Fix description for cache_mode option of DFP Rolling Window module

docs/source/modules/examples/digital_fingerprinting/dfp_deployment.md

@@ -102,7 +102,7 @@ This module function sets up modular Digital Fingerprinting Pipeline instance.
 
 | Parameter               | Type   | Description                                                 | Example Value | Default Value |
 |-------------------------|--------|-------------------------------------------------------------|---------------|---------------|
-| `cache_mode`            | string | The user ID to use if the user ID is not found              | "batch"       | `batch`       |
+| `cache_mode`            | string | Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are met. Otherwise, continue to aggregate user's history. | "batch"       | `batch`       |
 | `min_history`           | int    | Minimum history to trigger a new training event             | 1             | `1`           |
 | `max_history`           | int    | Maximum history to include in a new training event          | 0             | `0`           |
 | `timestamp_column_name` | string | Name of the column containing timestamps                    | "timestamp"   | `timestamp`   |

docs/source/modules/examples/digital_fingerprinting/dfp_inference_pipe.md

@@ -60,7 +60,7 @@ into a single module.
 
 | Parameter               | Type   | Description                                                 | Example Value | Default Value |
 |-------------------------|--------|-------------------------------------------------------------|---------------|---------------|
-| `cache_mode`            | string | The user ID to use if the user ID is not found              | "batch"       | `batch`       |
+| `cache_mode`            | string | Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are met. Otherwise, continue to aggregate user's history. | "batch"       | `batch`       |
 | `min_history`           | int    | Minimum history to trigger a new training event             | 1             | `1`             |
 | `max_history`           | int    | Maximum history to include in a new training event          | 0             | `0`             |
 | `timestamp_column_name` | string | Name of the column containing timestamps                    | "timestamp"   | `timestamp`   |
@@ -105,7 +105,7 @@ into a single module.
   },
   "stream_aggregation_options": {
     "timestamp_column_name": "timestamp",
-    "cache_mode": "MEMORY",
+    "cache_mode": "batch",
     "trigger_on_min_history": true,
     "aggregation_span": "1D",
     "trigger_on_min_increment": true,

docs/source/modules/examples/digital_fingerprinting/dfp_rolling_window.md

@@ -23,7 +23,7 @@ This module is responsible for maintaining a rolling window of historical data,
 
 | Parameter                | Type   | Description                                                  | Example Value | Default Value |
 |--------------------------|--------|--------------------------------------------------------------|---------------|---------------|
-| cache_mode               | string | The user ID to use if the user ID is not found               | "batch"       | "batch"       |
+| cache_mode               | string | Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are met. Otherwise, continue to aggregate user's history.  | "batch"       | "batch"       |
 | trigger_on_min_history   | int    | Minimum history to trigger a new training event              | 1             | 1             |
 | trigger_on_min_increment | int    | Minmum increment from the last trained to new training event | 0             | 0             |
 | timestamp_column_name    | string | Name of the column containing timestamps                     | "timestamp"   | "timestamp"   |

docs/source/modules/examples/digital_fingerprinting/dfp_training_pipe.md

@@ -59,7 +59,7 @@ This module function consolidates multiple DFP pipeline modules relevant to the
 
 | Key                     | Type   | Description                                                 | Example Value | Default Value |
 |-------------------------|--------|-------------------------------------------------------------|---------------|---------------|
-| `cache_mode`            | string | The user ID to use if the user ID is not found              | "batch"       | `batch`       |
+| `cache_mode`            | string | Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are met. Otherwise, continue to aggregate user's history. | "batch"       | `batch`       |
 | `min_history`           | int    | Minimum history to trigger a new training event             | 1             | `1`           |
 | `max_history`           | int    | Maximum history to include in a new training event          | 0             | `0`           |
 | `timestamp_column_name` | string | Name of the column containing timestamps                    | 'timestamp'   | `timestamp`   |

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

@@ -98,7 +98,8 @@ def dfp_deployment(builder: mrc.Builder):
         - timestamp_column_name (str): Name of the timestamp column; Example: `timestamp`; Default: timestamp
 
     stream_aggregation_options:
-        - cache_mode (str): The user ID to use if the user ID is not found; Example: 'batch'; Default: 'batch'
+        - cache_mode (str): Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are
+          met. Otherwise, continue to aggregate user's history.; Example: 'batch'; Default: 'batch'
         - trigger_on_min_history (int): Minimum history to trigger a new training event; Example: 1; Default: 1
         - trigger_on_min_increment (int): Minmum increment from the last trained to new training event;
         Example: 0; Default: 0

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

@@ -94,7 +94,8 @@ def dfp_inference_pipe(builder: mrc.Builder):
             - timestamp_column_name (str): Name of the timestamp column; Example: "timestamp"; Default: timestamp
 
         stream_aggregation_options:
-            - cache_mode (str): The user ID to use if the user ID is not found; Example: 'batch'; Default: 'batch'
+            - cache_mode (str): Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions
+              are met. Otherwise, continue to aggregate user's history.; Example: 'batch'; Default: 'batch'
             - trigger_on_min_history (int): Minimum history to trigger a new training event; Example: 1; Default: 1
             - trigger_on_min_increment (int): Minimum increment from the last trained to new training event;
             Example: 0; Default: 0

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

@@ -48,7 +48,8 @@ def dfp_rolling_window(builder: mrc.Builder):
     Notes
     -----
     Configurable parameters:
-        - cache_mode (str): The user ID to use if the user ID is not found; Example: 'batch'; Default: 'batch'
+        - cache_mode (str): Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are
+          met. Otherwise, continue to aggregate user's history.; Example: 'batch'; Default: 'batch'
         - trigger_on_min_history (int): Minimum history to trigger a new training event; Example: 1; Default: 1
         - trigger_on_min_increment (int): Minmum increment from the last trained to new training event;
         Example: 0; Default: 0

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

@@ -87,7 +87,8 @@ def dfp_training_pipe(builder: mrc.Builder):
             - timestamp_column_name (str): Name of the timestamp column; Example: `timestamp`; Default: timestamp
 
         stream_aggregation_options:
-            - cache_mode (str): The user ID to use if the user ID is not found; Example: 'batch'; Default: 'batch'
+            - cache_mode (str): Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions
+              are met. Otherwise, continue to aggregate user's history.; Example: 'batch'; Default: 'batch'
             - trigger_on_min_history (int): Minimum history to trigger a new training event; Example: 1; Default: 1
             - trigger_on_min_increment (int): Minimum increment from the last trained to new training event;
             Example: 0; Default: 0

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

@@ -84,7 +84,7 @@ def infer_module_conf(self):
             "stream_aggregation_options": {
                 "aggregation_span": "1d",
                 "cache_to_disk": False,
-                "cache_mode": "streaming",
+                "cache_mode": "batch",
             },
             "preprocessing_options": {
                 "schema": {
@@ -139,7 +139,7 @@ def train_module_conf(self):
             "stream_aggregation_options": {
                 "aggregation_span": "60d",
                 "cache_to_disk": False,
-                "cache_mode": "streaming",
+                "cache_mode": "aggregate",
                 "trigger_on_min_history": 300,
                 "trigger_on_min_increment": 300
             },

examples/digital_fingerprinting/production/morpheus/notebooks/dfp_azure_integrated_training.ipynb

@@ -309,7 +309,7 @@
     "\n",
     "| Parameter               | Type   | Description                                                 | Example Value | Default Value |\n",
     "|-------------------------|--------|-------------------------------------------------------------|---------------|---------------|\n",
-    "| `cache_mode`            | string | The user ID to use if the user ID is not found              | \"batch\"       | `batch`       |\n",
+    "| `cache_mode`            | string | Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are met. Otherwise, continue to aggregate user's history.              | \"batch\"       | `batch`       |\n",
     "| `min_history`           | int    | Minimum history to trigger a new training event             | 1             | `1`           |\n",
     "| `max_history`           | int    | Maximum history to include in a new training event          | 0             | `0`           |\n",
     "| `timestamp_column_name` | string | Name of the column containing timestamps                    | \"timestamp\"   | `timestamp`   |\n",

examples/digital_fingerprinting/production/morpheus/notebooks/dfp_duo_integrated_training.ipynb

@@ -311,7 +311,7 @@
     "\n",
     "| Parameter               | Type   | Description                                                 | Example Value | Default Value |\n",
     "|-------------------------|--------|-------------------------------------------------------------|---------------|---------------|\n",
-    "| `cache_mode`            | string | The user ID to use if the user ID is not found              | \"batch\"       | `batch`       |\n",
+    "| `cache_mode`            | string | Mode for managing user cache. Setting to `batch` flushes cache once trigger conditions are met. Otherwise, continue to aggregate user's history. | \"batch\"       | `batch`       |\n",
     "| `min_history`           | int    | Minimum history to trigger a new training event             | 1             | `1`           |\n",
     "| `max_history`           | int    | Maximum history to include in a new training event          | 0             | `0`           |\n",
     "| `timestamp_column_name` | string | Name of the column containing timestamps                    | \"timestamp\"   | `timestamp`   |\n",