aws · mollyheamazon · Feb 4, 2026 · Feb 4, 2026
diff --git a/sagemaker-core/example_notebooks/get_started.ipynb b/sagemaker-core/example_notebooks/get_started.ipynb
@@ -187,7 +187,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image = '141502667606.dkr.ecr.eu-west-1.amazonaws.com/sagemaker-xgboost:1.7-1'"
+    "from sagemaker.core import image_uris\n",
+    "\n",
+    "image = image_uris.retrieve(\n",
+    "    framework=\"xgboost\",\n",
+    "    region=region,\n",
+    "    version='1.7-1'\n",
+    ")"
    ]
   },
   {
@@ -687,7 +693,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "temp_env",
    "language": "python",
    "name": "python3"
   },

diff --git a/sagemaker-core/example_notebooks/inference_and_resource_chaining.ipynb b/sagemaker-core/example_notebooks/inference_and_resource_chaining.ipynb
@@ -241,11 +241,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Image name is hardcoded here\n",
-    "# Image name can be programatically got by using sagemaker package and calling image_uris.retrieve\n",
-    "# Since that is a high level abstraction that has multiple dependencies, the image URIs functionalities will live in sagemaker (V2)\n",
+    "from sagemaker.core import image_uris\n",
     "\n",
-    "image = \"433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\""
+    "image = image_uris.retrieve(\n",
+    "    framework=\"xgboost\",\n",
+    "    region=region,\n",
+    "    version='latest'\n",
+    ")"
    ]
   },
   {

diff --git a/sagemaker-core/example_notebooks/intelligent_defaults_and_logging.ipynb b/sagemaker-core/example_notebooks/intelligent_defaults_and_logging.ipynb
@@ -269,11 +269,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Image name is hardcoded here\n",
-    "# Image name can be programatically got by using sagemaker package and calling image_uris.retrieve\n",
-    "# Since that is a high level abstraction that has multiple dependencies, the image URIs functionalities will live in sagemaker (V2)\n",
+    "from sagemaker.core import image_uris\n",
     "\n",
-    "image = \"433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\""
+    "image = image_uris.retrieve(\n",
+    "    framework=\"xgboost\",\n",
+    "    region=region,\n",
+    "    version='latest'\n",
+    ")"
    ]
   },
   {

diff --git a/sagemaker-core/example_notebooks/sagemaker_core_overview.ipynb b/sagemaker-core/example_notebooks/sagemaker_core_overview.ipynb
@@ -255,11 +255,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Image name is hardcoded here\n",
-    "# Image name can be programatically got by using sagemaker package and calling image_uris.retrieve\n",
-    "# Since that is a high level abstraction that has multiple dependencies, the image URIs functionalities will live in sagemaker (V2)\n",
+    "from sagemaker.core import image_uris\n",
     "\n",
-    "image = \"433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\""
+    "image = image_uris.retrieve(\n",
+    "    framework=\"xgboost\",\n",
+    "    region=region,\n",
+    "    version='latest'\n",
+    ")"
    ]
   },
   {

diff --git a/sagemaker-train/example_notebooks/evaluate/benchmark_demo.ipynb b/sagemaker-train/example_notebooks/evaluate/benchmark_demo.ipynb
diff --git a/sagemaker-train/example_notebooks/evaluate/custom_scorer_demo.ipynb b/sagemaker-train/example_notebooks/evaluate/custom_scorer_demo.ipynb
diff --git a/sagemaker-train/example_notebooks/evaluate/llm_as_judge_demo.ipynb b/sagemaker-train/example_notebooks/evaluate/llm_as_judge_demo.ipynb
diff --git a/v3-examples/inference-examples/optimize-example.ipynb b/v3-examples/inference-examples/optimize-example.ipynb
@@ -54,8 +54,8 @@
     "MODEL_ID = \"meta-textgeneration-llama-3-8b-instruct\"\n",
     "MODEL_NAME_PREFIX = \"jumpstart-optimize-example\"\n",
     "ENDPOINT_NAME_PREFIX = \"jumpstart-optimize-example-endpoint\"\n",
-    "AWS_ACCOUNT_ID = \"593793038179\"\n",
-    "AWS_REGION = \"us-east-2\"\n",
+    "AWS_ACCOUNT_ID = Session.account_id()\n",
+    "AWS_REGION = Session.boto_region_name\n",
     "\n",
     "# Generate unique identifiers\n",
     "unique_id = str(uuid.uuid4())[:8]\n",

diff --git a/v3-examples/inference-examples/train-inference-e2e-example.ipynb b/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 = \"us-west-2\"\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",

diff --git a/v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb b/v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb
@@ -34,8 +34,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install from local SDK for development (includes fixes for MLflow path resolution issues)\n",
-    "%pip install -e ../../sagemaker-core -e ../../sagemaker-train -e ../../sagemaker-serve -e ../../sagemaker-mlops -e ../../. \"mlflow==3.4.0\" --upgrade"
+    "# Install fix for MLflow path resolution issues\n",
+    "%pip install mlflow==3.4.0"
    ]
   },
   {
@@ -62,6 +62,7 @@
    "source": [
     "import uuid\n",
     "from sagemaker.core import image_uris\n",
+    "from sagemaker.core.helper.session_helper import Session\n",
     "\n",
     "# =============================================================================\n",
     "# MLflow Configuration - UPDATE THIS WITH YOUR TRACKING SERVER ARN\n",
@@ -70,7 +71,7 @@
     "MLFLOW_TRACKING_ARN = \"XXXXX\"\n",
     "\n",
     "# AWS Configuration\n",
-    "AWS_REGION = \"us-east-1\"\n",
+    "AWS_REGION = Session.boto_region_name\n",
     "\n",
     "# Get PyTorch training image dynamically\n",
     "PYTORCH_TRAINING_IMAGE = image_uris.retrieve(\n",
@@ -556,7 +557,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "py3.10.14",
    "language": "python",
    "name": "python3"
   },
@@ -570,7 +571,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

diff --git a/v3-examples/ml-ops-examples/v3-model-registry-example/v3-model-registry-example.ipynb b/v3-examples/ml-ops-examples/v3-model-registry-example/v3-model-registry-example.ipynb
@@ -23,15 +23,17 @@
     "from sagemaker.core import image_uris\n",
     "import boto3\n",
     "\n",
+    "sagemaker_session = Session()\n",
+    "role = get_execution_role()\n",
+    "region = sagemaker_session.boto_region_name\n",
+    "\n",
     "image_uri = image_uris.retrieve(\n",
     "    framework=\"xgboost\",\n",
-    "    region=\"us-east-1\",\n",
+    "    region=region,\n",
     "    version=\"1.0-1\",\n",
     "    py_version=\"py3\",\n",
     "    instance_type=\"ml.m5.xlarge\",\n",
-    ")\n",
-    "sagemaker_session = Session()\n",
-    "role = get_execution_role()"
+    ")"
    ]
   },
   {
@@ -181,7 +183,7 @@
     "# approve the version before creating model\n",
     "\n",
     "# There is a gap that API response for a versioned model package doesn't include model_package_name\n",
-    "sagemaker_client = boto3.client('sagemaker', region_name='us-east-1')\n",
+    "sagemaker_client = boto3.client('sagemaker', region_name=region)\n",
     "sagemaker_client.update_model_package(\n",
     "    ModelPackageArn=registered_model_package_arn,\n",
     "    ModelApprovalStatus=\"Approved\"\n",

diff --git a/v3-examples/model-customization-examples/sm-studio-nova-training-job-sample-notebook.ipynb b/v3-examples/model-customization-examples/sm-studio-nova-training-job-sample-notebook.ipynb
@@ -45,16 +45,6 @@
     "## Setup and Dependencies"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "84cf410f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade sagemaker --quiet  # restart the kernel after running this cell"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/v3-examples/sagemaker_v3_setup.ipynb b/v3-examples/sagemaker_v3_setup.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SageMaker Python SDK v3+ Setup\n",
+    "\n",
+    "This notebook helps you upgrade to SageMaker Python SDK v3+ and verify the installation.\n",
+    "\n",
+    "**⚠️ Important:** After running this notebook, restart your kernel before using SageMaker v3."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Check Current Version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip show sagemaker | grep Version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Install/Upgrade\n",
+    "\n",
+    "Choose **ONE** of the following methods:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Method 1: Standard Upgrade (Try this first)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --upgrade sagemaker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Method 2: Force Reinstall (If Method 1 fails)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --force-reinstall --no-cache-dir sagemaker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Method 3: Clean Uninstall + Reinstall (If residual files exist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip uninstall -y sagemaker sagemaker-core sagemaker-train sagemaker-serve sagemaker-mlops\n",
+    "!pip cache purge\n",
+    "!pip install sagemaker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Restart Kernel\n",
+    "\n",
+    "**⚠️ REQUIRED:** Click **Kernel → Restart Kernel** in the menu above, then continue to Step 4."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Verify Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip show sagemaker | grep Version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Verify Core Components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from sagemaker.core.helper.session_helper import Session\n",
+    "    from sagemaker.train import ModelTrainer\n",
+    "    from sagemaker.serve import ModelBuilder\n",
+    "    from sagemaker.mlops.workflow.pipeline import Pipeline\n",
+    "    print(\"✓ Core v3 modules imported successfully\")\n",
+    "    \n",
+    "    session = Session()\n",
+    "    print(f\"✓ Session created - Region: {session.boto_region_name}\")\n",
+    "    print(f\"✓ Default bucket: {session.default_bucket()}\")\n",
+    "    \n",
+    "except ImportError as e:\n",
+    "    print(f\"✗ Import failed: {e}\")\n",
+    "    print(\"Try Method 3 (Clean Uninstall + Reinstall)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Troubleshooting\n",
+    "\n",
+    "### Version still shows v2.x after upgrade\n",
+    "1. Restart kernel (Kernel → Restart Kernel)\n",
+    "2. Use Method 3 (Clean Uninstall + Reinstall)\n",
+    "\n",
+    "### Import errors after upgrade\n",
+    "Clear Python cache and restart kernel:\n",
+    "```python\n",
+    "!find . -type d -name __pycache__ -exec rm -r {} +\n",
+    "!find . -type f -name '*.pyc' -delete\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/v3-examples/training-examples/distributed-local-training-example.ipynb b/v3-examples/training-examples/distributed-local-training-example.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# SageMaker V3 Distributed Local Training Example\n",
     "\n",
-    "This notebook demonstrates how to run distributed training locally using SageMaker V3 ModelTrainer with multiple Docker containers."
+    "This notebook demonstrates how to run distributed training locally using SageMaker V3 ModelTrainer with multiple Docker containers. Note: This notebook will not run in SageMaker Studio. "
    ]
   },
   {

diff --git a/v3-examples/training-examples/local-training-example.ipynb b/v3-examples/training-examples/local-training-example.ipynb
@@ -6,7 +6,8 @@
    "source": [
     "# SageMaker V3 Local Training Example\n",
     "\n",
-    "This notebook demonstrates how to use SageMaker V3 ModelTrainer in Local Container mode for testing training jobs in Docker containers locally."
+    "This notebook demonstrates how to use SageMaker V3 ModelTrainer in Local Container mode for testing training jobs in Docker containers locally. \n",
+    "Note: This notebook will not run in SageMaker Studio. "
    ]
   },
   {