MachineLearningNotebooks/tutorials/get-started-day1/day1-part1-setup.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/tutorials/day1-part1-setup.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Tutorial: Get started (day 1) with Azure Machine Learning (Part 1 of 4)\n",
        "\n",
        "---\n",
        "## Introduction <a id='intro'></a>\n",
        "\n",
        "In this **four-part tutorial series**, you will learn the fundamentals of Azure Machine Learning and complete jobs-based Python machine learning tasks in the Azure cloud, including:\n",
        "\n",
        "1. Set up a compute cluster\n",
        "2. Run code in the cloud using Azure Machine Learning's Python SDK.\n",
        "3. Manage the Python environment you use for model training.\n",
        "4. Upload data to Azure and consume that data in training.\n",
        "\n",
        "In this first part of the tutorial series you learn how to create an Azure Machine Learning Compute Cluster that will be used in subsequent parts of the series to submit jobs to. This notebook follows the steps provided on the [Python (day 1) - set up local computer documentation page](https://aka.ms/day1aml).\n",
        "\n",
        "## Pre-requisites <a id='pre-reqs'></a>\n",
        "\n",
        "- An Azure Subscription. If you don't have an Azure subscription, create a free account before you begin. Try [Azure Machine Learning](https://aka.ms/AMLFree) today.\n",
        "- Familiarity with Python and Machine Learning concepts. For example, environments, training, scoring, and so on.\n",
        "- If you are using a compute instance in Azure Machine Learning to run this notebook series, you are all set. Otherwise, please follow the [Configure a development environment for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Ensure you have the latest Azure Machine Learning Python SDK\n",
        "\n",
        "This tutorial series depends on having the Azure Machine Learning SDK version 1.14.0 onwards installed. You can check your version using the code cell below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import VERSION\n",
        "\n",
        "print ('Version: ' + VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If your version is below 1.14.0, then upgrade the SDK using `pip` (**Note: You may need to restart your kernel for the changes to take effect. Re-run the cell above to ensure you have the right version**).\n",
        "\n",
        "```bash\n",
        "!pip install -U azureml-sdk\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an Azure Machine Learning compute cluster <a id='createcc'></a>\n",
        "\n",
        "As this tutorial focuses on jobs-based machine learning tasks, you will be submitting python code to run on an Azure Machine Learning **Compute cluster**, which is well suited for large jobs and production. Therefore, you create an Azure Machine Learning compute cluster that will auto-scale between zero and four nodes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create mlc",
          "batchai"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "ws = Workspace.from_config() # this automatically looks for a directory .azureml\n",
        "\n",
        "# Choose a name for your CPU cluster\n",
        "cpu_cluster_name = \"cpu-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
        "                                                            max_nodes=4, \n",
        "                                                            idle_seconds_before_scaledown=2400)\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "\n",
        "cpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "> <span style=\"color:darkblue;font-weight:bold\"> ! INFORMATION   \n",
        "> When the cluster has been created it will have 0 nodes provisioned. Therefore, the cluster does not incur costs until you submit a job. This cluster will scale down when it has been idle for 2400 seconds (40 minutes).</span>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Next Steps\n",
        "\n",
        "In the next tutorial, you walk through submitting a script to the Azure Machine Learning compute cluster.\n",
        "\n",
        "[Tutorial: Run \"Hello World\" Python Script on Azure](day1-part2-hello-world.ipynb)\n"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "samkemp"
      }
    ],
    "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": "3.6.5"
    },
    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
  },
  "nbformat": 4,
  "nbformat_minor": 4
}