--- a
+++ b/simdeep/simdeep_utils.py
@@ -0,0 +1,121 @@
+from simdeep.config import PATH_TO_SAVE_MODEL
+
+from os.path import isfile
+from os.path import isdir
+
+from os import mkdir
+
+# from sys import version_info
+
+# if version_info > (3, 0, 0):
+#     import pickle as cPickle
+# else:
+#     import cPickle
+
+import dill
+
+from time import time
+
+def save_model(boosting, path_to_save_model=PATH_TO_SAVE_MODEL):
+    """ """
+    if not isdir(path_to_save_model):
+        mkdir(path_to_save_model)
+
+    boosting._convert_logs()
+
+    t = time()
+
+    with open('{0}/{1}.pickle'.format(
+            path_to_save_model,
+            boosting._project_name), 'wb') as f_pick:
+        dill.dump(boosting, f_pick)
+
+    print('model saved in %2.1f s at %s/%s.pickle' % (
+        time() - t, path_to_save_model, boosting._project_name))
+
+
+def load_model(project_name, path_model=PATH_TO_SAVE_MODEL):
+    """ """
+    t = time()
+    project_name = project_name.replace('.pickle', '') + '.pickle'
+
+    assert(isfile('{0}/{1}'.format(path_model, project_name)))
+
+    with open('{0}/{1}'.format(path_model, project_name), 'rb') as f_pick:
+        boosting = dill.load(f_pick)
+
+    print('model loaded in %2.1f s' % (time() - t))
+
+    return boosting
+
+
+def metadata_usage_type(value):
+    """ """
+    if value not in {None,
+                     False,
+                     'labels',
+                     'new-features',
+                     'test-labels',
+                     'all', True}:
+        raise Exception(
+            "metadata_usage_type: {0} should be from the following choices:" \
+            " [None, False, 'labels', 'new-features', 'all', True]" \
+            .format(value))
+
+    if value == True:
+        return 'all'
+
+    return value
+
+
+def feature_selection_usage_type(value):
+    """ """
+    if value not in {'individual',
+                     'lasso',
+                     None}:
+        raise Exception(
+            "feature_selection_usage_type: {0} should be from the following choices:" \
+            " ['individual', 'lasso', None]" \
+            .format(value))
+
+    return value
+
+
+def load_labels_file(path_labels, sep="\t"):
+    """
+    """
+    labels_dict = {}
+
+    for line in open(path_labels):
+        split = line.strip().split(sep)
+
+        if len(split) < 2:
+            raise Exception(
+                '## Errorfor file in load_labels_file: {0} for line{1}' \
+                ' line cannot be splitted in more than 2'.format(
+                    line, path_labels))
+
+        patient, label = split[0], split[1]
+
+        try:
+            label = int(float(label))
+        except Exception:
+            raise Exception(
+                '## Error: in load_labels_file {0} for line {1}' \
+                'labels should be an int'.format(
+                    path_labels, line))
+
+        if len(split) > 2:
+            try:
+                proba = float(split[2])
+            except Exception:
+                raise Exception(
+                    '## Error: in load_labels_file {0} for line {1}' \
+                    'label proba in column 3 should be a float'.format(
+                        path_labels, line))
+            else:
+                proba = label
+
+        labels_dict[patient] = (label, proba)
+
+    return labels_dict