initialize

Connected Car Demo/.library.json

@@ -0,0 +1 @@
+{"name":"AML service - Connected Car Demo","id":"connected-car","created":"12/6/2018 12:42:23 AM +00:00","modified":"12/6/2018 12:42:23 AM +00:00","lastBackedUp":"","accessed":"12/6/2018 12:42:23 AM +00:00","clonedFrom":"99bdf672a9944a63bc0333b369cd37eb","cloneCount":0,"gitRepositoryUrl":null,"public":"True","starCount":0,"setupSteps":[]}

Connected Car Demo/aml_config/config.json

@@ -0,0 +1,5 @@
+{
+    "subscription_id": "2a779d6f-0806-4359-a6e8-f1fd57bb5dd7",
+    "resource_group": "devintersection-2018-aml-demo",
+    "workspace_name": "devintersection-workspace"
+}

Connected Car Demo/amlcompute-cluster.ipynb

@@ -0,0 +1,113 @@
+{
+  "cells": [
+    {
+      "metadata": {
+        "collapsed": true,
+        "trusted": false
+      },
+      "cell_type": "code",
+      "source": "",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "from azureml.core import Workspace\n\nsubscription_id = '2a779d6f-0806-4359-a6e8-f1fd57bb5dd7' \nresource_group  = 'devintersection-2018-aml-demo'\nworkspace_name  = 'devintersection-workspace'\n\ntry:\n    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n    ws.write_config()\n    print('Library configuration succeeded')\nexcept:\n    print('Workspace not found')",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": "Wrote the config file config.json to: /home/nbuser/library/aml_config/config.json\nLibrary configuration succeeded\n",
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "from azureml.core.compute import ComputeTarget, AmlCompute\nfrom azureml.core.compute_target import ComputeTargetException\n\n# choose a name for your cluster\ncluster_name = \"cpucluster\"\n\ntry:\n    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n    print('Found existing compute target.')\nexcept ComputeTargetException:\n    print('Creating a new compute target...')\n    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D12_V2',\n                                                           max_nodes=4)\n\n    # create the cluster\n    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n\n    compute_target.wait_for_completion(show_output=True)\n\n# Use the 'status' property to get a detailed status for the current AmlCompute. \nprint(compute_target.status.serialize())",
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": "Creating a new compute target...\nCreating.\nAmlCompute wait for completion finished\nMinimum number of nodes requested have been provisioned\n{'allocationState': 'steady', 'allocationStateTransitionTime': '2018-12-04T18:13:30.463000+00:00', 'creationTime': '2018-12-04T18:13:10.187000+00:00', 'currentNodeCount': 0, 'errors': None, 'nodeStateCounts': {'idleNodeCount': 0, 'leavingNodeCount': 0, 'preparingNodeCount': 0, 'runningNodeCount': 0, 'unusableNodeCount': 0}, 'provisioningState': 'succeeded', 'provisioningStateTransitionTime': '2018-12-04T18:13:29.797000+00:00', 'scaleSettings': {'manual': None, 'autoScale': {'maximumNodeCount': 4, 'minimumNodeCount': 0, 'initialNodeCount': 0}}, 'vmPriority': 'dedicated', 'vmSize': 'STANDARD_D12_V2'}\n",
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "aks_cluster_name = 'my-aks-cluster' \nresource_id = '/subscriptions/2a779d6f-0806-4359-a6e8-f1fd57bb5dd7/resourceGroups/devintersection-2018-aml-demo/providers/Microsoft.BatchAI/workspaces/devintersection-workspace/clusters/cpucluster1c848275bca'\n\n",
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "from azureml.core.compute import AksCompute, ComputeTarget\n\ntry:\n    # attach to existing cluster\n    #aks_target = AksCompute.attach(workspace=ws, name=aks_cluster_name, resource_id=resource_id)\n    \n    aks_target = AksCompute.attach_configuration(resource_group,aks_cluster_name)\n    \n    print('Found existing compute target.')\nexcept ComputeTargetException:\n    print('Creating a new compute target...')\n    \n    # Use the default configuration (can also provide parameters to customize)\n    prov_config = AksCompute.provisioning_configuration()\n    \n    # Create the cluster\n    aks_target = ComputeTarget.create(workspace = ws, \n                                  name = aks_cluster_name, \n                                  provisioning_configuration = prov_config)\n    \n    aks_target.wait_for_completion(True)",
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": "Found existing compute target.\n",
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "import os\nprint(os.getcwd())",
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": "/home/nbuser/library\n",
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "name": "python36",
+      "display_name": "Python 3.6",
+      "language": "python"
+    },
+    "language_info": {
+      "mimetype": "text/x-python",
+      "nbconvert_exporter": "python",
+      "name": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6",
+      "file_extension": ".py",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

Connected Car Demo/automl-regression/get_data.py

@@ -0,0 +1,13 @@
+
+import pandas as pd
+import numpy as np
+
+def get_data():
+
+    data = pd.read_csv("https://devintersection.blob.core.windows.net/data/simulated-data.csv")
+
+    X = data.iloc[:,4:72]
+    Y = np.empty(data.shape[0], dtype=object)
+    Y[:] = data.iloc[:,0].tolist()
+
+    return { "X" : X, "y" : Y }