t-SNE optimization using scikit-learn-intelex

pyproject.toml

@@ -58,6 +58,7 @@ dependencies = [
     "h5py>=3.1",
     "tqdm",
     "scikit-learn>=0.24",
+    "scikit-learn-intelex>=2024.3.0",
     "statsmodels>=0.13",
     "patsy",
     "networkx>=2.7",

scanpy/tools/_tsne.py

@@ -156,14 +156,16 @@
                 )
             )
     if use_fast_tsne is False:  # In case MultiCore failed to import
+        from sklearnex import patch_sklearn,unpatch_sklearn
+        patch_sklearn()
         from sklearn.manifold import TSNE
 
         # unfortunately, sklearn does not allow to set a minimum number
         # of iterations for barnes-hut tSNE
         tsne = TSNE(**params_sklearn)
         logg.info("    using sklearn.manifold.TSNE")
         X_tsne = tsne.fit_transform(X)
-
+        unpatch_sklearn()
     # update AnnData instance
     adata.obsm["X_tsne"] = X_tsne  # annotate samples with tSNE coordinates
     adata.uns["tsne"] = {