Merge branch 'branch-25.02' into impl-cpp-monitor-stage

docs/source/developer_guide/guides/2_real_world_phishing.md

@@ -482,7 +482,6 @@ To explicitly set the output format we could specify the `file_type` argument to
 ```python
 import logging
 import os
-import tempfile
 
 import click
 
@@ -542,7 +541,7 @@ MORPHEUS_ROOT = os.environ['MORPHEUS_ROOT']
 @click.option("--server_url", default='localhost:8000', help="Tritonserver url.")
 @click.option(
     "--output_file",
-    default=os.path.join(tempfile.gettempdir(), "detections.jsonlines"),
+    default=".tmp/output/phishing_detections.jsonlines",
     help="The path to the file where the inference output will be saved.",
 )
 def run_pipeline(use_stage_function: bool,
@@ -633,7 +632,7 @@ morpheus --log_level=debug --plugin examples/developer_guide/2_1_real_world_phis
   monitor --description="Inference Rate" --smoothing=0.001 --unit=inf \
   add-scores --label=is_phishing \
   serialize \
-  to-file --filename=/tmp/detections.jsonlines --overwrite
+  to-file --filename=.tmp/output/phishing_detections_cli.jsonlines --overwrite
 ```
 
 ## Stage Constructors

examples/abp_nvsmi_detection/README.md

@@ -140,7 +140,7 @@ morpheus --log_level=DEBUG \
    `# 7th Stage: Convert from objects back into strings. Ignore verbose input data` \
    serialize --include 'mining' \
    `# 8th Stage: Write out the JSON lines to the detections.jsonlines file` \
-   to-file --filename=detections.jsonlines --overwrite
+   to-file --filename=.tmp/output/abp_nvsmi_detections.jsonlines --overwrite
 ```
 
 If successful, the following should be displayed:
@@ -217,15 +217,15 @@ Added stage: <add-class-5; AddClassificationsStage(threshold=0.5, labels=[], pre
   └─ morpheus.ControlMessage -> morpheus.ControlMessage
 Added stage: <serialize-6; SerializeStage(include=['mining'], exclude=['^ID$', '^_ts_'], fixed_columns=True)>
   └─ morpheus.ControlMessage -> morpheus.MessageMeta
-Added stage: <to-file-7; WriteToFileStage(filename=detections.jsonlines, overwrite=True, file_type=FileTypes.Auto)>
+Added stage: <to-file-7; WriteToFileStage(filename=.tmp/output/abp_nvsmi_detections.jsonlines, overwrite=True, file_type=FileTypes.Auto)>
   └─ morpheus.MessageMeta -> morpheus.MessageMeta
 ====Building Pipeline Complete!====
 Starting! Time: 1656353254.9919598
 Inference Rate[Complete]: 1242inf [00:00, 1863.04inf/s]
 ====Pipeline Complete====
 ```
 
-The output file `detections.jsonlines` will contain a single boolean value for each input line. At some point the values will switch from `0` to `1`:
+The output file `.tmp/output/detections.jsonlines` will contain a single boolean value for each input line. At some point the values will switch from `0` to `1`:
 
 ```
 ...

examples/abp_pcap_detection/README.md

@@ -97,7 +97,7 @@ python examples/abp_pcap_detection/run.py
 ```
 Note: Both Morpheus and Triton Inference Server containers must have access to the same GPUs in order for this example to work.
 
-The pipeline will process the input `abp_pcap_dump.jsonlines` sample data and write it to `pcap_out.jsonlines`.
+The pipeline will process the input `abp_pcap_dump.jsonlines` sample data and write it to `.tmp/output/pcap_out.jsonlines`.
 
 ### CLI Example
 The above example is illustrative of using the Python API to build a custom Morpheus Pipeline.
@@ -118,6 +118,6 @@ morpheus --log_level INFO --plugin "examples/abp_pcap_detection/abp_pcap_preproc
     monitor --description "Add classification rate" --unit "add-class" \
     serialize \
     monitor --description "Serialize rate" --unit ser \
-    to-file --filename "pcap_out.jsonlines" --overwrite \
+    to-file --filename ".tmp/output/pcap_out_cli.jsonlines" --overwrite \
     monitor --description "Write to file rate" --unit "to-file"
 ```

examples/abp_pcap_detection/run.py

@@ -65,7 +65,7 @@
 )
 @click.option(
     "--output_file",
-    default="./pcap_out.jsonlines",
+    default="./.tmp/output/pcap_out.jsonlines",
     help="The path to the file where the inference output will be saved.",
 )
 @click.option(

examples/cpu_only/README.md

@@ -53,7 +53,7 @@ Options:
 To launch the configured Morpheus pipeline with the sample data that is provided in `examples/data`, run the following:
 
 ```bash
-python examples/cpu_only/run.py --use_cpu_only --in_file=examples/data/email.jsonlines --out_file=.tmp/out.jsonlines
+python examples/cpu_only/run.py --use_cpu_only --in_file=examples/data/email.jsonlines --out_file=.tmp/output/cpu_only_out.jsonlines
 ```
 
 ### CLI Example
@@ -68,5 +68,5 @@ morpheus --log_level INFO \
     deserialize \
     monitor --description "deserialize" \
     serialize \
-    to-file --filename=.tmp/out.jsonlines --overwrite
+    to-file --filename=.tmp/output/cpu_only_cli_out.jsonlines --overwrite
 ```

examples/cpu_only/run.py

@@ -61,7 +61,7 @@
     "--out_file",
     help="Output file",
     type=click.Path(dir_okay=False),
-    default="output.csv",
+    default=".tmp/output/cpu_only_out.csv",
     required=True,
 )
 def run_pipeline(log_level: int, use_cpu_only: bool, in_file: pathlib.Path, out_file: pathlib.Path):

examples/developer_guide/2_1_real_world_phishing/run.py

@@ -17,7 +17,6 @@
 
 import logging
 import os
-import tempfile
 
 import click
 from recipient_features_stage import RecipientFeaturesStage
@@ -77,7 +76,7 @@
 @click.option("--server_url", default='localhost:8000', help="Tritonserver url.")
 @click.option(
     "--output_file",
-    default=os.path.join(tempfile.gettempdir(), "detections.jsonlines"),
+    default=".tmp/output/phishing_detections.jsonlines",
     help="The path to the file where the inference output will be saved.",
 )
 def run_pipeline(use_stage_function: bool,

examples/gnn_fraud_detection_pipeline/README.md

@@ -97,7 +97,7 @@ Added stage: <serialize-8; SerializeStage(include=None, exclude=None, fixed_colu
   └─ morpheus.ControlMessage -> morpheus.MessageMeta
 Added stage: <monitor-9; MonitorStage(description=Serialize rate, smoothing=0.05, unit=messages, delayed_start=False, determine_count_fn=None, log_level=LogLevels.INFO)>
   └─ morpheus.MessageMeta -> morpheus.MessageMeta
-Added stage: <to-file-10; WriteToFileStage(filename=output.csv, overwrite=True, file_type=FileTypes.Auto, include_index_col=True, flush=False)>
+Added stage: <to-file-10; WriteToFileStage(filename=.tmp/output/gnn_fraud_detection_output.csv, overwrite=True, file_type=FileTypes.Auto, include_index_col=True, flush=False)>
   └─ morpheus.MessageMeta -> morpheus.MessageMeta
 ====Building Segment Complete!====
 Graph construction rate[Complete]: 265 messages [00:00, 1016.18 messages/s]
@@ -128,5 +128,5 @@ morpheus --log_level INFO \
 	gnn-fraud-classification --model_xgb_file examples/gnn_fraud_detection_pipeline/model/xgb.pt \
 	monitor --description "Add classification rate" \
 	serialize \
-	to-file --filename "output.csv" --overwrite
+	to-file --filename "gnn_fraud_detection_cli_output.csv" --overwrite
 ```

examples/gnn_fraud_detection_pipeline/run.py

@@ -84,7 +84,7 @@
 @click.option(
     "--output_file",
     type=click.Path(dir_okay=False),
-    default="output.csv",
+    default=".tmp/output/gnn_fraud_detection_output.csv",
     help="The path to the file where the inference output will be saved.",
 )
 def run_pipeline(num_threads,

examples/log_parsing/README.md

@@ -119,6 +119,6 @@ morpheus --log_level INFO \
 	monitor --description "Inference rate" --unit inf \
 	log-postprocess --vocab_path ./models/training-tuning-scripts/sid-models/resources/bert-base-cased-vocab.txt \
 		--model_config_path=./models/log-parsing-models/log-parsing-config-20220418.json \
-	to-file --filename ./log-parsing-output.jsonlines --overwrite  \
+	to-file --filename .tmp/output/log-parsing-cli-output.jsonlines --overwrite  \
 	monitor --description "Postprocessing rate"
 ```

examples/log_parsing/run.py

@@ -60,7 +60,7 @@
 )
 @click.option(
     "--output_file",
-    default="log-parsing-output.jsonlines",
+    default=".tmp/output/log-parsing-output.jsonlines",
     help="The path to the file where the inference output will be saved.",
 )
 @click.option('--model_vocab_hash_file',

examples/nlp_si_detection/README.md

@@ -131,8 +131,8 @@ morpheus --log_level=DEBUG \
    filter --filter_source=TENSOR \
    `# 8th Stage: Convert from objects back into strings` \
    serialize --exclude '^_ts_' \
-   `# 9th Stage: Write out the JSON lines to the detections.jsonlines file` \
-   to-file --filename=detections.jsonlines --overwrite
+   `# 9th Stage: Write out the JSON lines to the nlp_si_detections.jsonlines file` \
+   to-file --filename=.tmp/output/nlp_si_detections.jsonlines --overwrite
 ```
 
 If successful, the following should be displayed:
@@ -187,7 +187,7 @@ Added stage: <add-class-5; AddClassificationsStage(threshold=0.5, labels=[], pre
   └─ morpheus.ControlMessage -> morpheus.ControlMessage
 Added stage: <serialize-6; SerializeStage(include=[], exclude=['^_ts_'], fixed_columns=True)>
   └─ morpheus.ControlMessage -> morpheus.MessageMeta
-Added stage: <to-file-7; WriteToFileStage(filename=detections.jsonlines, overwrite=True, file_type=FileTypes.Auto)>
+Added stage: <to-file-7; WriteToFileStage(filename=.tmp/output/nlp_si_detections.jsonlines, overwrite=True, file_type=FileTypes.Auto)>
   └─ morpheus.MessageMeta -> morpheus.MessageMeta
 ====Building Pipeline Complete!====
 Starting! Time: 1656352480.541071
@@ -196,7 +196,7 @@ Inference Rate[Complete]: 93085inf [00:07, 12673.63inf/s]
 
 ```
 
-The output file `detections.jsonlines` will contain the original PCAP messages with the following additional fields added:
+The output file `.tmp/output/nlp_si_detections.jsonlines` will contain the original PCAP messages with the following additional fields added:
 * `address`
 * `bank_acct`
 * `credit_card`

examples/nlp_si_detection/run.sh

@@ -29,4 +29,4 @@ morpheus --log_level=DEBUG \
     add-class \
     filter --filter_source=TENSOR \
     serialize --exclude '^_ts_' \
-    to-file --filename=detections.jsonlines --overwrite
+    to-file --filename=.tmp/output/nlp_si_detections.jsonlines --overwrite

examples/ransomware_detection/README.md

@@ -72,7 +72,7 @@ python examples/ransomware_detection/run.py --server_url=localhost:8000 \
               --sliding_window=3 \
               --model_name=ransomw-model-short-rf \
               --input_glob=./examples/data/appshield/*/snapshot-*/*.json \
-              --output_file=./ransomware_detection_output.jsonlines
+              --output_file=.tmp/output/ransomware_detection_output.jsonlines
 ```
 
 Input features for a short model can be taken from every three snapshots sequence, such as (1, 2, 3), or (2, 3, 4). The sliding window represents the number of subsequent snapshots that need to be taken into consideration when generating the input for a model. Sliding window for the medium model is `5` and for the long model it is `10`.

examples/ransomware_detection/run.py

@@ -21,9 +21,12 @@
 from stages.create_features import CreateFeaturesRWStage
 from stages.preprocessing import PreprocessingRWStage
 
+from morpheus.common import TypeId
 from morpheus.config import Config
 from morpheus.config import PipelineModes
+from morpheus.messages import MessageMeta
 from morpheus.pipeline.linear_pipeline import LinearPipeline
+from morpheus.pipeline.stage_decorator import stage
 from morpheus.stages.general.monitor_stage import MonitorStage
 from morpheus.stages.inference.triton_inference_stage import TritonInferenceStage
 from morpheus.stages.input.appshield_source_stage import AppShieldSourceStage
@@ -61,6 +64,12 @@
     type=click.IntRange(min=1),
     help="Max batch size to use for the model.",
 )
+@click.option(
+    "--pipeline_batch_size",
+    default=1024,
+    type=click.IntRange(min=1),
+    help=("Internal batch size for the pipeline. Can be much larger than the model batch size."),
+)
 @click.option(
     "--conf_file",
     type=click.STRING,
@@ -95,21 +104,22 @@
 @click.option(
     "--output_file",
     type=click.STRING,
-    default="./ransomware_detection_output.jsonlines",
+    default=".tmp/output/ransomware_detection_output.jsonlines",
     help="The path to the file where the inference output will be saved.",
 )
-def run_pipeline(debug,
-                 num_threads,
-                 n_dask_workers,
-                 threads_per_dask_worker,
-                 model_max_batch_size,
-                 conf_file,
-                 model_name,
-                 server_url,
-                 sliding_window,
-                 input_glob,
-                 watch_directory,
-                 output_file):
+def run_pipeline(debug: bool,
+                 num_threads: int,
+                 n_dask_workers: int,
+                 threads_per_dask_worker: int,
+                 model_max_batch_size: int,
+                 pipeline_batch_size: int,
+                 conf_file: str,
+                 model_name: str,
+                 server_url: str,
+                 sliding_window: int,
+                 input_glob: str,
+                 watch_directory: bool,
+                 output_file: str):
 
     if debug:
         configure_logging(log_level=logging.DEBUG)
@@ -125,6 +135,7 @@ def run_pipeline(debug,
     # Below properties are specified by the command line.
     config.num_threads = num_threads
     config.model_max_batch_size = model_max_batch_size
+    config.pipeline_batch_size = pipeline_batch_size
     config.feature_length = snapshot_fea_length * sliding_window
     config.class_labels = ["pred", "score"]
 
@@ -222,6 +233,18 @@ def run_pipeline(debug,
     # This stage logs the metrics (msg/sec) from the above stage.
     pipeline.add_stage(MonitorStage(config, description="Serialize rate"))
 
+    @stage(needed_columns={'timestamp_process': TypeId.STRING})
+    def concat_columns(msg: MessageMeta) -> MessageMeta:
+        """
+        This stage concatinates the timestamp and pid_process columns to create a unique field.
+        """
+        with msg.mutable_dataframe() as df:
+            df['timestamp_process'] = df['timestamp'] + df['pid_process']
+
+        return msg
+
+    pipeline.add_stage(concat_columns(config))
+
     # Add a write file stage.
     # This stage writes all messages to a file.
     pipeline.add_stage(WriteToFileStage(config, filename=output_file, overwrite=True))

examples/root_cause_analysis/README.md

@@ -124,7 +124,7 @@ add-scores --label=is_root_cause \
 `# 7th Stage: Convert from objects back into strings` \
 serialize --exclude '^ts_' \
 `# 8th Stage: Write results out to CSV file` \
-to-file --filename=./root-cause-binary-output.jsonlines --overwrite
+to-file --filename=.tmp/output/root-cause-binary-output.jsonlines --overwrite
 ```
 
 If successful, the following should be displayed:
@@ -177,10 +177,10 @@ Added stage: <add-scores-5; AddScoresStage(labels=('is_root_cause',), prefix=)>
   └─ morpheus.ControlMessagee -> morpheus.ControlMessage
 Added stage: <serialize-6; SerializeStage(include=(), exclude=('^ts_',), fixed_columns=True)>
   └─ morpheus.ControlMessage -> morpheus.MessageMeta
-Added stage: <to-file-7; WriteToFileStage(filename=./root-cause-binary-output.jsonlines, overwrite=True, file_type=FileTypes.Auto, include_index_col=True)>
+Added stage: <to-file-7; WriteToFileStage(filename=.tmp/output/root-cause-binary-output.jsonlines, overwrite=True, file_type=FileTypes.Auto, include_index_col=True)>
   └─ morpheus.MessageMeta -> morpheus.MessageMeta
 Inference rate[Complete]: 473 inf [00:01, 340.43 inf/s]
 ====Pipeline Complete====
 ```
 
-The output file `root-cause-binary-output.jsonlines` will contain the original kernel log messages with an additional field `is_root_cause`. The value of the new field will be the root cause probability.
+The output file `.tmp/output/root-cause-binary-output.jsonlines` will contain the original kernel log messages with an additional field `is_root_cause`. The value of the new field will be the root cause probability.

python/morpheus/morpheus/config.py

@@ -235,6 +235,7 @@ def freeze(self):
         """
         self._check_cpp_mode(fix_mis_match=not self.frozen)
         if not self.frozen:
+            self._validate_config()
             self.frozen = True
 
     def _check_cpp_mode(self, fix_mis_match: bool = False):
@@ -267,7 +268,6 @@ def pipeline_batch_size(self):
     @pipeline_batch_size.setter
     def pipeline_batch_size(self, value: int):
         self._pipeline_batch_size = value
-        self._validate_config()
 
     @property
     def model_max_batch_size(self):
@@ -276,7 +276,6 @@ def model_max_batch_size(self):
     @model_max_batch_size.setter
     def model_max_batch_size(self, value: int):
         self._model_max_batch_size = value
-        self._validate_config()
 
     def _validate_config(self):
         if self._pipeline_batch_size < self._model_max_batch_size:

tests/morpheus/stages/test_multi_processing_stage.py

@@ -172,7 +172,7 @@ def test_created_stage_pipe(config: Config, dataset_pandas: DatasetManager):
     expected_df = input_df.copy()
     expected_df["new_column"] = "Hello"
 
-    df_count = 100
+    df_count = 10
     df_generator = partial(pandas_dataframe_generator, dataset_pandas, df_count)
 
     partial_fn = partial(_process_df, column="new_column", value="Hello")
@@ -225,7 +225,7 @@ def test_multiple_stages_pipe(config: Config, dataset_pandas: DatasetManager):
     expected_df["new_column_1"] = "new_value"
     expected_df["new_column_2"] = "Hello"
 
-    df_count = 100
+    df_count = 10
     df_generator = partial(pandas_dataframe_generator, dataset_pandas, df_count)
 
     partial_fn = partial(_process_df, column="new_column_1", value="new_value")

tests/morpheus/test_config.py

@@ -159,6 +159,7 @@ def test_warning_model_batch_size_less_than_pipeline_batch_size(caplog: pytest.L
     config.pipeline_batch_size = 256
     with caplog.at_level(logging.WARNING):
         config.model_max_batch_size = 257
+        config.freeze()
         assert len(caplog.records) == 1
         import re
         assert re.match(".*pipeline_batch_size < model_max_batch_size.*", caplog.records[0].message) is not None
@@ -169,6 +170,7 @@ def test_warning_pipeline_batch_size_less_than_model_batch_size(caplog: pytest.L
     config.model_max_batch_size = 8
     with caplog.at_level(logging.WARNING):
         config.pipeline_batch_size = 7
+        config.freeze()
         assert len(caplog.records) == 1
         import re
         assert re.match(".*pipeline_batch_size < model_max_batch_size.*", caplog.records[0].message) is not None