simplify trainer output

pytorch_lightning/models/trainer.py

@@ -288,9 +288,6 @@ class Trainer(TrainerIO):
     # MODEL TRAINING
     # -----------------------------
     def fit(self, model):
-        # CHOOSE OPTIMIZER
-        # filter out the weights that were done on gpu so we can load on good old cpus
-        self.optimizers = model.configure_optimizers()
 
         # when using gpus, first thing we do is spawn a new process between each worker
         # applies to single gpu, multi-gpu and multi-nodes
@@ -298,6 +295,10 @@ class Trainer(TrainerIO):
             self.experiment = self.experiment.get_meta_copy()
             mp.spawn(self.dp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))
         else:
+            # CHOOSE OPTIMIZER
+            # filter out the weights that were done on gpu so we can load on good old cpus
+            self.optimizers = model.configure_optimizers()
+
             # run through amp wrapper
             if self.use_amp:
                 # An example
@@ -338,10 +339,16 @@ class Trainer(TrainerIO):
         ip = self.__get_root_node_ip(self.proc_rank, self.nb_gpu_nodes)
         self.__init_tcp_connection(ip)
 
+        # CHOOSE OPTIMIZER
+        # filter out the weights that were done on gpu so we can load on good old cpus
+        self.optimizers = model.configure_optimizers()
+
+        # MODEL
         # copy model to each gpu
         torch.cuda.set_device(gpu_nb)
         model.cuda(gpu_nb)
 
+        # AMP
         # run through amp wrapper before going to distributed DP
         if self.use_amp:
             # An example