update samples - test

configuration.ipynb

@@ -103,7 +103,7 @@
    "source": [
     "import azureml.core\n",
     "\n",
-        "print(\"This notebook was created using version 1.0.43 of the Azure ML SDK\")\n",
+    "print(\"This notebook was created using version AZUREML-SDK-VERSION of the Azure ML SDK\")\n",
     "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
    ]
   },

how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.ipynb

@@ -265,7 +265,7 @@
     "from azureml.core.compute_target import ComputeTargetException\n",
     "\n",
     "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpucluster\"\n",
+    "cpu_cluster_name = \"cpu-cluster\"\n",
     "\n",
     "# Verify that cluster does not exist already\n",
     "try:\n",
@@ -370,7 +370,7 @@
     "from azureml.core.compute_target import ComputeTargetException\n",
     "\n",
     "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpucluster\"\n",
+    "cpu_cluster_name = \"cpu-cluster\"\n",
     "\n",
     "# Verify that cluster does not exist already\n",
     "try:\n",
@@ -506,7 +506,7 @@
    "outputs": [],
    "source": [
     "# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
-        "# 'cpucluster' in this case but use a different VM family for instance.\n",
+    "# 'cpu-cluster' in this case but use a different VM family for instance.\n",
     "\n",
     "# cpu_cluster.delete()"
    ]

how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb

@@ -36,22 +36,6 @@
     "4. Visualize the global and local explanations with the visualization dashboard."
    ]
   },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This example needs sklearn-pandas. If it is not installed, uncomment and run the following line."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#!pip install sklearn-pandas"
-      ]
-    },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -63,7 +47,6 @@
     "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from sklearn_pandas import DataFrameMapper\n",
     "import pandas as pd\n",
     "import numpy as np"
    ]
@@ -113,6 +96,13 @@
     "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sklearn imports"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -121,7 +111,51 @@
    "source": [
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "transformations = ColumnTransformer([\n",
+    "    (\"age_fare\", Pipeline(steps=[\n",
+    "        ('imputer', SimpleImputer(strategy='median')),\n",
+    "        ('scaler', StandardScaler())\n",
+    "    ]), [\"age\", \"fare\"]),\n",
+    "    (\"embarked\", Pipeline(steps=[\n",
+    "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
+    "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
+    "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
+    "])\n",
+    "\n",
+    "\n",
+    "# Append classifier to preprocessing pipeline.\n",
+    "# Now we have a full prediction pipeline.\n",
+    "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+    "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "# Uncomment below if sklearn-pandas is not installed\n",
+    "#!pip install sklearn-pandas\n",
     "from sklearn_pandas import DataFrameMapper\n",
     "\n",
     "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
@@ -141,7 +175,8 @@
     "# Append classifier to preprocessing pipeline.\n",
     "# Now we have a full prediction pipeline.\n",
     "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])"
+    "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
+    "'''"
    ]
   },
   {

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb

@@ -81,7 +81,7 @@
    "metadata": {},
    "source": [
     "### Set up datastores\n",
-        "First, let\u00e2\u20ac\u2122s access the datastore that has the model, labels, and images. \n",
+    "First, let’s access the datastore that has the model, labels, and images. \n",
     "\n",
     "### Create a datastore that points to a blob container containing sample images\n",
     "\n",
@@ -111,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-        "Next, let\u00e2\u20ac\u2122s specify the default datastore for the outputs."
+    "Next, let’s specify the default datastore for the outputs."
    ]
   },
   {
@@ -298,7 +298,7 @@
    "metadata": {},
    "source": [
     "## Build and run the batch scoring pipeline\n",
-        "You have everything you need to build the pipeline. Let\u00e2\u20ac\u2122s put all these together."
+    "You have everything you need to build the pipeline. Let’s put all these together."
    ]
   },
   {

how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb

@@ -299,7 +299,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-        "You can also specify a custom Docker image for exeution. In this case, you probably want to tell the system not to build a new conda environment for you. Instead, you can specify the path to an existing Python environment in the custom Docker image.\n",
+    "You can also specify a custom Docker image for execution. In this case, you probably want to tell the system not to build a new conda environment for you. Instead, you can specify the path to an existing Python environment in the custom Docker image. If custom Docker image information is not specified, Azure ML uses the default Docker image to run your training. For more information about Docker containers used in Azure ML training, please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers).\n",
     "\n",
     "**Note**: since the below example points to the preinstalled Python environment in the miniconda3 image maintained by continuum.io on Docker Hub where Azure ML SDK is not present, the logging metric code is not triggered. But a run history record is still recorded. "
    ]

how-to-use-azureml/training/README.md

@@ -5,9 +5,10 @@ Follow these sample notebooks to learn:
 1. [Train within notebook](train-within-notebook): train a simple scikit-learn model using the Jupyter kernel and deploy the model to Azure Container Service.
 2. [Train on local](train-on-local): train a model using local computer as compute target.
 3. [Train on remote VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
-4. [Train on AmlCompute](train-on-amlcompute): train a model using an AmlCompute cluster as compute target.
+4. [Train on ML Compute](train-on-amlcompute): train a model using an ML Compute cluster as compute target.
 5. [Train in an HDI Spark cluster](train-in-spark): train a Spark ML model using an HDInsight Spark cluster as compute target.
 6. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
-7. [Train and hyperparameter tune on Iris Dataset with Scikit-learn](train-hyperparameter-tune-deploy-with-sklearn): train a model using the Scikit-learn estimator and tune hyperparameters with Hyperdrive.
+7. [Manage runs](manage-runs): learn different ways how to start runs and child runs, monitor them, and cancel them.
+8. [Train and hyperparameter tune on Iris Dataset with Scikit-learn](train-hyperparameter-tune-deploy-with-sklearn): train a model using the Scikit-learn estimator and tune hyperparameters with Hyperdrive.
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/README.png)

how-to-use-azureml/training/logging-api/logging-api.ipynb

@@ -100,7 +100,7 @@
     "\n",
     "# Check core SDK version number\n",
     "\n",
-        "print(\"This notebook was created using SDK version 1.0.43, you are currently running version\", azureml.core.VERSION)"
+    "print(\"This notebook was created using SDK version AZUREML-SDK-VERSION, you are currently running version\", azureml.core.VERSION)"
    ]
   },
   {

how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb

@@ -362,7 +362,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-        "The below run will likely fail because `train.py` needs dependency `azureml`, `scikit-learn` and others, which are not found in that Python environment. "
+    "The below run will likely fail because `train.py` needs dependency `azureml`, `scikit-learn` and others, which are not found in that Python environment."
    ]
   },
   {
@@ -372,7 +372,13 @@
    "outputs": [],
    "source": [
     "run = exp.submit(config=src)\n",
-        "run.wait_for_completion(show_output=True)"
+    "\n",
+    "from azureml.exceptions import ActivityFailedException\n",
+    "\n",
+    "try:\n",
+    "    run.wait_for_completion(show_output=True)\n",
+    "except ActivityFailedException as ex:\n",
+    "    print(ex)"
    ]
   },
   {