joblib 0.13.2

视野

我们的愿景是提供工具，以轻松实现更好的性能和 处理长时间运行的作业时的再现性。

• Avoid computing twice the same thing: code is rerun over an over, for instance when prototyping computational-heavy jobs (as in scientific development), but hand-crafted solution to alleviate this issue is error-prone and often leads to unreproducible results
• Persist to disk transparently: persisting in an efficient way 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在离开代码和流时解决这些问题 尽可能不修改控件（没有框架，没有新的范例）。

主要功能

1. 输出值的透明快速磁盘缓存：备忘录或 为python函数创建类似的功能 任意的python对象，包括非常大的numpy数组。分开 域逻辑或算法的持久性和流执行逻辑 通过将操作作为一组定义良好的步骤编写代码 输入和输出：python函数。joblib可以保存 计算到磁盘并仅在必要时重新运行：

   >>> from joblib import Memory
   >>> cachedir = 'your_cache_dir_goes_here'
   >>> mem = Memory(cachedir)
   >>> import numpy as np
   >>> a = np.vander(np.arange(3)).astype(np.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 - 0...s, 0.0min
   
   >>> c = square(a)
   >>> # The above call did not trigger an evaluation
   

2. 让人难堪的并行助手：使其易于写可读 并行代码并快速调试：

   >>> 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. 快速压缩持久性：pickle的替代品 有效地处理包含大数据的python对象（ joblib.dump&；joblib.load）。