Joblib: Lightweight pipelining with Python functions

Warnings

• breaking Joblib 1.5.3 introduces changes to the Memory class that may affect existing cache directories.
  Fix: Review and update cache directory configurations to align with new Memory class behavior.
• gotcha Using joblib.load() on untrusted sources can execute arbitrary code, posing security risks.
  Fix: Avoid loading objects from untrusted sources to maintain security.

Install

• pip install joblib Install Joblib

Imports

• Memory

  from joblib import Memory

  Ensure correct import path for Memory class

Quickstart

Example of using Joblib's Memory class for caching a function's output.

from joblib import Memory

# Set up a cache directory
location = 'your_cache_dir'
mem = Memory(location, verbose=1)

# Define a function to cache
import numpy as np

def square(x):
    return np.square(x)

# Cache the function
cached_square = mem.cache(square)

# Use the cached function
result = cached_square(np.array([1, 2, 3]))
print(result)