Simple Caching

Small project to standardize storing (key, value) data for caching purposes.

Supported caching methods:

1. Disk
   • NpyFS - Numpy array export
2. Memory
   • DictMemory - keys are stored as dict keys and recalled from memory

Example

from simple_caching.storage import MemoryDict
import numpy as np
import time

def working_fn(image: np.ndarray) -> np.ndarray:
   """Working function that takes on average 0.2s"""
   if np.random.rand() <= 0.2:
      time.sleep(1)
   return (image - image.min()) / (image.max() - image.min())

data = np.random.randn(32, 240, 420, 3).astype(np.float32) # 32 images of 240x420 shape

# Standard version. Takes 0.2s in average per epoch for the same processing.
for i in range(100):
   new_data = [working_fn(image) for image in data]
   # do further processing with the result

# Cached version. We store the result on memory.

def key_encode_fn(image: np.ndarray) -> str:
   """Return a cachable key for each image. We use a string of mean + std that should be unique enough."""
   return f"{image.mean()}_{image.std()}"

cache = DictMemory(name="images", key_encode_fn=key_encode_fn)
# alternative, use: cache.map(working_fn, data)
for image in data:
   cache[image] = working_fn(image)

for i in range(100):
   new_data = [cache[image] for image in data]
   # do further processing with the result

Decoator version

@cache_fn(NpyFS, key_encode_fn)
def working_fn(image: np.ndarray) -> np.ndarray:
   """Working function that takes on average 0.2s"""
   if np.random.rand() <= 0.2:
      time.sleep(1)
   return (image - image.min()) / (image.max() - image.min())

def key_encode_fn(image: np.ndarray) -> str:
   """Return a cachable key for each image. We use a string of mean + std that should be unique enough."""
   return f"{image.mean()}_{image.std()}"

for i in range(100):
   new_data = working_fn(image)
   # do further processing with the result