Replace ensemble_model with executor_model

examples/Building-and-deploying-multi-stage-RecSys/02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb

@@ -657,7 +657,7 @@
       "│ ├─1/\n",
       "│ │ └─model.py\n",
       "│ └─config.pbtxt\n",
-      "└─ensemble_model/\n",
+      "└─executor_model/\n",
       "  ├─1/\n",
       "  └─config.pbtxt\n"
      ]

examples/sagemaker-tensorflow/sagemaker-merlin-tensorflow.ipynb

@@ -956,9 +956,9 @@
       "1_predicttensorflow/1/model.savedmodel/variables/variables.data-00000-of-00001\n",
       "1_predicttensorflow/1/model.savedmodel/saved_model.pb\n",
       "1_predicttensorflow/1/model.savedmodel/keras_metadata.pb\n",
-      "ensemble_model/\n",
-      "ensemble_model/config.pbtxt\n",
-      "ensemble_model/1/\n",
+      "executor_model/\n",
+      "executor_model/config.pbtxt\n",
+      "executor_model/1/\n",
       "0_transformworkflow/\n",
       "0_transformworkflow/config.pbtxt\n",
       "0_transformworkflow/1/\n",
@@ -999,7 +999,7 @@
     "\n",
     "Although we use the Sagemaker Python SDK to train our model, here we will use `boto3` to launch our inference endpoint as it offers more low-level control than the Python SDK.\n",
     "\n",
-    "The model artificat `model.tar.gz` uploaded to S3 from the Sagemaker training job contained three directories: `0_transformworkflow` for the NVTabular workflow, `1_predicttensorflow` for the Tensorflow model, and `ensemble_model` for the ensemble graph that we can use in Triton.\n",
+    "The model artificat `model.tar.gz` uploaded to S3 from the Sagemaker training job contained three directories: `0_transformworkflow` for the NVTabular workflow, `1_predicttensorflow` for the Tensorflow model, and `executor_model` for the ensemble graph that we can use in Triton.\n",
     "\n",
     "```shell\n",
     "/tmp/ensemble/\n",
@@ -1037,13 +1037,13 @@
     "│   │           ├── variables.data-00000-of-00001\n",
     "│   │           └── variables.index\n",
     "│   └── config.pbtxt\n",
-    "├── ensemble_model\n",
+    "├── executor_model\n",
     "│   ├── 1\n",
     "│   └── config.pbtxt\n",
     "└── model.tar.gz\n",
     "```\n",
     "\n",
-    "We specify that we only want to use `ensemble_model` in Triton by passing the environment variable `SAGEMAKER_TRITON_DEFAULT_MODEL_NAME`."
+    "We specify that we only want to use `executor_model` in Triton by passing the environment variable `SAGEMAKER_TRITON_DEFAULT_MODEL_NAME`."
    ]
   },
   {
@@ -1072,7 +1072,7 @@
     "    \"ModelDataUrl\": estimator.model_data,\n",
     "    \"Environment\": {\n",
     "        \"SAGEMAKER_TRITON_TENSORFLOW_VERSION\": \"2\",\n",
-    "        \"SAGEMAKER_TRITON_DEFAULT_MODEL_NAME\": \"ensemble_model\",\n",
+    "        \"SAGEMAKER_TRITON_DEFAULT_MODEL_NAME\": \"executor_model\",\n",
     "    },\n",
     "}\n",
     "\n",

examples/scaling-criteo/04-Triton-Inference-with-Merlin-Models-TensorFlow.ipynb

@@ -318,7 +318,7 @@
    "source": [
     "# send request to tritonserver\n",
     "with grpcclient.InferenceServerClient(\"localhost:8001\") as client:\n",
-    "    response = client.infer(\"ensemble_model\", inputs, request_id=\"1\", outputs=outputs)"
+    "    response = client.infer(\"executor_model\", inputs, request_id=\"1\", outputs=outputs)"
    ]
   },
   {

tests/integration/examples/test_ci_building_deploying_multi_stage_RecSys.py

@@ -79,7 +79,7 @@ def test_func():
             request = make_df({"user_id_raw": [100]})
             request["user_id_raw"] = request["user_id_raw"].astype(np.int32)
             response = run_ensemble_on_tritonserver(
-                "/tmp/examples/poc_ensemble", ensemble.graph.input_schema, request, outputs,  "ensemble_model"
+                "/tmp/examples/poc_ensemble", ensemble.graph.input_schema, request, outputs,  "executor_model"
             )
             response = [x.tolist()[0] for x in response["ordered_ids"]]
             shutil.rmtree("/tmp/examples/", ignore_errors=True)

tests/unit/examples/test_building_deploying_multi_stage_RecSys.py

@@ -72,7 +72,7 @@ def test_func():
             )
             from merlin.systems.triton.utils import run_ensemble_on_tritonserver
             response = run_ensemble_on_tritonserver(
-                "/tmp/examples/poc_ensemble", ensemble.graph.input_schema, batch, outputs,  "ensemble_model"
+                "/tmp/examples/poc_ensemble", ensemble.graph.input_schema, batch, outputs,  "executor_model"
             )
             response = [x.tolist()[0] for x in response["ordered_ids"]]
             shutil.rmtree("/tmp/examples/", ignore_errors=True)

tests/unit/examples/test_scaling_criteo_merlin_models.py

@@ -88,7 +88,7 @@ def test_func():
             outputs = ensemble.output_schema.column_names
             response = run_ensemble_on_tritonserver(
                 "/tmp/output/criteo/ensemble/",workflow.input_schema, batch.fillna(0),
-                outputs, "ensemble_model"
+                outputs, "executor_model"
             )
             response = [x.tolist()[0] for x in response["label/binary_classification_task"]]
             shutil.rmtree("/tmp/input/criteo", ignore_errors=True)