Now that's interesting...

README.md

@@ -44,3 +44,11 @@ Ejemplo para grafico de difusion:
 $ . venv/bin/activate
 $ python src/experiments/difussion_2d.py 100
 ```
+
+Para algunos experimentos muy costosos, es posible ejecutarlos en paralelo si el hardware lo permite.
+En estos casos, se ejecutaran por defecto, usando el máximo número de CPUs físicos disponibles (sin contar SMT).
+Si se quiere asignar un número de cpus distinto a utilizar, hay que setear la variable de entorno `MAX_CPUS` antes de ejecutar el script:
+
+```shell
+$ MAX_CPUS=2 python src/experiments/3d.py
+```

src/classifier/misc.py

+import os
 import subprocess as sp
 
-
 def get_physical_cores():
-    list = set(map(int,
-                    sp.run(['lscpu', '-b', '-e=Core'], capture_output=True, text=True)
-                    .stdout.splitlines()[1:]))
+    try:
+        res = sp.run(['/usr/bin/lscpu', '-b', '-e=Core'], capture_output=True, text=True, check=True)
+
+        return len(set(map(int, res.stdout.splitlines()[1:])))
+    except (FileNotFoundError, sp.CalledProcessError):
+        return os.cpu_count() // 2 # type: ignore
 
-    return len(list)
\ No newline at end of file
+def config_for_multiple_cpus():
+    return os.getenv('MAX_CPUS', get_physical_cores())

src/experiments/3d.py

 import multiprocessing as mp
 from multiprocessing.pool import AsyncResult
-import os
 import pickle
 import numpy as np
 import matplotlib.pyplot as plt
@@ -8,10 +7,10 @@ from classifier.data_models import DataModelMatrices, get_vectorized_data_for, g
 from classifier.cv import cross_validations
 from classifier.knn import KNNPredictor
 from classifier.data_loader import *
-from classifier.misc import get_physical_cores
+from classifier.misc import config_for_multiple_cpus
 from classifier.pca import PCA
 
-MAX_PROCESSES  = int(os.getenv('MAX_CPUS', get_physical_cores()))
+MAX_PROCESSES  = config_for_multiple_cpus()
 MAXIMUM_K      = 100 # Neighbors
 P_MIN          = 50
 P_MAX          = 250

src/experiments/full_dataset_analisys.py

@@ -7,9 +7,10 @@ from classifier.data_models import DataModelMatrices, get_vectorized_data_for, g
 from classifier.cv import cross_validations
 from classifier.knn import KNNPredictor
 from classifier.data_loader import *
+from classifier.misc import config_for_multiple_cpus
 from classifier.pca import PCA
 
-MAX_PROCESSES  = 8
+MAX_PROCESSES  = config_for_multiple_cpus()
 MAXIMUM_K      = 100 # Neighbors
 P_MIN          = 50
 P_MAX          = 250