Rename model version to a model

.gitignore

@@ -133,3 +133,4 @@ dmypy.json
 
 *.zen
 .vscode
+.local

template/configs/inference.yaml

@@ -7,7 +7,7 @@ settings:
       - pyarrow
 
 # configuration of the Model Control Plane
-model_version:
+model:
   name: "breast_cancer_classifier"
   version: "production"
   license: Apache 2.0

template/configs/training_rf.yaml

@@ -7,7 +7,7 @@ settings:
       - pyarrow
 
 # configuration of the Model Control Plane
-model_version:
+model:
   name: breast_cancer_classifier
   version: rf
   license: Apache 2.0

template/configs/training_sgd.yaml

@@ -7,7 +7,7 @@ settings:
       - pyarrow
 
 # configuration of the Model Control Plane
-model_version:
+model:
   name: breast_cancer_classifier
   version: sgd
   license: Apache 2.0

template/pipelines/inference.py

@@ -25,10 +25,10 @@ def inference(random_state: str, target: str):
         target: Name of target column in dataset.
     """
     # Get the production model artifact
-    model = get_pipeline_context().model_version.get_artifact("sklearn_classifier")
+    model = get_pipeline_context().model.get_artifact("sklearn_classifier")
 
     # Get the preprocess pipeline artifact associated with this version
-    preprocess_pipeline = get_pipeline_context().model_version.get_artifact(
+    preprocess_pipeline = get_pipeline_context().model.get_artifact(
         "preprocess_pipeline"
     )
 

template/quickstart.ipynb

@@ -142,7 +142,7 @@
     "\n",
     "import random\n",
     "import pandas as pd\n",
-    "from zenml import step, ExternalArtifact, pipeline, ModelVersion, get_step_context\n",
+    "from zenml import step, ExternalArtifact, pipeline, Model, get_step_context\n",
     "from zenml.client import Client\n",
     "from zenml.logger import get_logger\n",
     "from uuid import UUID\n",
@@ -729,7 +729,7 @@
     "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n",
     "which is a central register of all your ML models.\n",
     "\n",
-    "You can easily create a ZenML `Model` and associate it with your pipelines using the `ModelVersion` object:"
+    "You can easily create a ZenML Model and associate it with your pipelines using the `Model` object:"
    ]
   },
   {
@@ -742,7 +742,7 @@
     "pipeline_settings = {}\n",
     "\n",
     "# Lets add some metadata to the model to make it identifiable\n",
-    "pipeline_settings[\"model_version\"] = ModelVersion(\n",
+    "pipeline_settings[\"model\"] = Model(\n",
     "    name=\"breast_cancer_classifier\",\n",
     "    license=\"Apache 2.0\",\n",
     "    description=\"A breast cancer classifier\",\n",
@@ -758,7 +758,7 @@
    "outputs": [],
    "source": [
     "# Let's train the SGD model and set the version name to \"sgd\"\n",
-    "pipeline_settings[\"model_version\"].version = \"sgd\"\n",
+    "pipeline_settings[\"model\"].version = \"sgd\"\n",
     "\n",
     "# the `with_options` method allows us to pass in pipeline settings\n",
     "#  and returns a configured pipeline\n",
@@ -780,7 +780,7 @@
    "outputs": [],
    "source": [
     "# Let's train the RF model and set the version name to \"rf\"\n",
-    "pipeline_settings[\"model_version\"].version = \"rf\"\n",
+    "pipeline_settings[\"model\"].version = \"rf\"\n",
     "\n",
     "# the `with_options` method allows us to pass in pipeline settings\n",
     "#  and returns a configured pipeline\n",
@@ -939,11 +939,11 @@
     "@step\n",
     "def inference_predict(dataset_inf: pd.DataFrame) -> Annotated[pd.Series, \"predictions\"]:\n",
     "    \"\"\"Predictions step\"\"\"\n",
-    "    # Get the model_version\n",
-    "    model_version = get_step_context().model_version\n",
+    "    # Get the model\n",
+    "    model = get_step_context().model\n",
     "\n",
     "    # run prediction from memory\n",
-    "    predictor = model_version.load_artifact(\"sklearn_classifier\")\n",
+    "    predictor = model.load_artifact(\"sklearn_classifier\")\n",
     "    predictions = predictor.predict(dataset_inf)\n",
     "\n",
     "    predictions = pd.Series(predictions, name=\"predicted\")\n",
@@ -994,7 +994,7 @@
    "id": "c7afe7be",
    "metadata": {},
    "source": [
-    "The way to load the right model is to pass in the `production` stage into the `ModelVersion` config this time.\n",
+    "The way to load the right model is to pass in the `production` stage into the `Model` config this time.\n",
     "This will ensure to always load the production model, decoupled from all other pipelines:"
    ]
   },
@@ -1008,7 +1008,7 @@
     "pipeline_settings = {\"enable_cache\": False}\n",
     "\n",
     "# Lets add some metadata to the model to make it identifiable\n",
-    "pipeline_settings[\"model_version\"] = ModelVersion(\n",
+    "pipeline_settings[\"model\"] = Model(\n",
     "    name=\"breast_cancer_classifier\",\n",
     "    version=\"production\", # We can pass in the stage name here!\n",
     "    license=\"Apache 2.0\",\n",

template/run.py

@@ -201,7 +201,7 @@ def main(
         with open(pipeline_args["config_path"], "r") as f:
             config = yaml.load(f, Loader=yaml.SafeLoader)
         zenml_model = client.get_model_version(
-            config["model_version"]["name"], config["model_version"]["version"]
+            config["model"]["name"], config["model"]["version"]
         )
         preprocess_pipeline_artifact = zenml_model.get_artifact("preprocess_pipeline")
 

template/steps/model_promoter.py

@@ -36,26 +36,26 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool:
         is_promoted = True
 
         # Get the model in the current context
-        current_model_version = get_step_context().model_version
+        current_model = get_step_context().model
 
         # Get the model that is in the production stage
         client = Client()
         try:
-            stage_model_version = client.get_model_version(
-                current_model_version.name, stage
+            stage_model = client.get_model_version(
+                current_model.name, stage
             )
             # We compare their metrics
             prod_accuracy = (
-                stage_model_version.get_artifact("sklearn_classifier")
+                stage_model.get_artifact("sklearn_classifier")
                 .run_metadata["test_accuracy"]
                 .value
             )
             if float(accuracy) > float(prod_accuracy):
                 # If current model has better metrics, we promote it
                 is_promoted = True
-                current_model_version.set_stage(stage, force=True)
+                current_model.set_stage(stage, force=True)
         except KeyError:
             # If no such model exists, current one is promoted
             is_promoted = True
-            current_model_version.set_stage(stage, force=True)
+            current_model.set_stage(stage, force=True)
     return is_promoted