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Using LogisticRegresion with solver="saga" uses a thread-based backend if possible (see these lines)
However, I observed performance issues (potentially due to over-subscription?)
import itertools
import time
import numpy as np
from sklearn.externals.joblib import parallel_backend
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=100000, n_features=10, n_informative=5,
n_classes=10)
for backend, n_jobs in itertools.product(['threading', 'loky'], [1, 2, 4]):
with parallel_backend(backend):
clf = LogisticRegression(solver='saga', n_jobs=n_jobs)
t0 = time.time()
clf.fit(X, y)
total_time = time.time() - t0
print("backend: {:>9} n_jobs: {} total time: ({:.3f}, "
" n_iter: {})".format(backend, n_jobs, total_time,
np.mean(clf.n_iter_)))yields
backend: threading n_jobs: 1 total time: (7.078, n_iter: 14.6)
backend: threading n_jobs: 2 total time: (8.821, n_iter: 14.8)
backend: threading n_jobs: 4 total time: (30.201, n_iter: 14.8)
backend: loky n_jobs: 1 total time: (7.394, n_iter: 14.3)
backend: loky n_jobs: 2 total time: (5.375, n_iter: 15.0)
backend: loky n_jobs: 4 total time: (3.994, n_iter: 15.2)I traced the number of iterations to make sure it is not simply a matter of convergence.
Monitoring CPU usage showed a big (~50%) proportion of system calls.
ping @ogrisel @jeremiedbb
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