From 99549c20e9173b536ac816ab683bc13025f182a2 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Tue, 5 May 2020 11:07:53 +0200 Subject: fix use of threads and n_jobs in Parallel --- src/python/gudhi/point_cloud/knn.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 07553d6d..34e80b5d 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -200,8 +200,8 @@ class KNearestNeighbors: from joblib import Parallel, delayed, effective_n_jobs from sklearn.utils import gen_even_slices - slices = gen_even_slices(len(X), effective_n_jobs(-1)) - parallel = Parallel(backend="threading", n_jobs=-1) + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) if self.params.get("sort_results", True): def func(M): @@ -242,8 +242,8 @@ class KNearestNeighbors: else: func = lambda M: numpy.partition(M, k - 1)[:, 0:k] - slices = gen_even_slices(len(X), effective_n_jobs(-1)) - parallel = Parallel(backend="threading", n_jobs=-1) + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) distances = numpy.concatenate(parallel(delayed(func)(X[s]) for s in slices)) return distances return None -- cgit v1.2.3