修复CPU与GPU不能同时训练的BUG，退出时自动存档

cuda_train.cmd

@@ -1 +1 @@
-python train.py --load_model --batch_size 4 --learning_rate 0.0003 --enable_onnx
+python train.py --load_model --batch_size 4 --learning_rate 0.0003 --enable_onnx --num_actors 1

douzero/dmc/arguments.py

@@ -11,7 +11,7 @@ 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=30, type=int,
+parser.add_argument('--onnx_sync_interval', default=120, type=int,
                     help='Time interval (in seconds) at which to sync the onnx model')
 parser.add_argument('--actor_device_cpu', action='store_true',
                     help='Use CPU as actor device')

douzero/dmc/dmc.py

@@ -87,7 +87,7 @@ def train(flags):
     if flags.actor_device_cpu:
         device_iterator = ['cpu']
     else:
-        device_iterator = range(flags.num_actor_devices) #[0, 'cpu']
+        device_iterator = [0, 'cpu'] #range(flags.num_actor_devices) #[0, 'cpu']
         assert flags.num_actor_devices <= len(flags.gpu_devices.split(',')), 'The number of actor devices can not exceed the number of available devices'
 
     # Initialize actor models
@@ -282,6 +282,7 @@ def train(flags):
                      pprint.pformat(stats))
 
     except KeyboardInterrupt:
+        checkpoint(frames)
         return
     else:
         for thread in threads:

douzero/dmc/models.py

@@ -315,18 +315,19 @@ class Model:
                 'landlord_down': None
             }
 
-    def set_onnx_model(self):
+    def set_onnx_model(self, device='cpu'):
         positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
         for position in positions:
             model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.savedir, self.flags.xpid, position))
-            self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+            if device == 'cpu':
+                self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
+            else:
+                self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
 
     def get_onnx_params(self, position):
         self.models[position].get_onnx_params(self.device)
 
     def forward(self, position, z, x, return_value=False, flags=None, debug=False):
-        if self.flags.enable_onnx and len(self.onnx_models) == 0:
-                self.set_onnx_model()
         model = self.onnx_models[position]
         if model is None:
             model = self.models[position]

douzero/dmc/utils.py

@@ -112,8 +112,7 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
             # print("posi", position)
             if flags.enable_onnx and onnx_frame.value != last_onnx_frame:
                 last_onnx_frame = onnx_frame.value
-                for p in positions:
-                    model.set_onnx_model()
+                model.set_onnx_model(device)
 
             while True:
                 if len(obs['legal_actions']) > 1: