fixing some warnings messages from CI (aws#157)

Null warning mesg that were handled in the PR - 
1) comparison with != for None comparison and
2) regex string was not raw

Author: Vikas-kum

smdebug/core/index_reader.py

@@ -362,13 +362,13 @@ def _is_event_file_present(self, file):
     def list_index_files(self):
         index_dirname = IndexFileLocationUtils.get_index_path(self.path)
         # index files are json files or csv files ending with string ".csv" or ".json"
-        index_files_regex = "(.+)\.(json|csv)$"
+        index_files_regex = r"(.+)\.(json|csv)$"
         index_files = list_files_in_directory(index_dirname, file_regex=index_files_regex)
         return sorted(index_files)
 
     def list_event_files(self, start_after_key=None):
         # event files are ending with string ".tfevents"
-        event_file_regex = "(.+)\.(tfevents)$"
+        event_file_regex = r"(.+)\.(tfevents)$"
         event_files = list_files_in_directory(
             get_path_to_events_directory(self.path), file_regex=event_file_regex
         )

smdebug/core/utils.py

@@ -205,7 +205,7 @@ def parse_worker_name_from_file(filename: str) -> str:
     :return: worker_name: str
     """
     # worker_2 = /tmp/ts-logs/index/000000001/000000001230_worker_2.json
-    worker_name_regex = re.compile(".+\/\d+_(.+)\.(json|csv|tfevents)$")
+    worker_name_regex = re.compile(r".+\/\d+_(.+)\.(json|csv|tfevents)$")
     worker_name_regex_match = re.match(worker_name_regex, filename)
     if worker_name_regex_match is None:
         raise IndexReaderException(f"Invalid File Found: {filename}")

tests/mxnet/test_hook_reduce_config.py

@@ -55,29 +55,29 @@ def test_save_config(hook=None, out_dir=None):
     assert len(tr.steps()) == 7
 
     print(tr.tensor_names())
-    tname = tr.tensor_names(regex="conv\d+_weight")[0]
+    tname = tr.tensor_names(regex=r"conv\d+_weight")[0]
     # Global reduction with max and mean
     weight_tensor = tr.tensor(tname)
     max_val = weight_tensor.reduction_value(step_num=1, abs=False, reduction_name="max")
-    assert max_val != None
+    assert max_val is not None
     mean_val = weight_tensor.reduction_value(step_num=1, abs=False, reduction_name="mean")
-    assert mean_val != None
+    assert mean_val is not None
 
     # custom reduction at step 4 with reduction = 'min' and abs reduction = 'max'
-    tname = tr.tensor_names(regex="conv\d+_relu_input_0")[0]
+    tname = tr.tensor_names(regex=r"conv\d+_relu_input_0")[0]
     relu_input = tr.tensor(tname)
     min_val = relu_input.reduction_value(step_num=4, abs=False, reduction_name="min")
-    assert min_val != None
+    assert min_val is not None
     abs_max_val = relu_input.reduction_value(step_num=4, abs=True, reduction_name="max")
-    assert abs_max_val != None
+    assert abs_max_val is not None
 
     # Custom reduction with normalization
-    tname = tr.tensor_names(regex="flatten\d+_input_0")[0]
+    tname = tr.tensor_names(regex=r"flatten\d+_input_0")[0]
     flatten_input = tr.tensor(tname)
     l1_norm = flatten_input.reduction_value(step_num=4, abs=False, reduction_name="l1")
-    assert l1_norm != None
+    assert l1_norm is not None
     l2_norm = flatten_input.reduction_value(step_num=4, abs=True, reduction_name="l2")
-    assert l2_norm != None
+    assert l2_norm is not None
     if hook_created:
         shutil.rmtree(out_dir)
 

tests/mxnet/test_modes.py

@@ -44,7 +44,7 @@ def test_modes(hook=None, path=None):
     assert grad_eval_steps == []
 
     # Ensure that the weights are available in TRAIN and EVAL  modes.
-    wt_tns_name = tr.tensor_names(regex="conv\d+_weight")[0]
+    wt_tns_name = tr.tensor_names(regex=r"conv\d+_weight")[0]
     wt_tns = tr.tensor(wt_tns_name)
     wt_train_steps = wt_tns.steps(mode=modes.TRAIN)
     wt_eval_steps = wt_tns.steps(mode=modes.EVAL)

tests/tensorflow/hooks/test_mirrored_strategy.py

@@ -323,7 +323,7 @@ def test_basic(out_dir, zcc=False):
             assert len(tr.tensor(tname).workers(s, ModeKeys.EVAL)) == 1  # as eval_dist = False
             assert tr.tensor(tname).value(s, mode=ModeKeys.EVAL) is not None
 
-    tensornames = tr.tensor_names(regex="Identity_\d+:0")
+    tensornames = tr.tensor_names(regex=r"Identity_\d+:0")
     for s in tr.tensor(tensornames[0]).steps(ModeKeys.TRAIN):
         for w in tr.tensor(tensornames[0]).workers(s, ModeKeys.TRAIN):
             assert tr.tensor(tensornames[0]).value(s, worker=w, mode=ModeKeys.TRAIN) is not None
@@ -369,7 +369,7 @@ def test_eval_distributed(out_dir):
             assert len(tr.tensor(tname).workers(s, ModeKeys.EVAL)) == strategy.num_replicas_in_sync
             assert tr.tensor(tname).value(s, mode=ModeKeys.EVAL) is not None
 
-    tensornames = tr.tensor_names(regex="Identity_\d+:0")
+    tensornames = tr.tensor_names(regex=r"Identity_\d+:0")
     for s in tr.tensor(tensornames[0]).steps(ModeKeys.TRAIN):
         for w in tr.tensor(tensornames[0]).workers(s, ModeKeys.TRAIN):
             assert tr.tensor(tensornames[0]).value(s, worker=w, mode=ModeKeys.TRAIN) is not None