Merge branch 'master' into feature-store-lakeformation

Author: mollyheamazon

sagemaker-mlops/src/sagemaker/mlops/workflow/steps.py

@@ -205,7 +205,7 @@ def _find_dependencies_in_step_arguments(
                 else:
                     dependencies.add(self._get_step_name_from_str(referenced_step, step_map))
 
-            from sagemaker.core.workflow.function_step import DelayedReturn
+            from sagemaker.mlops.workflow.function_step import DelayedReturn
 
             # TODO: we can remove the if-elif once move the validators to JsonGet constructor
             if isinstance(pipeline_variable, JsonGet):

sagemaker-mlops/tests/unit/workflow/test_steps.py

@@ -409,7 +409,7 @@ def test_step_find_dependencies_in_step_arguments_with_json_get():
     obj = {"key": json_get}
 
     with patch('sagemaker.mlops.workflow.steps.TYPE_CHECKING', False):
-        with patch.dict('sys.modules', {'sagemaker.core.workflow.function_step': Mock()}):
+        with patch.dict('sys.modules', {'sagemaker.mlops.workflow.function_step': Mock()}):
             dependencies = Step._find_dependencies_in_step_arguments(step2, obj, {"step1": step1})
             assert "step1" in dependencies
 
@@ -445,7 +445,7 @@ def test_step_find_dependencies_in_step_arguments_with_delayed_return():
     mock_module = Mock()
     mock_module.DelayedReturn = delayed_return_class
 
-    with patch.dict('sys.modules', {'sagemaker.core.workflow.function_step': mock_module}):
+    with patch.dict('sys.modules', {'sagemaker.mlops.workflow.function_step': mock_module}):
         dependencies = Step._find_dependencies_in_step_arguments(step2, obj, {"step1": step1})
         assert "step1" in dependencies
 
@@ -473,7 +473,7 @@ def test_step_find_dependencies_in_step_arguments_with_string_reference():
     mock_module = Mock()
     mock_module.DelayedReturn = delayed_return_class
 
-    with patch.dict('sys.modules', {'sagemaker.core.workflow.function_step': mock_module}):
+    with patch.dict('sys.modules', {'sagemaker.mlops.workflow.function_step': mock_module}):
         dependencies = Step._find_dependencies_in_step_arguments(step2, obj, step_map)
         assert "step1" in dependencies
 

v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb

@@ -333,22 +333,24 @@
     "from mlflow import MlflowClient\n",
     "\n",
     "client = MlflowClient()\n",
-    "registered_model = client.get_registered_model(name=MLFLOW_REGISTERED_MODEL_NAME)\n",
     "\n",
-    "latest_version = registered_model.latest_versions[0]\n",
+    "# MLflow 3.x removed RegisteredModel.latest_versions - use search_model_versions instead\n",
+    "latest_version = client.search_model_versions(\n",
+    "    filter_string=f\"name='{MLFLOW_REGISTERED_MODEL_NAME}'\",\n",
+    "    order_by=[\"version_number DESC\"],\n",
+    "    max_results=1\n",
+    ")[0]\n",
+    "\n",
     "model_version = latest_version.version\n",
     "model_source = latest_version.source\n",
     "\n",
-    "# Get S3 URL of model files (for info only)\n",
-    "artifact_uri = client.get_model_version_download_uri(MLFLOW_REGISTERED_MODEL_NAME, model_version)\n",
-    "\n",
     "# MLflow model registry path to use with ModelBuilder\n",
     "mlflow_model_path = f\"models:/{MLFLOW_REGISTERED_MODEL_NAME}/{model_version}\"\n",
     "\n",
     "print(f\"Registered Model: {MLFLOW_REGISTERED_MODEL_NAME}\")\n",
     "print(f\"Latest Version: {model_version}\")\n",
-    "print(f\"Source: {model_source}\")\n",
-    "print(f\"Model artifacts location: {artifact_uri}\")"
+    "print(f\"Source (model artifacts location): {model_source}\")\n",
+    "print(f\"MLflow model path for deployment: {mlflow_model_path}\")"
    ]
   },
   {
@@ -481,19 +483,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import boto3\n",
-    "\n",
     "# Test with JSON input\n",
     "test_data = [[0.1, 0.2, 0.3, 0.4]]\n",
     "\n",
-    "runtime_client = boto3.client('sagemaker-runtime')\n",
-    "response = runtime_client.invoke_endpoint(\n",
-    "    EndpointName=core_endpoint.endpoint_name,\n",
-    "    Body=json.dumps(test_data),\n",
-    "    ContentType='application/json'\n",
+    "result = core_endpoint.invoke(\n",
+    "    body=json.dumps(test_data),\n",
+    "    content_type=\"application/json\"\n",
     ")\n",
     "\n",
-    "prediction = json.loads(response['Body'].read().decode('utf-8'))\n",
+    "# Decode and display the result\n",
+    "prediction = json.loads(result.body.read().decode('utf-8'))\n",
     "print(f\"Input: {test_data}\")\n",
     "print(f\"Prediction: {prediction}\")"
    ]