diff --git a/ecnet/server.py b/ecnet/server.py
index f07c537..87741b7 100644
--- a/ecnet/server.py
+++ b/ecnet/server.py
@@ -24,10 +24,6 @@
     resave_df, resave_model, save_config, save_df, save_project, train_model,\
     use_model, use_project
 
-# Stdlib imports
-from multiprocessing import set_start_method
-from os import name
-
 
 class Server:
 
@@ -52,9 +48,6 @@ def __init__(self, model_config: str='config.yml', prj_file: str=None,
 
         self._num_processes = num_processes
 
-        if name != 'nt':
-            set_start_method('spawn', force=True)
-
         if prj_file is not None:
             self._prj_name, self._num_pools, self._num_candidates, self._df,\
                 self._cf_file, self._vars = open_project(prj_file)
diff --git a/ecnet/tasks/training.py b/ecnet/tasks/training.py
index fc3a891..89e6ca8 100644
--- a/ecnet/tasks/training.py
+++ b/ecnet/tasks/training.py
@@ -13,8 +13,9 @@
 #
 
 # stdlib. imports
-from multiprocessing import Pool
+from multiprocessing import Pool, set_start_method
 from operator import itemgetter
+from os import name
 
 # ECNet imports
 from ecnet.utils.logging import logger
@@ -48,6 +49,9 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int,
         num_processes (int): number of concurrent processes used to train
     '''
 
+    if name != 'nt':
+        set_start_method('spawn', force=True)
+
     logger.log('info', 'Training {}x{} models'.format(
         num_pools, num_candidates
     ), call_loc='TRAIN')
diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py
index e3bc266..5f3af00 100644
--- a/ecnet/tasks/tuning.py
+++ b/ecnet/tasks/tuning.py
@@ -8,6 +8,10 @@
 # Contains functions/fitness functions for tuning hyperparameters
 #
 
+# stdlib. imports
+from multiprocessing import set_start_method
+from os import name
+
 # 3rd party imports
 from ecabc.abc import ABC
 
@@ -42,6 +46,9 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
         dict: tuned hyperparameters
     '''
 
+    if name != 'nt':
+        set_start_method('spawn', force=True)
+
     logger.log('info', 'Tuning architecture/learning hyperparameters',
                call_loc='TUNE')
     logger.log('debug', 'Arguments:\n\t| num_employers:\t{}\n\t| '