diff --git a/.circleci/config.yml b/.circleci/config.yml
index 2237e39423bb0..1cd6ac7a4d27a 100755
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -22,7 +22,7 @@ references:
      command: |
        python --version ; pip --version ; pip list
        py.test pytorch_lightning tests -v --doctest-modules --junitxml=test-reports/pytest_junit.xml
-     no_output_timeout: 30m
+     no_output_timeout: 15m
 
   examples: &examples
    run:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b6280a1beeab6..31b829c61c7a2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -36,6 +36,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Run graceful training teardown on interpreter exit ([#1631](https://github.com/PyTorchLightning/pytorch-lightning/pull/1631))
+
 - Fixed user warning when apex was used together with learning rate schedulers ([#1873](https://github.com/PyTorchLightning/pytorch-lightning/pull/1873))
 
 - Fixed an issue with `Trainer.from_argparse_args` when passing in unknown Trainer args ([#1932](https://github.com/PyTorchLightning/pytorch-lightning/pull/1932))
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index 55c63679ae9f6..bd09d8252a5cc 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -141,21 +141,23 @@ def training_step(self, batch, batch_idx):
 
 """
 
+import atexit
+import signal
 from abc import ABC, abstractmethod
 from typing import Callable
 from typing import Union, List
 
 import numpy as np
-from torch.utils.data import DataLoader
 import torch
+from torch.utils.data import DataLoader
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     from apex import amp
@@ -179,9 +181,11 @@ def training_step(self, batch, batch_idx):
 else:
     HOROVOD_AVAILABLE = True
 
+# constant which signals should be catched for graceful trainer shutdown
+SIGNAL_TERMINATE = ('SIGTERM', 'SIGSEGV', 'SIGINT')
 
-class TrainerTrainLoopMixin(ABC):
 
+class TrainerTrainLoopMixin(ABC):
     # this is just a summary on variables used in this abstract class,
     #  the proper values/initialisation should be done in child class
     max_epochs: int
@@ -300,6 +304,15 @@ def has_arg(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
     def train(self):
+        # add signal handlers for process kills
+        def _signal_kill_handler(*args):
+            return TrainerTrainLoopMixin.run_training_teardown(self)
+
+        orig_signal_handlers = {}
+        for sig_name in SIGNAL_TERMINATE:
+            orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),
+                                                           _signal_kill_handler)
+
         # get model
         model = self.get_model()
 
@@ -371,6 +384,10 @@ def train(self):
 
             self.run_training_teardown()
 
+            # reset signal handlers
+            for sig_name in SIGNAL_TERMINATE:
+                signal.signal(getattr(signal, sig_name), orig_signal_handlers[sig_name])
+
         except KeyboardInterrupt:
             if self.proc_rank == 0:
                 log.info('Detected KeyboardInterrupt, attempting graceful shutdown...')
@@ -405,7 +422,7 @@ def run_training_epoch(self):
 
         # run epoch
         for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(
-            enumerate(_with_is_last(train_dataloader)), "get_train_batch"
+                enumerate(_with_is_last(train_dataloader)), "get_train_batch"
         ):
             # stop epoch if we limited the number of training batches
             if batch_idx >= self.num_training_batches:
@@ -663,7 +680,10 @@ def _get_optimizers_iterable(self):
         opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)
         return [(opt_idx, self.optimizers[opt_idx])]
 
+    @atexit.register
     def run_training_teardown(self):
+        if hasattr(self, '_teardown_already_run') and self._teardown_already_run:
+            return
         # Train end events
         with self.profiler.profile('on_train_end'):
             # callbacks
@@ -678,6 +698,8 @@ def run_training_teardown(self):
         # summarize profile results
         self.profiler.describe()
 
+        self._teardown_already_run = True
+
     def training_forward(self, batch, batch_idx, opt_idx, hiddens):
         """
         Handle forward for each training case (distributed, single gpu, etc...)