fix: MLFlow E2E Example Notebook (5513)

Author: aviruthen

v3-examples/inference-examples/train-inference-e2e-example.ipynb

@@ -61,7 +61,7 @@
     "TRAINING_JOB_PREFIX = \"e2e-v3-pytorch\"\n",
     "\n",
     "# AWS Configuration\n",
-    "AWS_REGION = Session.boto_region_name\n",
+    "AWS_REGION = Session().boto_region_name\n",
     "PYTORCH_TRAINING_IMAGE = f\"763104351884.dkr.ecr.{AWS_REGION}.amazonaws.com/pytorch-training:1.13.1-cpu-py39\"\n",
     "\n",
     "# Generate unique identifiers\n",

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

@@ -71,7 +71,7 @@
     "MLFLOW_TRACKING_ARN = \"XXXXX\"\n",
     "\n",
     "# AWS Configuration\n",
-    "AWS_REGION = Session.boto_region_name\n",
+    "AWS_REGION = Session().boto_region_name\n",
     "\n",
     "# Get PyTorch training image dynamically\n",
     "PYTORCH_TRAINING_IMAGE = image_uris.retrieve(\n",
@@ -330,25 +330,33 @@
    "outputs": [],
    "source": [
     "# Get the latest version of the registered model\n",
+    "# NOTE: MLflow 3.x removed `registered_model.latest_versions`. Use\n",
+    "# `client.search_model_versions()` instead.\n",
     "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",
+    "# Search for the latest version of the registered model (MLflow 3.x compatible)\n",
+    "versions = client.search_model_versions(\n",
+    "    filter_string=f\"name='{MLFLOW_REGISTERED_MODEL_NAME}'\",\n",
+    "    order_by=['version_number DESC'],\n",
+    "    max_results=1\n",
+    ")\n",
+    "\n",
+    "if not versions:\n",
+    "    raise ValueError(f\"No versions found for model '{MLFLOW_REGISTERED_MODEL_NAME}'\")\n",
+    "\n",
+    "latest_version = versions[0]\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 (artifact location): {model_source}\")\n",
+    "print(f\"MLflow model path for deployment: {mlflow_model_path}\")"
    ]
   },
   {
@@ -427,6 +435,8 @@
     "from sagemaker.serve.mode.function_pointers import Mode\n",
     "\n",
     "# Cloud deployment to SageMaker endpoint\n",
+    "# Note: 'dependencies' parameter is deprecated. You may see a deprecation warning.\n",
+    "# Use configure_for_torchserve() for new projects.\n",
     "model_builder = ModelBuilder(\n",
     "    mode=Mode.SAGEMAKER_ENDPOINT,\n",
     "    schema_builder=schema_builder,\n",
@@ -481,23 +491,43 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import boto3\n",
-    "\n",
-    "# Test with JSON input\n",
+    "# Test with JSON input using V3-native endpoint invocation\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",
+    "prediction = json.loads(result.body.read().decode('utf-8'))\n",
     "print(f\"Input: {test_data}\")\n",
     "print(f\"Prediction: {prediction}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test with multiple inputs\n",
+    "test_inputs = [\n",
+    "    [[0.5, 0.3, 0.2, 0.1]],\n",
+    "    [[0.9, 0.1, 0.8, 0.2]],\n",
+    "    [[0.2, 0.7, 0.4, 0.6]]\n",
+    "]\n",
+    "\n",
+    "for i, test_input in enumerate(test_inputs, 1):\n",
+    "    result = core_endpoint.invoke(\n",
+    "        body=json.dumps(test_input),\n",
+    "        content_type='application/json'\n",
+    "    )\n",
+    "    \n",
+    "    prediction = json.loads(result.body.read().decode('utf-8'))\n",
+    "    print(f\"Test {i} - Input {test_input}: {prediction}\")\n",
+    "    print('-' * 50)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -551,7 +581,12 @@
     "- `ModelBuilder` with `MLFLOW_MODEL_PATH` - deploy from registry\n",
     "\n",
     "Key patterns:\n",
-    "- Custom `PayloadTranslator` classes for PyTorch tensor serialization\n"
+    "- Custom `PayloadTranslator` classes for PyTorch tensor serialization\n",
+    "- V3-native `core_endpoint.invoke()` for inference\n",
+    "\n",
+    "**MLflow 3.x API Note:**\n",
+    "- Use `client.search_model_versions()` instead of the removed `registered_model.latest_versions` attribute\n",
+    "- Use `latest_version.source` for artifact location instead of `client.get_model_version_download_uri()`\n"
    ]
   }
  ],