Merge pull request #76 from rastala/master

Update 00.configuration.ipynb

00.configuration.ipynb

@@ -101,11 +101,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core import Workspace\n",
+    "import os\n",
     "\n",
-    "subscription_id ='<subscription-id>'\n",
-    "resource_group ='<resource-group>'\n",
-    "workspace_name = '<workspace-name>'\n",
+    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<my-subscription-id>\")\n",
+    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<my-resource-group>\")\n",
+    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<my-workspace-name>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
     "\n",
     "try:\n",
     "   ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
@@ -142,15 +151,6 @@
     "Specify a region where your workspace will be located from the list of [Azure Machine Learning regions](https://linktoregions)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "workspace_region = \"eastus2\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -159,10 +159,11 @@
    "source": [
     "import os\n",
     "\n",
-    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", subscription_id)\n",
-    "resource_group = os.environ.get(\"RESOURCE_GROUP\", resource_group)\n",
-    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", workspace_name)\n",
-    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", workspace_region)"
+    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<my-subscription-id>\")\n",
+    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"my-aml-resource-group\")\n",
+    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"my-first-workspace\")\n",
+    "\n",
+    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"eastus2\")"
    ]
   },
   {
@@ -207,12 +208,92 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Success!\n",
-    "Great, you are ready to move on to the rest of the sample notebooks."
+    "## Create compute resources for your training experiments\n",
+    "\n",
+    "Many of the subsequent examples use BatchAI clusters to train models at scale. To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 8 nodes when busy."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your CPU cluster\n",
+    "cpu_cluster_name = \"cpucluster\"\n",
+    "\n",
+    "# Verify that cluster does not exist already\n",
+    "try:\n",
+    "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+    "    print('Found existing cluster, use it.')\n",
+    "except ComputeTargetException:\n",
+    "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
+    "                                                                autoscale_enabled=True,\n",
+    "                                                                cluster_min_nodes=0, \n",
+    "                                                                cluster_max_nodes=6)\n",
+    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+    "\n",
+    "cpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your GPU cluster\n",
+    "gpu_cluster_name = \"gpucluster\"\n",
+    "\n",
+    "# Check if cluster exists already\n",
+    "try:\n",
+    "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+    "    print('Found existing cluster, use it.')\n",
+    "except ComputeTargetException:\n",
+    "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+    "                                                                autoscale_enabled=True,\n",
+    "                                                                cluster_min_nodes=0, \n",
+    "                                                                cluster_max_nodes=6)\n",
+    "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+    "\n",
+    "gpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "Great, you are ready to move on to the rest of the sample notebooks. A good place to start is the [01.train-model tutorial](./tutorials/01.train-model.ipynb) to learn how to train and then deploy an image classification model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
   "kernelspec": {
    "display_name": "Python 3.6",
    "language": "python",