MachineLearningNotebooks/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb

{
  "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": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-in-spark/train-in-spark.png)"
      ]
    },
    {
      "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",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace."
      ]
    },
    {
      "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": [
        "**Note** You can use Docker-based execution to run the Spark job in local computer or a remote VM. Please see the `train-in-remote-vm` notebook for example on how to configure and run in Docker mode in a VM. Make sure you choose a Docker image that has Spark installed, such as `microsoft/mmlspark:0.12`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Attach an HDI cluster\n",
        "Here we will use a actual 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 ComputeTarget, HDInsightCompute\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "import os\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",
        "    attach_config = HDInsightCompute.attach_configuration(address=os.environ.get('hdiservername', '<my_hdi_cluster_name>-ssh.azurehdinsight.net'), \n",
        "                                                          ssh_port=22, \n",
        "                                                          username=os.environ.get('hdiusername', '<ssh_username>'), \n",
        "                                                          password=os.environ.get('hdipassword', '<my_password>'))\n",
        "    hdi_compute = ComputeTarget.attach(workspace=ws, \n",
        "                                       name='myhdi', \n",
        "                                       attach_configuration=attach_config)\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": "markdown",
      "metadata": {},
      "source": [
        "Configure an execution using the HDInsight cluster with a conda environment that has `numpy`."
      ]
    },
    {
      "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",
        "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": "markdown",
      "metadata": {},
      "source": [
        "Monitor the run using a Juypter widget"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After the run is succesfully finished, you can check the metrics logged."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get all metris logged in the run\n",
        "metrics = run.get_metrics()\n",
        "print(metrics)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "aashishb"
      }
    ],
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    "friendly_name": "Training in Spark",
    "exclude_from_index": false,
    "index_order": 1,
    "category": "training",
    "task": "Submiting a run on a spark cluster",
    "datasets": [
      "None"
    ],
    "compute": [
      "HDI cluster"
    ],
    "deployment": [
      "None"
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    "framework": [
      "PySpark"
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