diff --git a/gklearn/utils/parallel.py b/gklearn/utils/parallel.py
index a1862c0..254df8a 100644
--- a/gklearn/utils/parallel.py
+++ b/gklearn/utils/parallel.py
@@ -7,60 +7,64 @@ Parallel aid functions.
 """
 import multiprocessing
 from multiprocessing import Pool
-from tqdm import tqdm
 import sys
+from gklearn.utils import get_iters
 
-def parallel_me(func, func_assign, var_to_assign, itr, len_itr=None, init_worker=None, 
-                glbv=None, method=None, n_jobs=None, chunksize=None, itr_desc='',
-                verbose=True):
-    '''
-    '''
-    if method == 'imap_unordered':
-        if glbv: # global varibles required.
-#            def init_worker(v_share):
-#                global G_var
-#                G_var = v_share
-            if n_jobs == None:
-                n_jobs = multiprocessing.cpu_count()
-            with Pool(processes=n_jobs, initializer=init_worker, 
-                      initargs=glbv) as pool:                
-                if chunksize is None:
-                    if len_itr < 100 * n_jobs:
-                        chunksize = int(len_itr / n_jobs) + 1
-                    else:
-                        chunksize = 100
-                for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
-                    desc=itr_desc, file=sys.stdout) if verbose else 
-                    pool.imap_unordered(func, itr, chunksize)):
-                    func_assign(result, var_to_assign)
-            pool.close()
-            pool.join()
-        else:
-            if n_jobs == None:
-                n_jobs = multiprocessing.cpu_count()
-            with Pool(processes=n_jobs) as pool:
-                if chunksize is None:
-                    if len_itr < 100 * n_jobs:
-                        chunksize = int(len_itr / n_jobs) + 1
-                    else:
-                        chunksize = 100
-                for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
-                    desc=itr_desc, file=sys.stdout) if verbose else 
-                    pool.imap_unordered(func, itr, chunksize)):
-                    func_assign(result, var_to_assign)
-            pool.close()
-            pool.join()
-                    
 
-def parallel_gm(func, Kmatrix, Gn, init_worker=None, glbv=None, 
-                method='imap_unordered', n_jobs=None, chunksize=None, 
-                verbose=True): # @todo: Gn seems not necessary.
-    from itertools import combinations_with_replacement
-    def func_assign(result, var_to_assign):
-        var_to_assign[result[0]][result[1]] = result[2]
-        var_to_assign[result[1]][result[0]] = result[2]
-    itr = combinations_with_replacement(range(0, len(Gn)), 2)
-    len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
-    parallel_me(func, func_assign, Kmatrix, itr, len_itr=len_itr,
-        init_worker=init_worker, glbv=glbv, method=method, n_jobs=n_jobs, 
-        chunksize=chunksize, itr_desc='Computing kernels', verbose=verbose)
\ No newline at end of file
+def parallel_me(func, func_assign, var_to_assign, itr, len_itr=None, init_worker=None,
+				glbv=None, method=None, n_jobs=None, chunksize=None, itr_desc='',
+				verbose=True):
+	'''
+	'''
+	if method == 'imap_unordered':
+		if glbv: # global varibles required.
+#			def init_worker(v_share):
+#				global G_var
+#				G_var = v_share
+			if n_jobs == None:
+				n_jobs = multiprocessing.cpu_count()
+			with Pool(processes=n_jobs, initializer=init_worker,
+					  initargs=glbv) as pool:
+				if chunksize is None:
+					if len_itr < 100 * n_jobs:
+						chunksize = int(len_itr / n_jobs) + 1
+					else:
+						chunksize = 100
+
+				iterator = get_iters(pool.imap_unordered(func, itr, chunksize),
+					desc=itr_desc, file=sys.stdout, length=len_itr,
+					verbose=(verbose >= 2))
+				for result in iterator:
+					func_assign(result, var_to_assign)
+			pool.close()
+			pool.join()
+		else:
+			if n_jobs == None:
+				n_jobs = multiprocessing.cpu_count()
+			with Pool(processes=n_jobs) as pool:
+				if chunksize is None:
+					if len_itr < 100 * n_jobs:
+						chunksize = int(len_itr / n_jobs) + 1
+					else:
+						chunksize = 100
+				iterator = get_iters(pool.imap_unordered(func, itr, chunksize),
+					desc=itr_desc, file=sys.stdout, length=len_itr,
+					verbose=(verbose >= 2))
+				for result in iterator:
+					func_assign(result, var_to_assign)
+			pool.close()
+			pool.join()
+
+
+def parallel_gm(func, Kmatrix, Gn, init_worker=None, glbv=None,
+				method='imap_unordered', n_jobs=None, chunksize=None,
+				verbose=True): # @todo: Gn seems not necessary.
+	from itertools import combinations_with_replacement
+	def func_assign(result, var_to_assign):
+		var_to_assign[result[0]][result[1]] = result[2]
+		var_to_assign[result[1]][result[0]] = result[2]
+	itr = combinations_with_replacement(range(0, len(Gn)), 2)
+	len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
+	parallel_me(func, func_assign, Kmatrix, itr, len_itr=len_itr,
+		init_worker=init_worker, glbv=glbv, method=method, n_jobs=n_jobs,
+		chunksize=chunksize, itr_desc='Computing kernels', verbose=verbose)
\ No newline at end of file