Fixing linting errors which could not be resolved in 23.07 (#1082)

There were too many linting errors to fix in 23.07 burndown so a PR has been created to fix this in 23.11.

Closes #1050

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

Approvers:
  - David Gardner (https://github.com/dagardner-nv)
  - Tad ZeMicheal (https://github.com/tzemicheal)

URL: #1082

docker/conda/environments/cuda11.8_dev.yml

@@ -82,17 +82,17 @@ dependencies:
     - python-confluent-kafka=1.9.2
     - python-graphviz
     - python=3.10
-    - pytorch=2.0.1
     - pytorch-cuda
+    - pytorch=2.0.1
     - rapidjson=1.1.0
     - scikit-build=0.17.1
     - scikit-learn=1.2.2
     - sphinx
     - sphinx_rtd_theme
     - sqlalchemy<2.0 # 2.0 is incompatible with pandas=1.3
     - sysroot_linux-64=2.17
-    - tritonclient=2.26 # Required by NvTabular, force the version, so we get protobufs compatible with 4.21
     - tqdm=4
+    - tritonclient=2.26 # Required by NvTabular, force the version, so we get protobufs compatible with 4.21
     - typing_utils=0.1
     - watchdog=2.1
     - yapf=0.40.1

examples/abp_pcap_detection/abp_pcap_preprocessing.py

@@ -21,7 +21,6 @@
 
 import cudf
 
-import morpheus._lib.stages as _stages
 from morpheus.cli.register_stage import register_stage
 from morpheus.common import TypeId
 from morpheus.config import Config
@@ -98,17 +97,17 @@ def pre_process_batch(x: MultiMessage, fea_len: int, fea_cols: typing.List[str],
         df["timestamp"] = x.get_meta("timestamp").astype("int64")
 
         def round_time_kernel(timestamp, rollup_time, secs):
-            for i, ts in enumerate(timestamp):
-                x = ts % secs
+            for i, time in enumerate(timestamp):
+                x = time % secs
                 y = 1 - (x / secs)
                 delta = y * secs
-                rollup_time[i] = ts + delta
+                rollup_time[i] = time + delta
 
         df = df.apply_rows(
             round_time_kernel,
             incols=["timestamp"],
-            outcols=dict(rollup_time=np.int64),
-            kwargs=dict(secs=60000000),
+            outcols={"rollup_time": np.int64},
+            kwargs={"secs": 60000000},
         )
 
         df["rollup_time"] = cudf.to_datetime(df["rollup_time"], unit="us").dt.strftime("%Y-%m-%d %H:%M")
@@ -149,7 +148,7 @@ def round_time_kernel(timestamp, rollup_time, secs):
         # syn/all - Number of flows with SYN flag to all flows
         # fin/all - Number of flows with FIN flag to all flows
         for col in ["rst", "syn", "fin"]:
-            dst_col = "{}/all".format(col)
+            dst_col = f"{col}/all"
             grouped_df[dst_col] = grouped_df[col] / grouped_df["all"]
 
         # Adding index column to retain the order of input messages.
@@ -197,4 +196,4 @@ def _get_preprocess_fn(self) -> typing.Callable[[MultiMessage], MultiInferenceMe
                        req_cols=self.req_cols)
 
     def _get_preprocess_node(self, builder: mrc.Builder):
-        return _stages.AbpPcapPreprocessingStage(builder, self.unique_name)
+        raise NotImplementedError("C++ node not implemented for this stage")

examples/developer_guide/2_1_real_world_phishing/recipient_features_stage.py

@@ -45,7 +45,7 @@ def __init__(self, config: Config, sep_token: str = '[SEP]'):
         if config.mode != PipelineModes.NLP:
             raise RuntimeError("RecipientFeaturesStage must be used in a pipeline configured for NLP")
 
-        if len(sep_token):
+        if len(sep_token) > 0:
             self._sep_token = sep_token
         else:
             raise ValueError("sep_token cannot be an empty string")

examples/developer_guide/3_simple_cpp_stage/pass_thru.py → examples/developer_guide/3_simple_cpp_stage/_lib/pass_thru.py

@@ -18,18 +18,17 @@
 import mrc
 from mrc.core import operators as ops
 
-from _lib import morpheus_example as morpheus_example_cpp
 from morpheus.cli.register_stage import register_stage
 from morpheus.pipeline.single_port_stage import SinglePortStage
 from morpheus.pipeline.stream_pair import StreamPair
 
 
-@register_stage("pass-thru")
+@register_stage("pass-thru-cpp")
 class PassThruStage(SinglePortStage):
 
     @property
     def name(self) -> str:
-        return "pass-thru"
+        return "pass-thru-cpp"
 
     def accepted_types(self) -> typing.Tuple:
         return (typing.Any, )
@@ -43,6 +42,9 @@ def on_data(self, message: typing.Any):
 
     def _build_single(self, builder: mrc.Builder, input_stream: StreamPair) -> StreamPair:
         if self._build_cpp_node():
+            from . import morpheus_example as morpheus_example_cpp
+
+            # pylint: disable=c-extension-no-member
             node = morpheus_example_cpp.PassThruStage(builder, self.unique_name)
         else:
             node = builder.make_node(self.unique_name, ops.map(self.on_data))

examples/developer_guide/3_simple_cpp_stage/run.py

@@ -17,8 +17,7 @@
 import logging
 import os
 
-from pass_thru import PassThruStage
-
+from _lib.pass_thru import PassThruStage
 from morpheus.config import Config
 from morpheus.pipeline import LinearPipeline
 from morpheus.stages.general.monitor_stage import MonitorStage

examples/digital_fingerprinting/production/morpheus/benchmarks/conftest.py

@@ -28,7 +28,7 @@ def get_json_lines_count(filename):
     return len(lines)
 
 
-def pytest_benchmark_update_json(config, benchmarks, output_json):
+def pytest_benchmark_update_json(_, __, output_json):
 
     curr_dir = path.dirname(path.abspath(__file__))
 
@@ -58,9 +58,9 @@ def pytest_benchmark_update_json(config, benchmarks, output_json):
 
         elif "glob_path" in PIPELINES_CONF[bench["name"]]:
             source_files_glob = path.join(curr_dir, PIPELINES_CONF[bench["name"]]["glob_path"])
-            for fn in glob.glob(source_files_glob):
-                line_count += get_json_lines_count(fn)
-                byte_count += path.getsize(fn)
+            for filename in glob.glob(source_files_glob):
+                line_count += get_json_lines_count(filename)
+                byte_count += path.getsize(filename)
         elif "message_path" in PIPELINES_CONF[bench["name"]]:
             source_message_glob = path.join(curr_dir, PIPELINES_CONF[bench["name"]]["message_path"])
             for message_fn in glob.glob(source_message_glob):

examples/digital_fingerprinting/production/morpheus/benchmarks/test_bench_e2e_dfp_pipeline.py

@@ -20,7 +20,7 @@
 import typing
 
 import boto3
-import dfp.modules  # noqa: F401
+import dfp.modules  # noqa: F401 # pylint:disable=unused-import
 import pytest
 from dfp.stages.dfp_file_batcher_stage import DFPFileBatcherStage
 from dfp.stages.dfp_file_to_df import DFPFileToDataFrameStage
@@ -35,13 +35,13 @@
 from dfp.utils.regex_utils import iso_date_regex
 from dfp.utils.schema_utils import Schema
 
-import morpheus.loaders  # noqa: F401
-import morpheus.modules  # noqa: F401
+import morpheus.loaders  # noqa: F401 # pylint:disable=unused-import
+import morpheus.modules  # noqa: F401 # pylint:disable=unused-import
 from benchmarks.benchmark_conf_generator import BenchmarkConfGenerator
 from benchmarks.benchmark_conf_generator import load_json
 from benchmarks.benchmark_conf_generator import set_mlflow_tracking_uri
-from morpheus._lib.common import FileTypes
-from morpheus._lib.common import FilterSource
+from morpheus.common import FileTypes
+from morpheus.common import FilterSource
 from morpheus.config import Config
 from morpheus.pipeline.linear_pipeline import LinearPipeline
 from morpheus.pipeline.pipeline import Pipeline
@@ -77,7 +77,7 @@ def remove_cache(cache_dir: str):
 
 
 def dfp_modules_pipeline(pipe_config: Config,
-                         modules_conf: typing.Dict[str, any],
+                         modules_conf: typing.Dict[str, typing.Any],
                          filenames: typing.List[str],
                          reuse_cache=False):
 
@@ -99,7 +99,7 @@ def dfp_modules_pipeline(pipe_config: Config,
 
 
 def dfp_training_pipeline_stages(pipe_config: Config,
-                                 stages_conf: typing.Dict[str, any],
+                                 stages_conf: typing.Dict[str, typing.Any],
                                  source_schema: DataFrameInputSchema,
                                  preprocess_schema: DataFrameInputSchema,
                                  filenames: typing.List[str],
@@ -150,7 +150,7 @@ def dfp_training_pipeline_stages(pipe_config: Config,
 
 
 def dfp_inference_pipeline_stages(pipe_config: Config,
-                                  stages_conf: typing.Dict[str, any],
+                                  stages_conf: typing.Dict[str, typing.Any],
                                   source_schema: DataFrameInputSchema,
                                   preprocess_schema: DataFrameInputSchema,
                                   filenames: typing.List[str],

examples/digital_fingerprinting/production/morpheus/dfp/modules/dfp_postprocessing.py

@@ -53,7 +53,7 @@ def process_events(message: MultiAEMessage):
         # df = message.get_meta()
         # df['event_time'] = datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ')
         # df.replace(np.nan, 'NaN', regex=True, inplace=True)
-        # TODO figure out why we are not able to set meta for a whole dataframe, but works for single column.
+        # TODO(Devin): figure out why we are not able to set meta for a whole dataframe, but works for single column.
         # message.set_meta(None, df)
         message.set_meta("event_time", datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ'))
 

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_file_batcher_stage.py

@@ -28,7 +28,7 @@
 from morpheus.pipeline.single_port_stage import SinglePortStage
 from morpheus.pipeline.stream_pair import StreamPair
 
-logger = logging.getLogger("morpheus.{}".format(__name__))
+logger = logging.getLogger(f"morpheus.{__name__}")
 
 TimestampFileObj = namedtuple("TimestampFileObj", ["timestamp", "file_object"])
 

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_inference_stage.py

@@ -30,7 +30,7 @@
 from ..utils.model_cache import ModelCache
 from ..utils.model_cache import ModelManager
 
-logger = logging.getLogger("morpheus.{}".format(__name__))
+logger = logging.getLogger(f"morpheus.{__name__}")
 
 
 class DFPInferenceStage(SinglePortStage):
@@ -93,12 +93,12 @@ def on_data(self, message: MultiDFPMessage) -> MultiDFPMessage:
             model_cache = self.get_model(user_id)
 
             if (model_cache is None):
-                raise RuntimeError("Could not find model for user {}".format(user_id))
+                raise RuntimeError(f"Could not find model for user {user_id}")
 
             loaded_model = model_cache.load_model(self._client)
 
-        except Exception:  # TODO
-            logger.exception("Error trying to get model")
+        except Exception:
+            logger.exception("Error trying to get model", exc_info=True)
             return None
 
         post_model_time = time.time()

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_mlflow_model_writer.py

@@ -45,7 +45,7 @@
 # Setup conda environment
 conda_env = {
     'channels': ['defaults', 'conda-forge'],
-    'dependencies': ['python={}'.format('3.10'), 'pip'],
+    'dependencies': ['python=3.10', 'pip'],
     'pip': ['mlflow'],
     'name': 'mlflow-env'
 }
@@ -131,7 +131,8 @@ def _apply_model_permissions(self, reg_model_name: str):
 
             get_registered_model_response = requests.get(url=get_registered_model_url,
                                                          headers=headers,
-                                                         params={"name": reg_model_name})
+                                                         params={"name": reg_model_name},
+                                                         timeout=10)
 
             registered_model_response = get_registered_model_response.json()
 
@@ -150,7 +151,8 @@ def _apply_model_permissions(self, reg_model_name: str):
 
             requests.patch(url=patch_registered_model_permissions_url,
                            headers=headers,
-                           json=patch_registered_model_permissions_body)
+                           json=patch_registered_model_permissions_body,
+                           timeout=10)
 
         except Exception:
             logger.exception("Error occurred trying to apply model permissions to model: %s",
@@ -194,14 +196,14 @@ def on_data(self, message: MultiAEMessage):
                 metrics_dict: typing.Dict[str, float] = {}
 
                 # Add info on the embeddings
-                for k, v in model.categorical_fts.items():
-                    embedding = v.get("embedding", None)
+                for key, value in model.categorical_fts.items():
+                    embedding = value.get("embedding", None)
 
                     if (embedding is None):
                         continue
 
-                    metrics_dict[f"embedding-{k}-num_embeddings"] = embedding.num_embeddings
-                    metrics_dict[f"embedding-{k}-embedding_dim"] = embedding.embedding_dim
+                    metrics_dict[f"embedding-{key}-num_embeddings"] = embedding.num_embeddings
+                    metrics_dict[f"embedding-{key}-embedding_dim"] = embedding.embedding_dim
 
                 mlflow.log_metrics(metrics_dict)
 
@@ -252,12 +254,12 @@ def on_data(self, message: MultiAEMessage):
                 }
 
                 # Now create the model version
-                mv = client.create_model_version(name=reg_model_name,
-                                                 source=model_src,
-                                                 run_id=run.info.run_id,
-                                                 tags=tags)
+                model_version = client.create_model_version(name=reg_model_name,
+                                                            source=model_src,
+                                                            run_id=run.info.run_id,
+                                                            tags=tags)
 
-                logger.debug("ML Flow model upload complete: %s:%s:%s", user, reg_model_name, mv.version)
+                logger.debug("ML Flow model upload complete: %s:%s:%s", user, reg_model_name, model_version.version)
 
         except Exception:
             logger.exception("Error uploading model to ML Flow", exc_info=True)

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_postprocessing_stage.py

@@ -28,7 +28,7 @@
 from morpheus.pipeline.single_port_stage import SinglePortStage
 from morpheus.pipeline.stream_pair import StreamPair
 
-logger = logging.getLogger("morpheus.{}".format(__name__))
+logger = logging.getLogger(f"morpheus.{__name__}")
 
 
 class DFPPostprocessingStage(SinglePortStage):

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_rolling_window_stage.py

@@ -31,7 +31,7 @@
 from ..utils.cached_user_window import CachedUserWindow
 from ..utils.logging_timer import log_time
 
-logger = logging.getLogger("morpheus.{}".format(__name__))
+logger = logging.getLogger(f"morpheus.{__name__}")
 
 
 class DFPRollingWindowStage(SinglePortStage):
@@ -124,8 +124,8 @@ def _build_window(self, message: DFPMessageMeta) -> MultiDFPMessage:
 
             if (not user_cache.append_dataframe(incoming_df=incoming_df)):
                 # Then our incoming dataframe wasnt even covered by the window. Generate warning
-                logger.warn(("Incoming data preceeded existing history. "
-                             "Consider deleting the rolling window cache and restarting."))
+                logger.warning(("Incoming data preceeded existing history. "
+                                "Consider deleting the rolling window cache and restarting."))
                 return None
 
             # Exit early if we dont have enough data

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_split_users_stage.py

@@ -31,7 +31,7 @@
 from ..messages.multi_dfp_message import DFPMessageMeta
 from ..utils.logging_timer import log_time
 
-logger = logging.getLogger("morpheus.{}".format(__name__))
+logger = logging.getLogger(f"morpheus.{__name__}")
 
 
 class DFPSplitUsersStage(SinglePortStage):
@@ -114,6 +114,7 @@ def extract_users(self, message: DataFrameType) -> typing.List[DFPMessageMeta]:
 
             if (self._include_individual):
 
+                # pylint: disable=unnecessary-comprehension
                 split_dataframes.update({
                     username: user_df
                     for username,

examples/digital_fingerprinting/production/morpheus/dfp/stages/dfp_training.py

@@ -28,7 +28,7 @@
 
 from ..messages.multi_dfp_message import MultiDFPMessage
 
-logger = logging.getLogger("morpheus.{}".format(__name__))
+logger = logging.getLogger(f"morpheus.{__name__}")
 
 
 class DFPTraining(SinglePortStage):
@@ -71,7 +71,7 @@ def __init__(self, c: Config, model_kwargs: dict = None, epochs=30, validation_s
 
         self._epochs = epochs
 
-        if (validation_size >= 0.0 and validation_size < 1.0):
+        if (0.0 <= validation_size < 1.0):
             self._validation_size = validation_size
         else:
             raise ValueError(f"validation_size={validation_size} should be a positive float in the (0, 1) range")

examples/digital_fingerprinting/production/morpheus/dfp/utils/dfp_arg_parser.py

@@ -68,7 +68,7 @@ def __init__(self,
         self._model_name_formatter = f"DFP-{source}-" + "{user_id}"
         self._experiment_name_formatter = f"dfp/{source}/training/" + "{reg_model_name}"
 
-    def verify_init(func):
+    def verify_init(self, func):
 
         def wrapper(self, *args, **kwargs):
             if not self._initialized:
@@ -134,7 +134,7 @@ def experiment_name_formatter(self):
         return self._experiment_name_formatter
 
     def _set_include_generic(self):
-        self._include_generic = self._train_users == "all" or self._train_users == "generic"
+        self._include_generic = self._train_users in ('all', 'generic')
 
     def _set_include_individual(self):
         self._include_individual = self._train_users != "generic"
@@ -150,9 +150,9 @@ def _create_time_fields(self, duration) -> TimeFields:
 
             end_time = self._start_time + duration
 
-        tf = TimeFields(self._start_time, end_time)
+        time_fields = TimeFields(self._start_time, end_time)
 
-        return tf
+        return time_fields
 
     def _set_mlflow_tracking_uri(self):
         if self._tracking_uri is None: