diff --git a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
index c8913211..714168eb 100644
--- a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
+++ b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
@@ -1,331 +1,331 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 05. Train in Spark\n",
-        "* Create Workspace\n",
-        "* Create Experiment\n",
-        "* Copy relevant files to the script folder\n",
-        "* Configure and Run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Experiment\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'train-on-spark'\n",
-        "\n",
-        "from azureml.core import Experiment\n",
-        "exp = Experiment(workspace=ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## View `train-spark.py`\n",
-        "\n",
-        "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open('train-spark.py', 'r') as training_script:\n",
-        "    print(training_script.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure & Run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure an ACI run\n",
-        "Before you try running on an actual Spark cluster, you can use a Docker image with Spark already baked in, and run it in ACI(Azure Container Registry)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# use pyspark framework\n",
-        "aci_run_config = RunConfiguration(framework=\"pyspark\")\n",
-        "\n",
-        "# use ACI to run the Spark job\n",
-        "aci_run_config.target = 'containerinstance'\n",
-        "aci_run_config.container_instance.region = 'eastus2'\n",
-        "aci_run_config.container_instance.cpu_cores = 1\n",
-        "aci_run_config.container_instance.memory_gb = 2\n",
-        "\n",
-        "# specify base Docker image to use\n",
-        "aci_run_config.environment.docker.enabled = True\n",
-        "aci_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
-        "\n",
-        "# specify CondaDependencies\n",
-        "cd = CondaDependencies()\n",
-        "cd.add_conda_package('numpy')\n",
-        "aci_run_config.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit script to ACI to run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
-        "                                    script= 'train-spark.py',\n",
-        "                                    run_config = aci_run_config)\n",
-        "run = exp.submit(script_run_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note** you can also create a new VM, or attach an existing VM, and use Docker-based execution to run the Spark job. Please see the `04.train-in-vm` for example on how to configure and run in Docker mode in a VM."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Attach an HDI cluster\n",
-        "Now we can use a real Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n",
-        " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
-        " 2. Enter the IP address, username and password below"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import HDInsightCompute\n",
-        "from azureml.exceptions import ComputeTargetException\n",
-        "\n",
-        "try:\n",
-        "    # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n",
-        "    hdi_compute = HDInsightCompute.attach(workspace=ws, \n",
-        "                                          name=\"myhdi\", \n",
-        "                                          address=\"<myhdi-ssh>.azurehdinsight.net\", \n",
-        "                                          ssh_port=22, \n",
-        "                                          username='<ssh-username>', \n",
-        "                                          password='<ssh-pwd>')\n",
-        "\n",
-        "except ComputeTargetException as e:\n",
-        "    print(\"Caught = {}\".format(e.message))\n",
-        "    \n",
-        "        \n",
-        "hdi_compute.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure HDI run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "\n",
-        "# use pyspark framework\n",
-        "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n",
-        "\n",
-        "# Set compute target to the HDI cluster\n",
-        "hdi_run_config.target = hdi_compute.name\n",
-        "\n",
-        "# specify CondaDependencies object to ask system installing numpy\n",
-        "cd = CondaDependencies()\n",
-        "cd.add_conda_package('numpy')\n",
-        "hdi_run_config.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit the script to HDI"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
-        "                                    script= 'train-spark.py',\n",
-        "                                    run_config = hdi_run_config)\n",
-        "run = exp.submit(config=script_run_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the URL of the run history web page\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get all metris logged in the run\n",
-        "metrics = run.get_metrics()\n",
-        "print(metrics)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "aashishb"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 05. Train in Spark\n",
+    "* Create Workspace\n",
+    "* Create Experiment\n",
+    "* Copy relevant files to the script folder\n",
+    "* Configure and Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'train-on-spark'\n",
+    "\n",
+    "from azureml.core import Experiment\n",
+    "exp = Experiment(workspace=ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View `train-spark.py`\n",
+    "\n",
+    "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('train-spark.py', 'r') as training_script:\n",
+    "    print(training_script.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure & Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure an ACI run\n",
+    "Before you try running on an actual Spark cluster, you can use a Docker image with Spark already baked in, and run it in ACI(Azure Container Registry)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# use pyspark framework\n",
+    "aci_run_config = RunConfiguration(framework=\"pyspark\")\n",
+    "\n",
+    "# use ACI to run the Spark job\n",
+    "aci_run_config.target = 'containerinstance'\n",
+    "aci_run_config.container_instance.region = 'eastus2'\n",
+    "aci_run_config.container_instance.cpu_cores = 1\n",
+    "aci_run_config.container_instance.memory_gb = 2\n",
+    "\n",
+    "# specify base Docker image to use\n",
+    "aci_run_config.environment.docker.enabled = True\n",
+    "aci_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
+    "\n",
+    "# specify CondaDependencies\n",
+    "cd = CondaDependencies()\n",
+    "cd.add_conda_package('numpy')\n",
+    "aci_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit script to ACI to run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig\n",
+    "\n",
+    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
+    "                                    script= 'train-spark.py',\n",
+    "                                    run_config = aci_run_config)\n",
+    "run = exp.submit(script_run_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note** you can also create a new VM, or attach an existing VM, and use Docker-based execution to run the Spark job. Please see the `04.train-in-vm` for example on how to configure and run in Docker mode in a VM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Attach an HDI cluster\n",
+    "Now we can use a real Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n",
+    " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
+    " 2. Enter the IP address, username and password below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import HDInsightCompute\n",
+    "from azureml.exceptions import ComputeTargetException\n",
+    "\n",
+    "try:\n",
+    "    # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n",
+    "    hdi_compute = HDInsightCompute.attach(workspace=ws, \n",
+    "                                          name=\"myhdi\", \n",
+    "                                          address=\"<myhdi-ssh>.azurehdinsight.net\", \n",
+    "                                          ssh_port=22, \n",
+    "                                          username='<ssh-username>', \n",
+    "                                          password='<ssh-pwd>')\n",
+    "\n",
+    "except ComputeTargetException as e:\n",
+    "    print(\"Caught = {}\".format(e.message))\n",
+    "    \n",
+    "        \n",
+    "hdi_compute.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure HDI run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "\n",
+    "# use pyspark framework\n",
+    "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n",
+    "\n",
+    "# Set compute target to the HDI cluster\n",
+    "hdi_run_config.target = hdi_compute.name\n",
+    "\n",
+    "# specify CondaDependencies object to ask system installing numpy\n",
+    "cd = CondaDependencies()\n",
+    "cd.add_conda_package('numpy')\n",
+    "hdi_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit the script to HDI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig\n",
+    "\n",
+    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
+    "                                    script= 'train-spark.py',\n",
+    "                                    run_config = hdi_run_config)\n",
+    "run = exp.submit(config=script_run_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the URL of the run history web page\n",
+    "run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get all metris logged in the run\n",
+    "metrics = run.get_metrics()\n",
+    "print(metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "aashishb"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}