diff --git a/codes/models/steps/steps.py b/codes/models/steps/steps.py
index 7af98b31..8671e30c 100644
--- a/codes/models/steps/steps.py
+++ b/codes/models/steps/steps.py
@@ -6,6 +6,7 @@ import torch
 from apex import amp
 from collections import OrderedDict
 from .injectors import create_injector
+from apex.optimizers import FusedNovoGrad
 
 logger = logging.getLogger('base')
 
@@ -50,9 +51,13 @@ class ConfigurableStep(Module):
             else:
                 if self.env['rank'] <= 0:
                     logger.warning('Params [{:s}] will not optimize.'.format(k))
-        opt = torch.optim.Adam(optim_params, lr=self.step_opt['lr'],
-                               weight_decay=self.step_opt['weight_decay'],
-                               betas=(self.step_opt['beta1'], self.step_opt['beta2']))
+        if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adam':
+            opt = torch.optim.Adam(optim_params, lr=self.step_opt['lr'],
+                                   weight_decay=self.step_opt['weight_decay'],
+                                   betas=(self.step_opt['beta1'], self.step_opt['beta2']))
+        elif self.step_opt['optimizer'] == 'novograd':
+            opt = FusedNovoGrad(optim_params, lr=self.step_opt['lr'], weight_decay=self.step_opt['weight_decay'],
+                                   betas=(self.step_opt['beta1'], self.step_opt['beta2']))
         self.optimizers = [opt]
 
     # Returns all optimizers used in this step.