Network note (#3466)

* adding prereq about isolated network requirements

* adding prereq about isolated network requirements

tutorials/get-started-notebooks/deploy-model.ipynb

@@ -47,7 +47,9 @@
     "1. Open in the studio and select a compute instance.\n",
     "    * If you opened this notebook from Azure Machine Learning studio, you need a compute instance to run the code. If you don't have a compute instance, select **Create compute** on the toolbar to first create one.  You can use all the default settings.  \n",
     "    \n",
-    "    ![Create compute](./media/create-compute.png)\n",
+    "        ![Create compute](./media/create-compute.png)\n",
+    "\n",
+    "    * If your Azure Machine Learning workspace is configured with a managed virtual network, you may need to add outbound rules to allow access to the public Python package repositories. For more information, see [Scenario: Access public machine learning packages](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network#scenario-access-public-machine-learning-packages).\n",
     "    \n",
     "    * If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
     "    \n",

tutorials/get-started-notebooks/explore-data.ipynb

@@ -28,6 +28,8 @@
     "\n",
     "    ![Create compute](./media/create-compute.png)\n",
     "\n",
+    "* If your Azure Machine Learning workspace is configured with a managed virtual network, you may need to add outbound rules to allow access to the public Python package repositories. For more information, see [Scenario: Access public machine learning packages](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network#scenario-access-public-machine-learning-packages).\n",
+    "\n",
     "* If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
     "\n",
     "## Set your kernel\n",

tutorials/get-started-notebooks/pipeline.ipynb

@@ -45,6 +45,8 @@
     "\n",
     "    ![Create compute](./media/create-compute.png)\n",
     "\n",
+    "* If your Azure Machine Learning workspace is configured with a managed virtual network, you may need to add outbound rules to allow access to the public Python package repositories. For more information, see [Scenario: Access public machine learning packages](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network#scenario-access-public-machine-learning-packages).\n",
+    "\n",
     "* If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
     "\n",
     "* Complete the tutorial [Tutorial: Upload, access and explore your data](explore-data.ipynb) to create the data asset you need in this tutorial.  Make sure you run all the code to create the initial data asset.  You can optionally explore the data and revise it, but you'll only need the initial data to complete this tutorial.\n",

tutorials/get-started-notebooks/quickstart.ipynb

@@ -34,6 +34,8 @@
     "\n",
     "    ![Create compute](./media/create-compute.png)\n",
     "\n",
+    "* If your Azure Machine Learning workspace is configured with a managed virtual network, you may need to add outbound rules to allow access to the public Python package repositories. For more information, see [Scenario: Access public machine learning packages](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network#scenario-access-public-machine-learning-packages).\n",
+    "\n",
     "* If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
     "\n",
     "## Set your kernel\n",
@@ -81,7 +83,18 @@
     },
     "name": "ml_client"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.8' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: 'c:/Users/larryfr/AppData/Local/Programs/Python/Python312/python.exe -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "from azure.ai.ml import MLClient\n",
     "from azure.identity import DefaultAzureCredential\n",
@@ -692,9 +705,9 @@
    "name": "python310-sdkv2"
   },
   "kernelspec": {
-   "display_name": "Python 3.10 - SDK v2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python310-sdkv2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -706,7 +719,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.12.8"
   },
   "microsoft": {
    "ms_spell_check": {

tutorials/get-started-notebooks/train-model.ipynb

@@ -33,6 +33,8 @@
     "\n",
     "    ![Create compute](./media/create-compute.png)\n",
     "\n",
+    "* If your Azure Machine Learning workspace is configured with a managed virtual network, you may need to add outbound rules to allow access to the public Python package repositories. For more information, see [Scenario: Access public machine learning packages](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network#scenario-access-public-machine-learning-packages).\n",
+    "\n",
     "* If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
     "\n",
     "## Set your kernel\n",