diff --git a/pytorch_lightning/core/saving.py b/pytorch_lightning/core/saving.py
index adf782e6..a2feeba0 100644
--- a/pytorch_lightning/core/saving.py
+++ b/pytorch_lightning/core/saving.py
@@ -154,6 +154,6 @@ def save_hparams_to_yaml(config_yaml, hparams: Union[dict, Namespace]) -> None:
 def convert(val: str) -> Union[int, float, bool, str]:
     try:
         return ast.literal_eval(val)
-    except (ValueError, SyntaxError) as e:
-        log.debug(e)
+    except (ValueError, SyntaxError) as err:
+        log.debug(err)
         return val
diff --git a/pytorch_lightning/loggers/comet.py b/pytorch_lightning/loggers/comet.py
index 01d0602a..094c82fc 100644
--- a/pytorch_lightning/loggers/comet.py
+++ b/pytorch_lightning/loggers/comet.py
@@ -135,7 +135,7 @@ class CometLogger(LightningLoggerBase):
         if experiment_name:
             try:
                 self.name = experiment_name
-            except TypeError as e:
+            except TypeError:
                 log.exception("Failed to set experiment name for comet.ml logger")
         self._kwargs = kwargs
 
diff --git a/pytorch_lightning/overrides/data_parallel.py b/pytorch_lightning/overrides/data_parallel.py
index b2f7816e..f2a23b18 100644
--- a/pytorch_lightning/overrides/data_parallel.py
+++ b/pytorch_lightning/overrides/data_parallel.py
@@ -177,9 +177,9 @@ def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):  # pragma: n
 
             with lock:
                 results[i] = output
-        except Exception as e:
+        except Exception as ex:
             with lock:
-                results[i] = e
+                results[i] = ex
 
     # TODO: fix hack (maybe not a hack)
     # make sure each module knows what training state it's in...
diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
index 3844896b..7fea36ec 100644
--- a/pytorch_lightning/trainer/distrib_data_parallel.py
+++ b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -277,7 +277,7 @@ class TrainerDDPMixin(ABC):
             should_fake = int(os.environ['FAKE_SLURM_MANAGING_TASKS'])
             if should_fake:
                 self.is_slurm_managing_tasks = True
-        except Exception as e:
+        except Exception:
             pass
 
         # notify user the that slurm is managing tasks
diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
index dabe72f2..9d6e29a7 100644
--- a/pytorch_lightning/trainer/distrib_parts.py
+++ b/pytorch_lightning/trainer/distrib_parts.py
@@ -343,7 +343,7 @@ from abc import ABC, abstractmethod
 import time
 import random
 import torch
-from typing import Union
+from typing import Union, Callable
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.loggers import LightningLoggerBase
@@ -748,26 +748,33 @@ def determine_root_gpu_device(gpus):
     return root_gpu
 
 
-def retry_jittered_backoff(f, num_retries=5):
-    # Based on:
-    # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
-    cap = 1.0                  # max sleep time is 1s
-    base = 0.01                # initial sleep time is 10ms
-    sleep = base               # initial sleep time is 10ms
+def retry_jittered_backoff(func: Callable, num_retries: int = 5, cap_delay: float = 1.0, base_delay: float = 0.01):
+    """Retry jittered backoff.
+
+    Based on:
+    https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
+
+    Args:
+        func: tested function
+        num_retries: number of tries
+        cap_delay: max sleep time
+        base_delay: initial sleep time is 10ms
+    """
+    sleep_delay = base_delay         # initial sleep time is 10ms
 
     for i in range(num_retries):
         try:
-            return f()
-        except RuntimeError as e:
+            return func()
+        except RuntimeError as err:
             if i == num_retries - 1:
-                raise e
+                raise err
             else:
                 continue
-        time.sleep(sleep)
-        sleep = min(cap, random.uniform(base, sleep * 3))
+        time.sleep(sleep_delay)
+        sleep_delay = min(cap_delay, random.uniform(base_delay, sleep_delay * 3))
 
 
-def pick_single_gpu(exclude_gpus=[]):
+def pick_single_gpu(exclude_gpus: list):
     for i in range(torch.cuda.device_count()):
         if i in exclude_gpus:
             continue
@@ -781,9 +788,9 @@ def pick_single_gpu(exclude_gpus=[]):
     raise RuntimeError("No GPUs available.")
 
 
-def pick_multiple_gpus(n):
+def pick_multiple_gpus(nb):
     picked = []
-    for _ in range(n):
+    for _ in range(nb):
         picked.append(pick_single_gpu(exclude_gpus=picked))
 
     return picked
diff --git a/pytorch_lightning/trainer/training_io.py b/pytorch_lightning/trainer/training_io.py
index d036f5d9..e55fb105 100644
--- a/pytorch_lightning/trainer/training_io.py
+++ b/pytorch_lightning/trainer/training_io.py
@@ -84,7 +84,6 @@ At a rough level, here's what happens inside Trainer :py:mod:`pytorch_lightning.
 """
 
 import os
-import pickle
 import re
 import signal
 from abc import ABC
@@ -211,7 +210,7 @@ class TrainerIOMixin(ABC):
             job_name = os.environ['SLURM_JOB_NAME']
             if job_name != 'bash':
                 on_slurm = True
-        except Exception as e:
+        except Exception:
             pass
 
         if on_slurm:
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index d9bc1dc9..de8039fe 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -823,5 +823,5 @@ def test_trainer_subclassing():
     assert trainer.fast_dev_run
 
     # when we pass in an unknown arg, the base class should complain
-    with pytest.raises(TypeError, match=r"__init__\(\) got an unexpected keyword argument 'abcdefg'") as e:
+    with pytest.raises(TypeError, match=r"__init__\(\) got an unexpected keyword argument 'abcdefg'"):
         TrainerSubclass(abcdefg='unknown_arg')