diff --git a/douzero/dmc/arguments.py b/douzero/dmc/arguments.py
index 588c23f..496fe4c 100644
--- a/douzero/dmc/arguments.py
+++ b/douzero/dmc/arguments.py
@@ -11,6 +11,8 @@ parser.add_argument('--objective', default='adp', type=str, choices=['adp', 'wp'
                     help='Use ADP or WP as reward (default: ADP)')    
 
 # Training settings
+parser.add_argument('--onnx_sync_interval', default=5, type=int,
+                    help='Time interval (in minutes) at which to sync the onnx model')
 parser.add_argument('--actor_device_cpu', action='store_true',
                     help='Use CPU as actor device')
 parser.add_argument('--gpu_devices', default='0', type=str,
diff --git a/douzero/dmc/dmc.py b/douzero/dmc/dmc.py
index 781ef97..53ff12e 100644
--- a/douzero/dmc/dmc.py
+++ b/douzero/dmc/dmc.py
@@ -130,6 +130,25 @@ def train(flags):
     frames, stats = 0, {k: 0 for k in stat_keys}
     position_frames = {'landlord':0, 'landlord_up':0, 'landlord_front':0, 'landlord_down':0}
 
+    def sync_onnx_model(frames):
+        for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
+            if flags.enable_onnx and position:
+                model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, position)
+                onnx_params = learner_model.get_model(position)\
+                    .get_onnx_params(torch.device('cpu') if flags.training_device == 'cpu' else torch.device('cuda:' + flags.training_device))
+                torch.onnx.export(
+                    learner_model.get_model(position),
+                    onnx_params['args'],
+                    model_path,
+                    export_params=True,
+                    opset_version=10,
+                    do_constant_folding=True,
+                    input_names=onnx_params['input_names'],
+                    output_names=onnx_params['output_names'],
+                    dynamic_axes=onnx_params['dynamic_axes']
+                )
+        onnx_frame.value = frames
+
     # Load models if any
     if flags.load_model and os.path.exists(checkpointpath):
         checkpoint_states = torch.load(
@@ -145,6 +164,7 @@ def train(flags):
 
         frames = checkpoint_states["frames"]
         position_frames = checkpoint_states["position_frames"]
+        sync_onnx_model(frames)
         log.info(f"Resuming preempted job, current stats:\n{stats}")
 
     # Starting actor processes
@@ -217,29 +237,13 @@ def train(flags):
             model_weights_dir = os.path.expandvars(os.path.expanduser(
                 '%s/%s/%s' % (flags.savedir, flags.xpid, "general_" + position + '_' + str(frames) + '.ckpt')))
             torch.save(learner_model.get_model(position).state_dict(), model_weights_dir)
-            if flags.enable_onnx and position:
-                model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, position)
-                onnx_params = learner_model.get_model(position)\
-                    .get_onnx_params(torch.device('cpu') if flags.training_device == 'cpu' else torch.device('cuda:' + flags.training_device))
-                torch.onnx.export(
-                    learner_model.get_model(position),
-                    onnx_params['args'],
-                    model_path,
-                    export_params=True,
-                    opset_version=10,
-                    do_constant_folding=True,
-                    input_names=onnx_params['input_names'],
-                    output_names=onnx_params['output_names'],
-                    dynamic_axes=onnx_params['dynamic_axes']
-                )
-        onnx_frame.value = frames
         shutil.move(checkpointpath + '.new', checkpointpath)
 
-
     fps_log = []
     timer = timeit.default_timer
     try:
         last_checkpoint_time = timer() - flags.save_interval * 60
+        last_onnx_sync_time = timer()
         while frames < flags.total_frames:
             start_frames = frames
             position_start_frames = {k: position_frames[k] for k in position_frames}
@@ -249,6 +253,11 @@ def train(flags):
             if timer() - last_checkpoint_time > flags.save_interval * 60:
                 checkpoint(frames)
                 last_checkpoint_time = timer()
+
+            if timer() - last_onnx_sync_time > flags.onnx_sync_interval * 60:
+                sync_onnx_model(frames)
+                last_onnx_sync_time = timer()
+
             end_time = timer()
 
             fps = (frames - start_frames) / (end_time - start_time)
diff --git a/requirements.txt b/requirements.txt
index 2dfca1a..cab6fd3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,4 +4,4 @@ gitdb2
 rlcard
 psutil
 onnx
-onnxruntime-gpu
+onnxruntime-gpu==1.7