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"""
==================================================
Checkpoint using joblib.Memory and joblib.Parallel
==================================================
This example illustrates how to cache intermediate computing results using
:class:`joblib.Memory` within :class:`joblib.Parallel`.
"""
###############################################################################
# Embed caching within parallel processing
###############################################################################
#
# It is possible to cache a computationally expensive function executed during
# a parallel process. ``costly_compute`` emulates such time consuming function.
import time
def costly_compute(data, column):
"""Emulate a costly function by sleeping and returning a column."""
time.sleep(2)
return data[column]
def data_processing_mean(data, column):
"""Compute the mean of a column."""
return costly_compute(data, column).mean()
###############################################################################
# Create some data. The random seed is fixed to generate deterministic data
# across Python session. Note that this is not necessary for this specific
# example since the memory cache is cleared at the end of the session.
import numpy as np
rng = np.random.RandomState(42)
data = rng.randn(int(1e4), 4)
###############################################################################
# It is first possible to make the processing without caching or parallel
# processing.
start = time.time()
results = [data_processing_mean(data, col) for col in range(data.shape[1])]
stop = time.time()
print('\nSequential processing')
print('Elapsed time for the entire processing: {:.2f} s'
.format(stop - start))
###############################################################################
# ``costly_compute`` is expensive to compute and it is used as an intermediate
# step in ``data_processing_mean``. Therefore, it is interesting to store the
# intermediate results from ``costly_compute`` using :class:`joblib.Memory`.
from joblib import Memory
location = './cachedir'
memory = Memory(location, verbose=0)
costly_compute_cached = memory.cache(costly_compute)
###############################################################################
# Now, we define ``data_processing_mean_using_cache`` which benefits from the
# cache by calling ``costly_compute_cached``
def data_processing_mean_using_cache(data, column):
"""Compute the mean of a column."""
return costly_compute_cached(data, column).mean()
###############################################################################
# Then, we execute the same processing in parallel and caching the intermediate
# results.
from joblib import Parallel, delayed
start = time.time()
results = Parallel(n_jobs=2)(
delayed(data_processing_mean_using_cache)(data, col)
for col in range(data.shape[1]))
stop = time.time()
print('\nFirst round - caching the data')
print('Elapsed time for the entire processing: {:.2f} s'
.format(stop - start))
###############################################################################
# By using 2 workers, the parallel processing gives a x2 speed-up compared to
# the sequential case. By executing again the same process, the intermediate
# results obtained by calling ``costly_compute_cached`` will be loaded from the
# cache instead of executing the function.
start = time.time()
results = Parallel(n_jobs=2)(
delayed(data_processing_mean_using_cache)(data, col)
for col in range(data.shape[1]))
stop = time.time()
print('\nSecond round - reloading from the cache')
print('Elapsed time for the entire processing: {:.2f} s'
.format(stop - start))
###############################################################################
# Reuse intermediate checkpoints
###############################################################################
#
# Having cached the intermediate results of the ``costly_compute_cached``
# function, they are reusable by calling the function. We define a new
# processing which will take the maximum of the array returned by
# ``costly_compute_cached`` instead of previously the mean.
def data_processing_max_using_cache(data, column):
"""Compute the max of a column."""
return costly_compute_cached(data, column).max()
start = time.time()
results = Parallel(n_jobs=2)(
delayed(data_processing_max_using_cache)(data, col)
for col in range(data.shape[1]))
stop = time.time()
print('\nReusing intermediate checkpoints')
print('Elapsed time for the entire processing: {:.2f} s'
.format(stop - start))
###############################################################################
# The processing time only corresponds to the execution of the ``max``
# function. The internal call to ``costly_compute_cached`` is reloading the
# results from the cache.
###############################################################################
# Clean-up the cache folder
###############################################################################
memory.clear(warn=False)
|
