locache 1.0.0

Cache expensive function calls to disk.

locache

Lo(cal)cache.

A small util library for caching the results of function calls to disk.

Only intended for expensive function calls with simple arguments and keyword arguments.

Installation

pip3 install locache

Examples

When used in normal python scripts, @persist is sufficient.

foo.py:

from locache import persist

@persist
def my_func(x, num=3):
    print("Hi from foo!")
    return x * num

my_func(1)        # prints "Hi from foo!", returns 3
my_func(2, num=2) # prints "Hi from foo!", returns 4
my_func(1)        # returns 3
my_func(2, num=2) # returns 4

Running foo.py will lead to the creation of a foo.py.cache/ directory, with files my_func__1__num=3 and my_func__2__num=2.

Notebooks

When using python notebooks, the name keyword is also required:

bar.ipynb:

from locache import persist

@persist(name="notebook")
def my_func(x, num=3):
    print("Hi from foo!")
    return x * num

my_func(1)        # prints "Hi from foo!", returns 3
my_func(1)        # returns 3

Running this cell will lead to the creation of a notebook.cache/ directory in the same directory as the notebook.

Resetting the Cache

Resetting the cache can be achieved in two ways:

• manually deleting the relevant cache files / directories.
• using locache.reset

from locache import persist, reset

@persist
def foo(x):
    print("hello from foo")
    return x ** 2

foo(1) # prints "Hi from foo!", returns 3
foo(1) # returns 3

reset(foo)

foo(1) # prints "Hi from foo!", returns 3

In a notebook setting, the relevant name needs to also be passed:

@persist(name="notebook")
def foo(x):
    return x**2

foo(1)
reset(foo, name="notebook")

Anti-Examples

Don't pass large data structures to persisted functions:

import numpy as np
from locache import persist

@persist # don't do this!
def foo(x):
    return np.sum(x * x)

arr = np.ones(10) + np.randn(10)
foo(arr)