Unlock the power of parallel Python programming with our in-depth Joblib tutorial. Learn about job caching and efficient Python parallel processing techniques.
pip install joblibWhat is joblib and why use it?
Key features and capabilities
Installation instructions
Basic usage examples
Common use cases
Best practices and tips
import joblib\nfrom joblib import Parallel, delayed\n\n# Define a simple function\ndef square(x):\n return x * x\n\n# Use joblib to execute in parallel\nsquared_numbers = Parallel(n_jobs=2)(delayed(square)(i) for i in range(10))\nprint(squared_numbers)
from joblib import Memory\nimport numpy as np\n\n# Create a memory object for caching\nmemory = Memory('/tmp/joblib_cache', verbose=0)\n\n@memory.cache\ndef expensive_computation(x):\n print('Computing...')\n return np.exp(x)\n\n# Call the function\nresult = expensive_computation(10)\nprint(result)\n\n# Call the function again with the same argument\n# This time the result will be fetched from the cache\nresult = expensive_computation(10)\nprint(result)ParallelExecute a function in parallel using the specified number of jobs.
delayedWraps the function call to be executed lazily in parallel.
MemoryCaches the result of expensive function calls.