The Memory class

Usecase

The Memory class defines a context for lazy evaluation of function, by storing the results to the disk, and not rerunning the function twice for the same arguments.

It works by explicitely saving the output to a file and it is designed to work with non-hashable and potentially large input and output data types such as numpy arrays.

A simple example:

First we create a temporary directory, for the cache:

>>> from tempfile import mkdtemp
>>> cachedir = mkdtemp()

We can instanciate a memory context, using this cache directory:

>>> from joblib import Memory
>>> memory = Memory(cachedir=cachedir)

Then we can decorate a function to be cached in this context:

>>> @memory.cache
... def f(x):
...     print 'Running f(%s)' % x
...     return x

When we call this function twice with the same argument, it does not get executed the second time, an the output is loaded from the pickle file:

>>> print f(1)
Running f(1)
1
>>> print f(1)
1

However, when we call it a third time, with a different argument, the output gets recomputed:

>>> print f(2)
Running f(2)
2

Comparison with memoize

The memoize decorator (http://code.activestate.com/recipes/52201/) caches in memory all the inputs and outputs of a function call. It can thus avoid running twice the same function, but with a very small overhead. However, it compares input objects with those in cache on each call. As a result, for big objects there is a huge overhead. More over this approach does not work with numpy arrays, or other objects subject to non-significant fluctuations. Finally, using memoize with large object will consume all the memory, where with Memory, objects are persisted to the disk, using a persister optimized for speed and memory usage (joblib.dump()).

In short, memoize is best suited for functions with “small” input and output objects, whereas Memory is best suited for functions with complex input and output objects, and agressive persistence to the disk.

Using with numpy

The original motivation behind the Memory context was to be able to a memoize-like pattern on numpy arrays. Memory uses fast cryptographic hashing of the input arguments to check if they have been computed;

An example

We define two functions, the first with a number as an argument, outputting an array, used by the second one. We decorate both functions with Memory.cache:

>>> import numpy as np

>>> @memory.cache
... def f(x):
...     print 'A long-running calculation, with parameter', x
...     return np.hamming(x)

>>> @memory.cache
... def g(x):
...     print 'A second long-running calculation, using f(x)'
...     return np.vander(x)

If we call the function g with the array created by the same call to f, g is not re-run:

>>> a = f(3)
A long-running calculation, with parameter 3
>>> a
array([ 0.08,  1.  ,  0.08])
>>> f(3)
array([ 0.08,  1.  ,  0.08])
>>> b = g(a)
A second long-running calculation, using f(x)
>>> b2 = g(a)
>>> b2
array([[ 0.0064,  0.08  ,  1.    ],
       [ 1.    ,  1.    ,  1.    ],
       [ 0.0064,  0.08  ,  1.    ]])
>>> np.allclose(b, b2)
True

Using memmapping

To speed up cache looking of large numpy arrays, you can load them using memmapping (memory mapping):

>>> memory2 = Memory(cachedir=cachedir, mmap_mode='r')
>>> square = memory2.cache(np.square)
>>> a = np.vander(np.arange(3))
>>> square(a)
array([[ 0,  0,  1],
       [ 1,  1,  1],
       [16,  4,  1]])

If the square function is called with the same input argument, its return value is loaded from the disk using memmapping:

>>> square(a)
memmap([[ 0,  0,  1],
       [ 1,  1,  1],
       [16,  4,  1]])

Note

If the memory mapping mode used was ‘r’, as in the above example, the array will be read only, and will be impossible to modified in place.

On the other hand, using ‘r+’ or ‘w+’ will enable modification of the array, but will propagate these modification to the disk, which will corrupt the cache. If you want modification of the array in memory, we suggest you use the ‘c’ mode: copy on write.

Warning

Because in the first run the array is a plain ndarray, and in the second run the array is a memmap, you can have side effects of using the Memory, especially when using mmap_mode=’r’ as the array is writable in the first run, and not the second.

Gotchas

• Function cache is identified by the function’s name. Thus if you have the same name to different functions, their cache will override each-others, and you well get unwanted re-run:

  >>> @memory.cache
  ... def f(x):
  ...     print 'Running f(%s)' % x
  
  >>> g = f
  
  >>> @memory.cache
  ... def f(x):
  ...     print 'Running a different f(%s)' % x
  
  >>> f(1)
  Running a different f(1)
  >>> g(1)
  Running f(1)
  >>> f(1)
  Running a different f(1)
  >>> g(1)
  Running f(1)
  

  Beware that all lambda functions have the same name:

  >>> def my_print(x):
  ...     print x
  
  >>> f = memory.cache(lambda : my_print(1))
  >>> g = memory.cache(lambda : my_print(2))
  
  >>> f()
  1
  >>> f()
  >>> g()
  2
  >>> g()
  >>> f()
  1
  

• memory cannot be used on some complex objects, eg a callable object with a __call__ method.

  Howevers, it works on numpy ufuncs:

  >>> sin = memory.cache(np.sin)
  >>> print sin(0)
  0.0
  

Reference documentation of the Memory class

class joblib.memory.Memory(cachedir, save_npy=True, mmap_mode=None, debug=False)

A context object for caching a function’s return value each time it is called with the same input arguments.

All values are cached on the filesystem, in a deep directory structure.

see The Memory class

__init__(cachedir, save_npy=True, mmap_mode=None, debug=False)

Parameters:

cachedir: string :

The path of the base directory to use as a data store

save_npy: boolean, optional :

If True, numpy arrays are saved outside of the pickle files in the cache, as npy files.

mmap_mode: {None, ‘r+’, ‘r’, ‘w+’, ‘c’}, optional :

The memmapping mode used when loading from cache numpy arrays. See numpy.load for the meaning of the arguments. Only used if save_npy was true when the cache was created.

debug: boolean, optional :

If True, debug messages will be issued as functions are revaluated.

cache(func)

Decorates the given function func to only compute its return value for input arguments not cached on disk.

eval(func, *args, **kwargs)

Eval function func with arguments *args and **kwargs, in the context of the memory.

This method works similarly to the builtin apply, except that the function is called only if the cache is not up to date.

clear()

Erase the complete cache directory.