--- a
+++ b/opengait/utils/msg_manager.py
@@ -0,0 +1,121 @@
+import time
+import torch
+
+import numpy as np
+import torchvision.utils as vutils
+import os.path as osp
+from time import strftime, localtime
+
+from torch.utils.tensorboard import SummaryWriter
+from .common import is_list, is_tensor, ts2np, mkdir, Odict, NoOp
+import logging
+
+
+class MessageManager:
+    def __init__(self):
+        self.info_dict = Odict()
+        self.writer_hparams = ['image', 'scalar']
+        self.time = time.time()
+
+    def init_manager(self, save_path, log_to_file, log_iter, iteration=0):
+        self.iteration = iteration
+        self.log_iter = log_iter
+        mkdir(osp.join(save_path, "summary/"))
+        self.writer = SummaryWriter(
+            osp.join(save_path, "summary/"), purge_step=self.iteration)
+        self.init_logger(save_path, log_to_file)
+
+    def init_logger(self, save_path, log_to_file):
+        # init logger
+        self.logger = logging.getLogger('opengait')
+        self.logger.setLevel(logging.INFO)
+        self.logger.propagate = False
+        formatter = logging.Formatter(
+            fmt='[%(asctime)s] [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+        if log_to_file:
+            mkdir(osp.join(save_path, "logs/"))
+            vlog = logging.FileHandler(
+                osp.join(save_path, "logs/", strftime('%Y-%m-%d-%H-%M-%S', localtime())+'.txt'))
+            vlog.setLevel(logging.INFO)
+            vlog.setFormatter(formatter)
+            self.logger.addHandler(vlog)
+
+        console = logging.StreamHandler()
+        console.setFormatter(formatter)
+        console.setLevel(logging.DEBUG)
+        self.logger.addHandler(console)
+
+    def append(self, info):
+        for k, v in info.items():
+            v = [v] if not is_list(v) else v
+            v = [ts2np(_) if is_tensor(_) else _ for _ in v]
+            info[k] = v
+        self.info_dict.append(info)
+
+    def flush(self):
+        self.info_dict.clear()
+        self.writer.flush()
+
+    def write_to_tensorboard(self, summary):
+
+        for k, v in summary.items():
+            module_name = k.split('/')[0]
+            if module_name not in self.writer_hparams:
+                self.log_warning(
+                    'Not Expected --Summary-- type [{}] appear!!!{}'.format(k, self.writer_hparams))
+                continue
+            board_name = k.replace(module_name + "/", '')
+            writer_module = getattr(self.writer, 'add_' + module_name)
+            v = v.detach() if is_tensor(v) else v
+            v = vutils.make_grid(
+                v, normalize=True, scale_each=True) if 'image' in module_name else v
+            if module_name == 'scalar':
+                try:
+                    v = v.mean()
+                except:
+                    v = v
+            writer_module(board_name, v, self.iteration)
+
+    def log_training_info(self):
+        now = time.time()
+        string = "Iteration {:0>5}, Cost {:.2f}s".format(
+            self.iteration, now-self.time, end="")
+        for i, (k, v) in enumerate(self.info_dict.items()):
+            if 'scalar' not in k:
+                continue
+            k = k.replace('scalar/', '').replace('/', '_')
+            end = "\n" if i == len(self.info_dict)-1 else ""
+            string += ", {0}={1:.4f}".format(k, np.mean(v), end=end)
+        self.log_info(string)
+        self.reset_time()
+
+    def reset_time(self):
+        self.time = time.time()
+
+    def train_step(self, info, summary):
+        self.iteration += 1
+        self.append(info)
+        if self.iteration % self.log_iter == 0:
+            self.log_training_info()
+            self.flush()
+            self.write_to_tensorboard(summary)
+
+    def log_debug(self, *args, **kwargs):
+        self.logger.debug(*args, **kwargs)
+
+    def log_info(self, *args, **kwargs):
+        self.logger.info(*args, **kwargs)
+
+    def log_warning(self, *args, **kwargs):
+        self.logger.warning(*args, **kwargs)
+
+
+msg_mgr = MessageManager()
+noop = NoOp()
+
+
+def get_msg_mgr():
+    if torch.distributed.get_rank() > 0:
+        return noop
+    else:
+        return msg_mgr