timeout=20) - Only caches the function for 20 seconds.
  • @cache.cache(fname="my_cache.pkl") - Saves cache to a custom filename (defaults to hidden file .cache.pkl)
  • @cache.cache(key=cache.ARGS[KWARGS,NONE]) - Check against args, kwargs or neither of them when doing a cache lookup.

How it works

With a file loaded from disk, checks against the name, arguments and hash of a function's source to decide if the function has been run before. If it has it returns the cached result immediately. It currently uses pickle and inspect under the hood, making it non-portable.

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