diff --git a/examples/new_project_templates/single_gpu_node_template.py b/examples/new_project_templates/single_gpu_node_template.py
new file mode 100644
index 0000000000..a03be32f17
--- /dev/null
+++ b/examples/new_project_templates/single_gpu_node_template.py
@@ -0,0 +1,112 @@
+"""
+Runs a model on a single node across N-gpus.
+"""
+import os
+import sys
+import numpy as np
+from time import sleep
+import torch
+
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
+from pytorch_lightning.models.trainer import Trainer
+from pytorch_lightning.utils.arg_parse import add_default_args
+
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+
+SEED = 2334
+torch.manual_seed(SEED)
+np.random.seed(SEED)
+
+from lightning_module_template import LightningTemplateModel
+
+
+def main(hparams):
+    """
+    Main training routine specific for this project
+    :param hparams:
+    :return:
+    """
+    # ------------------------
+    # 1 INIT LIGHTNING MODEL
+    # ------------------------
+    print('loading model...')
+    model = LightningTemplateModel(hparams)
+    print('model built')
+
+    # ------------------------
+    # 2 INIT TEST TUBE EXP
+    # ------------------------
+
+    # init experiment
+    exp = Experiment(
+        name=hyperparams.experiment_name,
+        save_dir=hyperparams.test_tube_save_path,
+        autosave=False,
+        description='test demo'
+    )
+
+    exp.argparse(hparams)
+    exp.save()
+
+    # ------------------------
+    # 3 DEFINE CALLBACKS
+    # ------------------------
+    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
+    early_stop = EarlyStopping(
+        monitor='val_acc',
+        patience=3,
+        verbose=True,
+        mode='max'
+    )
+
+    checkpoint = ModelCheckpoint(
+        filepath=model_save_path,
+        save_best_only=True,
+        verbose=True,
+        monitor='val_loss',
+        mode='min'
+    )
+
+    # ------------------------
+    # 4 INIT TRAINER
+    # ------------------------
+    trainer = Trainer(
+        experiment=exp,
+        checkpoint_callback=checkpoint,
+        early_stop_callback=early_stop,
+        gpus=hparams.gpus,
+    )
+
+    # ------------------------
+    # 5 START TRAINING
+    # ------------------------
+    trainer.fit(model)
+
+
+if __name__ == '__main__':
+
+    # dirs
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
+    checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
+    test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
+
+    # although we user hyperOptParser, we are using it only as argparse right now
+    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
+
+    # gpu args
+    parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
+
+    # allow model to overwrite or extend args
+    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
+    hyperparams = parser.parse_args()
+
+    # ---------------------
+    # RUN TRAINING
+    # ---------------------
+    # run on HPC cluster
+    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
+    main(hyperparams)
diff --git a/pytorch_lightning/models/trainer.py b/pytorch_lightning/models/trainer.py
index fe448972ae..f2252186cf 100644
--- a/pytorch_lightning/models/trainer.py
+++ b/pytorch_lightning/models/trainer.py
@@ -302,8 +302,7 @@ class Trainer(TrainerIO):
         world_size = self.nb_gpu_nodes * len(self.data_parallel_device_ids)
 
         # set up server using proc 0's ip address
-        ip_tables_dir = os.path.join(self.cluster.log_path, 'ip_tables')
-        ip = self.__get_root_node_ip(self.proc_rank, self.nb_gpu_nodes, ip_tables_dir)
+        ip = self.__get_root_node_ip(self.proc_rank, self.nb_gpu_nodes)
         dist.init_process_group("nccl", init_method=f'tcp://{ip}:12001', rank=self.proc_rank, world_size=world_size)
         print(f"GPU: {gpu_nb} - Rank: {self.proc_rank}")
 
@@ -315,7 +314,7 @@ class Trainer(TrainerIO):
         # continue training routine
         self.__run_pretrain_routine(model)
 
-    def __get_root_node_ip(self, world_gpu_nb, nb_gpu_nodes, ip_file_dir):
+    def __get_root_node_ip(self, world_gpu_nb, nb_gpu_nodes):
         """
         Resolves the ip address of proc 0.
         Proc 0 writes address to a file. Every other process waits until the ip is available before it starts
@@ -329,6 +328,9 @@ class Trainer(TrainerIO):
         if nb_gpu_nodes == 1:
             return '127.0.0.1'
 
+        # where to store ip_table
+        ip_file_dir = os.path.join(self.cluster.log_path, 'ip_tables')
+
         # the first gpu in the world becomes the host
         # this is based on its global rank
         # it communicates its ip by saving an ip_table to the slurm cluster logging dir