diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 78c698d5f2..20c0673d93 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -58,9 +58,9 @@ def reduce_distributed_output(output, nb_gpus):
 class Trainer(TrainerIO):
 
     def __init__(self,
-                 logger=None,
-                 checkpoint_callback=None,
-                 early_stop_callback=None,
+                 logger=True,
+                 checkpoint_callback=True,
+                 early_stop_callback=True,
                  default_save_path=None,
                  gradient_clip_val=0,
                  process_position=0,
@@ -126,7 +126,6 @@ class Trainer(TrainerIO):
         self.log_gpu_memory = log_gpu_memory
         self.gradient_clip_val = gradient_clip_val
         self.check_val_every_n_epoch = check_val_every_n_epoch
-        self.enable_early_stop = early_stop_callback is not None
         self.track_grad_norm = track_grad_norm
         self.on_gpu = gpus is not None and torch.cuda.is_available()
         self.process_position = process_position
@@ -176,24 +175,35 @@ class Trainer(TrainerIO):
 
         # configure early stop callback
         # creates a default one if none passed in
-        self.early_stop_callback = early_stop_callback
-        if self.early_stop_callback is None:
-            self.early_stop = EarlyStopping(
+        if early_stop_callback is True:
+            self.early_stop_callback = EarlyStopping(
                 monitor='val_loss',
                 patience=3,
                 verbose=True,
                 mode='min'
             )
+            self.enable_early_stop = True
+        elif not early_stop_callback:
+            self.early_stop_callback = None
+            self.enable_early_stop = False
+        else:
+            self.early_stop_callback = early_stop_callback
+            self.enable_early_stop = True
 
         # configure logger
-        self.logger = logger
-        if self.logger is None:
+        if logger is True:
+            # default logger
             self.logger = TestTubeLogger(
                 save_dir=self.default_save_path,
                 version=self.slurm_job_id,
                 name='lightning_logs'
             )
-        self.logger.rank = 0
+            self.logger.rank = 0
+        elif logger is False:
+            self.logger = None
+        else:
+            self.logger = logger
+            self.logger.rank = 0
 
         # configure checkpoint callback
         self.checkpoint_callback = checkpoint_callback
@@ -257,7 +267,7 @@ class Trainer(TrainerIO):
         User provided weights_saved_path
         Otherwise use os.getcwd()
         """
-        if self.checkpoint_callback is None:
+        if self.checkpoint_callback is True:
             # init a default one
             if isinstance(self.logger, TestTubeLogger):
                 ckpt_path = '{}/{}/version_{:04d}/{}'.format(
@@ -271,12 +281,15 @@ class Trainer(TrainerIO):
             self.checkpoint_callback = ModelCheckpoint(
                 filepath=ckpt_path
             )
+        elif self.checkpoint_callback is False:
+            self.checkpoint_callback = None
 
-        # set the path for the callbacks
-        self.checkpoint_callback.save_function = self.save_checkpoint
+        if self.checkpoint_callback:
+            # set the path for the callbacks
+            self.checkpoint_callback.save_function = self.save_checkpoint
 
-        # if checkpoint callback used, then override the weights path
-        self.weights_save_path = self.checkpoint_callback.filepath
+            # if checkpoint callback used, then override the weights path
+            self.weights_save_path = self.checkpoint_callback.filepath
 
         # if weights_save_path is still none here, set to current working dir
         if self.weights_save_path is None: