Fix intermittent failures in test_dfencoder_distributed_e2e test (nv-…

…morpheus#1113)

* Call the `manual_seed` method from within the subprocess, this ensures the subprocess runs deterministically.
* Add a sleep to a busy-loop in `morpheus/models/dfencoder/multiprocessing.py`
* Misc pylint fixes

fixes nv-morpheus#1021

Authors:
  - David Gardner (https://github.com/dagardner-nv)

Approvers:
  - Christopher Harris (https://github.com/cwharris)
  - Michael Demoret (https://github.com/mdemoret-nv)

URL: nv-morpheus#1113

morpheus/models/dfencoder/multiprocessing.py

@@ -16,6 +16,7 @@
 import multiprocessing
 import signal
 import sys
+import time
 
 
 def _wrap(fn, i, args, error_queue):
@@ -62,4 +63,4 @@ def start_processes(fn, args=(), nprocs=1, join=True, daemon=False, start_method
 
     # Loop on join until it returns True or raises an exception.
     while not context.join():
-        pass
+        time.sleep(0.1)

tests/dfencoder/test_dfencoder_distributed_e2e.py

@@ -16,7 +16,7 @@
 
 # This must come before torch
 # isort: off
-import cudf  # noqa: F401
+import cudf  # noqa: F401 pylint: disable=unused-import
 # isort: on
 
 import json
@@ -106,7 +106,6 @@ def cleanup_dist():
 
 
 @pytest.mark.slow
-@pytest.mark.usefixtures("manual_seed")
 def test_dfencoder_distributed_e2e():
 
     world_size = 1
@@ -117,14 +116,19 @@ def test_dfencoder_distributed_e2e():
 
 
 def _run_test(rank, world_size):
+    from morpheus.utils import seed as seed_utils
+    seed_utils.manual_seed(42)
 
     import torch
     torch.cuda.set_device(rank)
 
     setup_dist(rank, world_size)
 
-    preset_cats = json.load(open(PRESET_CATS_FILEPATH, 'r'))
-    preset_numerical_scaler_params = json.load(open(PRESET_NUMERICAL_SCALER_PARAMS_FILEPATH, 'r'))
+    with open(PRESET_CATS_FILEPATH, 'r', encoding='utf-8') as fh:
+        preset_cats = json.load(fh)
+
+    with open(PRESET_NUMERICAL_SCALER_PARAMS_FILEPATH, 'r', encoding='utf-8') as fh:
+        preset_numerical_scaler_params = json.load(fh)
 
     # Initializing model
     model = AutoEncoder(
@@ -171,9 +175,9 @@ def _run_test(rank, world_size):
         # Make sure model converges (low loss)
         for loss_type in LOSS_TYPES:
             ft_losses = getattr(model.logger, f"{loss_type}_fts")
-            for ft, losses_l in ft_losses.items():
+            for feature, losses_l in ft_losses.items():
                 losses = losses_l[1]
-                assert min(losses) < LOSS_TARGETS[loss_type][ft] * LOSS_TOLERANCE_RATIO
+                assert min(losses) < LOSS_TARGETS[loss_type][feature] * LOSS_TOLERANCE_RATIO
 
         # Inference
         inf_dataset = DatasetFromPath(