diff --git a/pytorch_lightning/models/trainer.py b/pytorch_lightning/models/trainer.py
index 2655809c4a..c9c4d8504e 100644
--- a/pytorch_lightning/models/trainer.py
+++ b/pytorch_lightning/models/trainer.py
@@ -244,6 +244,30 @@ class Trainer(TrainerIO):
             '''
             raise ModuleNotFoundError(msg)
 
+        # restore training and model
+        self.restore_state_if_existing_checkpoint()
+
+    def restore_state_if_existing_checkpoint(self):
+        # restore trainer state and model if there is a weight for this experiment
+        last_epoch = -1
+        last_ckpt_name = None
+
+        # find last epoch
+        checkpoints = os.listdir(self.checkpoint_callback.filepath)
+        for name in checkpoints:
+            if '.ckpt' in name:
+                epoch = name.split('epoch_')[1]
+                epoch = re.sub('[^0-9]', '' ,epoch)
+
+                if epoch > last_epoch:
+                    last_epoch = epoch
+                    last_ckpt_name = name
+
+        # restore last checkpoint
+        last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)
+        self.restore(last_ckpt_path, self.on_gpu)
+        print(f'model and trainer restored from checkpoint: {last_ckpt_path}')
+
     @property
     def data_parallel(self):
         return self.use_dp or self.use_ddp
diff --git a/pytorch_lightning/root_module/model_saving.py b/pytorch_lightning/root_module/model_saving.py
index 0bde0943f4..d9c2e3a5c7 100644
--- a/pytorch_lightning/root_module/model_saving.py
+++ b/pytorch_lightning/root_module/model_saving.py
@@ -58,6 +58,25 @@ class TrainerIO(object):
         # do the actual save
         torch.save(checkpoint, filepath)
 
+    def restore(self, checkpoint_path, on_gpu):
+
+        if on_gpu:
+            checkpoint = torch.load(checkpoint_path)
+        else:
+            checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
+
+        # load training state (affects trainer only)
+        self.restore_training_state(checkpoint)
+
+        # load model state
+        model = self.__get_model()
+
+        # load the state_dict on the model automatically
+        model.load_state_dict(checkpoint['state_dict'])
+
+        # call model hook
+        model.on_hpc_load(checkpoint)
+
     def dump_checkpoint(self):
 
         checkpoint = {
@@ -198,15 +217,15 @@ class TrainerIO(object):
         # call model hook
         model.on_hpc_load(checkpoint)
 
-    def max_ckpt_in_folder(self, path):
+    def max_ckpt_in_folder(self, path, name_key='ckpt_'):
         files = os.listdir(path)
-        files = [x for x in files if 'ckpt_' in x]
+        files = [x for x in files if name_key in x]
         if len(files) == 0:
             return 0
 
         ckpt_vs = []
         for name in files:
-            name = name.split('ckpt_')[-1]
+            name = name.split(name_key)[-1]
             name = re.sub('[^0-9]', '', name)
             ckpt_vs.append(int(name))