Ë s‰Kg ãó,—dZdZddlZddlmZddlmZddlmZddlmZdd lm Z dd lm Z dd l m Z dd l mZdd l mZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZgd¢Zej:j=dd«y)a­ Joblib is a set of tools to provide **lightweight pipelining in Python**. In particular: 1. transparent disk-caching of functions and lazy re-evaluation (memoize pattern) 2. easy simple parallel computing Joblib is optimized to be **fast** and **robust** on large data in particular and has specific optimizations for `numpy` arrays. It is **BSD-licensed**. ==================== =============================================== **Documentation:** https://joblib.readthedocs.io **Download:** https://pypi.python.org/pypi/joblib#downloads **Source code:** https://github.com/joblib/joblib **Report issues:** https://github.com/joblib/joblib/issues ==================== =============================================== Vision -------- The vision is to provide tools to easily achieve better performance and reproducibility when working with long running jobs. * **Avoid computing the same thing twice**: code is often rerun again and again, for instance when prototyping computational-heavy jobs (as in scientific development), but hand-crafted solutions to alleviate this issue are error-prone and often lead to unreproducible results. * **Persist to disk transparently**: efficiently persisting arbitrary objects containing large data is hard. Using joblib's caching mechanism avoids hand-written persistence and implicitly links the file on disk to the execution context of the original Python object. As a result, joblib's persistence is good for resuming an application status or computational job, eg after a crash. Joblib addresses these problems while **leaving your code and your flow control as unmodified as possible** (no framework, no new paradigms). Main features ------------------ 1) **Transparent and fast disk-caching of output value:** a memoize or make-like functionality for Python functions that works well for arbitrary Python objects, including very large numpy arrays. Separate persistence and flow-execution logic from domain logic or algorithmic code by writing the operations as a set of steps with well-defined inputs and outputs: Python functions. Joblib can save their computation to disk and rerun it only if necessary:: >>> from joblib import Memory >>> cachedir = 'your_cache_dir_goes_here' >>> mem = Memory(cachedir) >>> import numpy as np >>> a = np.vander(np.arange(3)).astype(float) >>> square = mem.cache(np.square) >>> b = square(a) # doctest: +ELLIPSIS ______________________________________________________________________... [Memory] Calling square... square(array([[0., 0., 1.], [1., 1., 1.], [4., 2., 1.]])) _________________________________________________...square - ...s, 0.0min >>> c = square(a) >>> # The above call did not trigger an evaluation 2) **Embarrassingly parallel helper:** to make it easy to write readable parallel code and debug it quickly:: >>> from joblib import Parallel, delayed >>> from math import sqrt >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10)) [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] 3) **Fast compressed Persistence**: a replacement for pickle to work efficiently on Python objects containing large data ( *joblib.dump* & *joblib.load* ). .. >>> import shutil ; shutil.rmtree(cachedir) z1.4.2éNé)ÚMemory)ÚMemorizedResult)Úregister_store_backend)Ú expires_after)Ú PrintTime)ÚLogger)Úhash)Údump)Úload)Úregister_compressor)ÚParallel)Údelayed)Ú cpu_count)Úregister_parallel_backend)Úparallel_backend)Úparallel_config)Úeffective_n_jobs)Úwrap_non_picklable_objects)rrrr r r r rrrrrrrrr rrÚKMP_INIT_AT_FORKÚFALSE)Ú__doc__Ú __version__ÚosÚmemoryrrrrÚloggerrr Úhashingr Ú numpy_pickler r Ú compressorr ÚparallelrrrrrrrÚ_cloudpickle_wrapperrÚ__all__ÚenvironÚ setdefault©óúS/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/joblib/__init__.pyÚr(slðñZðX€ ó åÝ#Ý*Ý!åÝååÝå+åÝÝÝ/Ý&Ý%Ý&Ý<ò <€ð‡ ×ÑÐ(¨'Õ2r&