Update notebooks

training/03.train-hyperparameter-tune-deploy-with-tensorflow/03.train-hyperparameter-tune-deploy-with-tensorflow.ipynb

@@ -17,7 +17,7 @@
         }
       },
       "source": [
-        "# 03. Training MNIST dataset with hyperparameter tuning & deploy to ACI\n",
+        "# 03. Training, hyperparameter tune, and deploy with TensorFlow\n",
         "\n",
         "## Introduction\n",
         "This tutorial shows how to train a simple deep neural network using the MNIST dataset and TensorFlow on Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of `28x28` pixels, representing number from 0 to 9. The goal is to create a multi-class classifier to identify the digit each image represents, and deploy it as a web service in Azure.\n",
@@ -72,6 +72,28 @@
         "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "Diagnostics"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "set_diagnostics_collection(send_diagnostics=True)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -246,17 +268,17 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
-        "batchai_cluster_name = \"gpucluster\"\n",
+        "cluster_name = \"gpucluster\"\n",
         "\n",
         "try:\n",
         "    # look for the existing cluster by name\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=batchai_cluster_name)\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
         "    if type(compute_target) is BatchAiCompute:\n",
-        "        print('found compute target {}, just use it.'.format(batchai_cluster_name))\n",
+        "        print('Found existing compute target {}.'.format(cluster_name))\n",
         "    else:\n",
-        "        print('{} exists but it is not a Batch AI cluster. Please choose a different name.'.format(batchai_cluster_name))\n",
+        "        print('{} exists but it is not a Batch AI cluster. Please choose a different name.'.format(cluster_name))\n",
         "except ComputeTargetException:\n",
-        "    print('creating a new compute target...')\n",
+        "    print('Creating a new compute target...')\n",
         "    compute_config = BatchAiCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\", # GPU-based VM\n",
         "                                                                #vm_priority='lowpriority', # optional\n",
         "                                                                autoscale_enabled=True,\n",
@@ -264,7 +286,7 @@
         "                                                                cluster_max_nodes=4)\n",
         "\n",
         "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, batchai_cluster_name, compute_config)\n",
+        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
         "    \n",
         "    # can poll for a minimum number of nodes and for a specific timeout. \n",
         "    # if no min node count is provided it uses the scale settings for the cluster\n",
@@ -278,7 +300,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets()` function returns. You should now see one entry named 'cpucluster' of type BatchAI."
+        "Now that you have created the compute target, let's see what the workspace's `compute_targets()` function returns. You should now see one entry named 'gpucluster' of type BatchAI."
       ]
     },
     {
@@ -287,8 +309,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for ct in ws.compute_targets():\n",
-        "    print(ct.name, ct.type, ct.provisioning_state)"
+        "compute_targets = ws.compute_targets()\n",
+        "for name, ct in compute_targets.items():\n",
+        "    print(name, ct.type, ct.provisioning_state)"
       ]
     },
     {
@@ -338,7 +361,7 @@
         "    parser = argparse.ArgumentParser()\n",
         "    parser.add_argument('--data_folder')\n",
         "```\n",
-        "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_submitted_run()`. Further down the script is using the `run` to report the training accuracy and the validation accuracy as training progresses.\n",
+        "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_context()`. Further down the script is using the `run` to report the training accuracy and the validation accuracy as training progresses.\n",
         "```\n",
         "    run.log('training_acc', np.float(acc_train))\n",
         "    run.log('validation_acc', np.float(acc_val))\n",
@@ -1056,14 +1079,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for model in ws.models():\n",
-        "    print(\"Model:\", model.name, model.id)\n",
-        "\n",
-        "for image in ws.images():\n",
-        "    print(\"Image:\", image.name, image.image_location)\n",
-        "\n",
-        "for webservice in ws.webservices():\n",
-        "    print(\"Webservice:\", webservice.name, webservice.scoring_uri)"
+        "models = ws.models()\n",
+        "for name, model in models.items():\n",
+        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "    \n",
+        "images = ws.images()\n",
+        "for name, image in images.items():\n",
+        "    print(\"Image: {}, location: {}\".format(name, image.image_location))\n",
+        "    \n",
+        "webservices = ws.webservices()\n",
+        "for name, webservice in webservices.items():\n",
+        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
       ]
     },
     {
@@ -1102,6 +1128,11 @@
     }
   ],
   "metadata": {
+    "authors": [
+      {
+        "name": "minxia"
+      }
+    ],
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",

training/03.train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py

@@ -64,7 +64,7 @@ init = tf.global_variables_initializer()
 saver = tf.train.Saver()
 
 # start an Azure ML run
-run = Run.get_submitted_run()
+run = Run.get_context()
 
 with tf.Session() as sess:
     init.run()