{
  "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"
      }
    ],
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