5. AML Compute.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Training using Azure Machine Learning Compute\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://cdn.thenewstack.io/media/2018/10/2e4f0988-az-ml-0.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.2 |Anaconda, Inc.| (default, Sep 30 2017, 18:42:57) \\n[GCC 7.2.0]'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML service version : 1.0.69\n"
     ]
    }
   ],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"Azure ML service version :\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azuremlservice\n",
      "azuremlserviceresourcegroup\n",
      "westeurope\n",
      "70b8f39e-8863-49f7-b6ba-34a80799550c\n"
     ]
    }
   ],
   "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": [
    "## Création expérimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "experiment_name = 'analysesamlcompute'\n",
    "experiment = Experiment(workspace = ws, name = experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to AmlCompute\n",
    "\n",
    "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
    "\n",
    "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
    "\n",
    "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
    "\n",
    "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
    "\n",
    "The training script `train.py` is already created for you. Let's have a look."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit an AmlCompute run in a few different ways\n",
    "\n",
    "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
    "\n",
    "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aks-aml-visual\n",
      "VMDS3V2\n",
      "aks-cluster01\n",
      "vmDS15V2\n",
      "StandardDS4V2\n",
      "automlD2V2\n",
      "automlD2v2\n",
      "monclusterDS2V2\n",
      "cpu-cluster\n"
     ]
    }
   ],
   "source": [
    "cts = ws.compute_targets\n",
    "for ct in cts:\n",
    "    print(ct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'gpus': 0,\n",
       "  'maxResourceVolumeMB': 51200,\n",
       "  'memoryGB': 3.5,\n",
       "  'name': 'Standard_D1',\n",
       "  'vCPUs': 1},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 102400,\n",
       "  'memoryGB': 7.0,\n",
       "  'name': 'Standard_D2',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 204800,\n",
       "  'memoryGB': 14.0,\n",
       "  'name': 'Standard_D3',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 409600,\n",
       "  'memoryGB': 28.0,\n",
       "  'name': 'Standard_D4',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 102400,\n",
       "  'memoryGB': 14.0,\n",
       "  'name': 'Standard_D11',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 204800,\n",
       "  'memoryGB': 28.0,\n",
       "  'name': 'Standard_D12',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 409600,\n",
       "  'memoryGB': 56.0,\n",
       "  'name': 'Standard_D13',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 819200,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_D14',\n",
       "  'vCPUs': 16},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 51200,\n",
       "  'memoryGB': 3.5,\n",
       "  'name': 'Standard_D1_v2',\n",
       "  'vCPUs': 1},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 102400,\n",
       "  'memoryGB': 7.0,\n",
       "  'name': 'Standard_D2_v2',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 204800,\n",
       "  'memoryGB': 14.0,\n",
       "  'name': 'Standard_D3_v2',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 409600,\n",
       "  'memoryGB': 28.0,\n",
       "  'name': 'Standard_D4_v2',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 102400,\n",
       "  'memoryGB': 14.0,\n",
       "  'name': 'Standard_D11_v2',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 204800,\n",
       "  'memoryGB': 28.0,\n",
       "  'name': 'Standard_D12_v2',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 409600,\n",
       "  'memoryGB': 56.0,\n",
       "  'name': 'Standard_D13_v2',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 819200,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_D14_v2',\n",
       "  'vCPUs': 16},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 7168,\n",
       "  'memoryGB': 3.5,\n",
       "  'name': 'Standard_DS1_v2',\n",
       "  'vCPUs': 1},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 14336,\n",
       "  'memoryGB': 7.0,\n",
       "  'name': 'Standard_DS2_v2',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 28672,\n",
       "  'memoryGB': 14.0,\n",
       "  'name': 'Standard_DS3_v2',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 57344,\n",
       "  'memoryGB': 28.0,\n",
       "  'name': 'Standard_DS4_v2',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 114688,\n",
       "  'memoryGB': 56.0,\n",
       "  'name': 'Standard_DS5_v2',\n",
       "  'vCPUs': 16},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 28672,\n",
       "  'memoryGB': 14.0,\n",
       "  'name': 'Standard_DS11_v2',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 57344,\n",
       "  'memoryGB': 28.0,\n",
       "  'name': 'Standard_DS12_v2',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 114688,\n",
       "  'memoryGB': 56.0,\n",
       "  'name': 'Standard_DS13_v2',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 229376,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_DS14_v2',\n",
       "  'vCPUs': 16},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 286720,\n",
       "  'memoryGB': 140.0,\n",
       "  'name': 'Standard_DS15_v2',\n",
       "  'vCPUs': 20},\n",
       " {'gpus': 1,\n",
       "  'maxResourceVolumeMB': 389120,\n",
       "  'memoryGB': 56.0,\n",
       "  'name': 'Standard_NV6',\n",
       "  'vCPUs': 6},\n",
       " {'gpus': 2,\n",
       "  'maxResourceVolumeMB': 696320,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_NV12',\n",
       "  'vCPUs': 12},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1474560,\n",
       "  'memoryGB': 224.0,\n",
       "  'name': 'Standard_NV24',\n",
       "  'vCPUs': 24},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 16384,\n",
       "  'memoryGB': 4.0,\n",
       "  'name': 'Standard_F2s_v2',\n",
       "  'vCPUs': 2},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 32768,\n",
       "  'memoryGB': 8.0,\n",
       "  'name': 'Standard_F4s_v2',\n",
       "  'vCPUs': 4},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 65536,\n",
       "  'memoryGB': 16.0,\n",
       "  'name': 'Standard_F8s_v2',\n",
       "  'vCPUs': 8},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 131072,\n",
       "  'memoryGB': 32.0,\n",
       "  'name': 'Standard_F16s_v2',\n",
       "  'vCPUs': 16},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 262144,\n",
       "  'memoryGB': 64.0,\n",
       "  'name': 'Standard_F32s_v2',\n",
       "  'vCPUs': 32},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 524288,\n",
       "  'memoryGB': 128.0,\n",
       "  'name': 'Standard_F64s_v2',\n",
       "  'vCPUs': 64},\n",
       " {'gpus': 0,\n",
       "  'maxResourceVolumeMB': 589824,\n",
       "  'memoryGB': 144.0,\n",
       "  'name': 'Standard_F72s_v2',\n",
       "  'vCPUs': 72},\n",
       " {'gpus': 1,\n",
       "  'maxResourceVolumeMB': 344064,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_NC6s_v3',\n",
       "  'vCPUs': 6},\n",
       " {'gpus': 2,\n",
       "  'maxResourceVolumeMB': 688128,\n",
       "  'memoryGB': 224.0,\n",
       "  'name': 'Standard_NC12s_v3',\n",
       "  'vCPUs': 12},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1376256,\n",
       "  'memoryGB': 448.0,\n",
       "  'name': 'Standard_NC24rs_v3',\n",
       "  'vCPUs': 24},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1376256,\n",
       "  'memoryGB': 448.0,\n",
       "  'name': 'Standard_NC24s_v3',\n",
       "  'vCPUs': 24},\n",
       " {'gpus': 1,\n",
       "  'maxResourceVolumeMB': 389120,\n",
       "  'memoryGB': 56.0,\n",
       "  'name': 'Standard_NC6',\n",
       "  'vCPUs': 6},\n",
       " {'gpus': 2,\n",
       "  'maxResourceVolumeMB': 696320,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_NC12',\n",
       "  'vCPUs': 12},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1474560,\n",
       "  'memoryGB': 224.0,\n",
       "  'name': 'Standard_NC24',\n",
       "  'vCPUs': 24},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1474560,\n",
       "  'memoryGB': 224.0,\n",
       "  'name': 'Standard_NC24r',\n",
       "  'vCPUs': 24},\n",
       " {'gpus': 1,\n",
       "  'maxResourceVolumeMB': 344064,\n",
       "  'memoryGB': 112.0,\n",
       "  'name': 'Standard_NC6s_v2',\n",
       "  'vCPUs': 6},\n",
       " {'gpus': 2,\n",
       "  'maxResourceVolumeMB': 688128,\n",
       "  'memoryGB': 224.0,\n",
       "  'name': 'Standard_NC12s_v2',\n",
       "  'vCPUs': 12},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1376256,\n",
       "  'memoryGB': 448.0,\n",
       "  'name': 'Standard_NC24rs_v2',\n",
       "  'vCPUs': 24},\n",
       " {'gpus': 4,\n",
       "  'maxResourceVolumeMB': 1376256,\n",
       "  'memoryGB': 448.0,\n",
       "  'name': 'Standard_NC24s_v2',\n",
       "  'vCPUs': 24}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "\n",
    "AmlCompute.supported_vmsizes(workspace = ws)\n",
    "#AmlCompute.supported_vmsizes(workspace = ws, location='southcentralus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./train-on-amlcompute/train.py'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "project_folder = './train-on-amlcompute'\n",
    "os.makedirs(project_folder, exist_ok=True)\n",
    "shutil.copy('train.py', project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "myenv = Environment(\"myenv\")\n",
    "\n",
    "myenv.docker.enabled = True\n",
    "myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provision as a persistent compute target\n",
    "\n",
    "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
    "\n",
    "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
    "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing cluster, use it.\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "cpu_cluster_name = \"cpu-cluster\"\n",
    "\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",
    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
    "\n",
    "cpu_cluster.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aks-aml-visual\n",
      "VMDS3V2\n",
      "aks-cluster01\n",
      "vmDS15V2\n",
      "StandardDS4V2\n",
      "automlD2V2\n",
      "automlD2v2\n",
      "monclusterDS2V2\n",
      "cpu-cluster\n"
     ]
    }
   ],
   "source": [
    "cts = ws.compute_targets\n",
    "for ct in cts:\n",
    "    print(ct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure & Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>analysesamlcompute</td><td>analysesamlcompute_1571927654_31de7d13</td><td>azureml.scriptrun</td><td>Starting</td><td><a href=\"https://mlworkspace.azure.ai/portal/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/azuremlserviceresourcegroup/providers/Microsoft.MachineLearningServices/workspaces/azuremlservice/experiments/analysesamlcompute/runs/analysesamlcompute_1571927654_31de7d13\" target=\"_blank\" rel=\"noopener\">Link to Azure Portal</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.script_run.ScriptRun?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Run(Experiment: analysesamlcompute,\n",
       "Id: analysesamlcompute_1571927654_31de7d13,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Starting)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from azureml.core import ScriptRunConfig\n",
    "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
    "\n",
    "src = ScriptRunConfig(source_directory=project_folder, script='train.py')\n",
    "\n",
    "src.run_config.target = cpu_cluster.name\n",
    "\n",
    "src.run_config.environment = myenv\n",
    " \n",
    "run = experiment.submit(config=src)\n",
    "run"
   ]
  },
  {
   "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": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: analysesamlcompute_1571927654_31de7d13\n",
      "Web View: https://mlworkspace.azure.ai/portal/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/azuremlserviceresourcegroup/providers/Microsoft.MachineLearningServices/workspaces/azuremlservice/experiments/analysesamlcompute/runs/analysesamlcompute_1571927654_31de7d13\n",
      "\n",
      "Streaming azureml-logs/55_azureml-execution-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt\n",
      "========================================================================================================================\n",
      "\n",
      "2019-10-24T14:37:50Z Starting output-watcher...\n",
      "Login Succeeded\n",
      "Using default tag: latest\n",
      "latest: Pulling from azureml/azureml_30e66b3edd8c80fa7056c857b57fdf50\n",
      "a1298f4ce990: Pulling fs layer\n",
      "04a3282d9c4b: Pulling fs layer\n",
      "9b0d3db6dc03: Pulling fs layer\n",
      "8269c605f3f1: Pulling fs layer\n",
      "6504d449e70c: Pulling fs layer\n",
      "4e38f320d0d4: Pulling fs layer\n",
      "b0a763e8ee03: Pulling fs layer\n",
      "11917a028ca4: Pulling fs layer\n",
      "a6c378d11cbf: Pulling fs layer\n",
      "6cc007ad9140: Pulling fs layer\n",
      "6c1698a608f3: Pulling fs layer\n",
      "78b5115f88e4: Pulling fs layer\n",
      "f92d957afa9e: Pulling fs layer\n",
      "7fe854dc4c5f: Pulling fs layer\n",
      "e6960fc02808: Pulling fs layer\n",
      "eadd8438e035: Pulling fs layer\n",
      "a611664305bb: Pulling fs layer\n",
      "6cc007ad9140: Waiting\n",
      "6c1698a608f3: Waiting\n",
      "78b5115f88e4: Waiting\n",
      "f92d957afa9e: Waiting\n",
      "7fe854dc4c5f: Waiting\n",
      "8269c605f3f1: Waiting\n",
      "e6960fc02808: Waiting\n",
      "eadd8438e035: Waiting\n",
      "a611664305bb: Waiting\n",
      "6504d449e70c: Waiting\n",
      "4e38f320d0d4: Waiting\n",
      "11917a028ca4: Waiting\n",
      "b0a763e8ee03: Waiting\n",
      "a6c378d11cbf: Waiting\n",
      "9b0d3db6dc03: Verifying Checksum\n",
      "9b0d3db6dc03: Download complete\n",
      "04a3282d9c4b: Download complete\n",
      "a1298f4ce990: Verifying Checksum\n",
      "a1298f4ce990: Download complete\n",
      "8269c605f3f1: Verifying Checksum\n",
      "8269c605f3f1: Download complete\n",
      "4e38f320d0d4: Verifying Checksum\n",
      "4e38f320d0d4: Download complete\n",
      "b0a763e8ee03: Verifying Checksum\n",
      "b0a763e8ee03: Download complete\n",
      "6504d449e70c: Verifying Checksum\n",
      "6504d449e70c: Download complete\n",
      "6cc007ad9140: Verifying Checksum\n",
      "6cc007ad9140: Download complete\n",
      "6c1698a608f3: Download complete\n",
      "11917a028ca4: Verifying Checksum\n",
      "11917a028ca4: Download complete\n",
      "78b5115f88e4: Verifying Checksum\n",
      "78b5115f88e4: Download complete\n",
      "f92d957afa9e: Verifying Checksum\n",
      "f92d957afa9e: Download complete\n",
      "a6c378d11cbf: Verifying Checksum\n",
      "a6c378d11cbf: Download complete\n",
      "e6960fc02808: Verifying Checksum\n",
      "e6960fc02808: Download complete\n",
      "7fe854dc4c5f: Download complete\n",
      "a611664305bb: Verifying Checksum\n",
      "a611664305bb: Download complete\n",
      "eadd8438e035: Verifying Checksum\n",
      "eadd8438e035: Download complete\n",
      "a1298f4ce990: Pull complete\n",
      "04a3282d9c4b: Pull complete\n",
      "9b0d3db6dc03: Pull complete\n",
      "8269c605f3f1: Pull complete\n",
      "6504d449e70c: Pull complete\n",
      "4e38f320d0d4: Pull complete\n",
      "b0a763e8ee03: Pull complete\n",
      "11917a028ca4: Pull complete\n",
      "a6c378d11cbf: Pull complete\n",
      "6cc007ad9140: Pull complete\n",
      "6c1698a608f3: Pull complete\n",
      "78b5115f88e4: Pull complete\n",
      "f92d957afa9e: Pull complete\n",
      "7fe854dc4c5f: Pull complete\n",
      "e6960fc02808: Pull complete\n",
      "eadd8438e035: Pull complete\n",
      "a611664305bb: Pull complete\n",
      "Digest: sha256:db083479c7e4e4c679513dc10bdac6841b2c398137abbc6cc282d62e5298c768\n",
      "Status: Downloaded newer image for azuremlservice8791701193.azurecr.io/azureml/azureml_30e66b3edd8c80fa7056c857b57fdf50:latest\n",
      "d7dce3ad758e4e87c18cbb32a4776d16db95b03463ae5dcd612b754a9146e0af\n",
      "2019/10/24 14:39:05 Version: 3.0.00998.0001 Branch: master Commit: 20ee96da\n",
      "2019/10/24 14:39:06 sshd runtime has already been installed in the container\n",
      "ssh-keygen: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libcrypto.so.1.0.0: no version information available (required by ssh-keygen)\n",
      "ssh-keygen: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libcrypto.so.1.0.0: no version information available (required by ssh-keygen)\n",
      "bash: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libtinfo.so.5: no version information available (required by bash)\n",
      "bash: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libtinfo.so.5: no version information available (required by bash)\n",
      "\n",
      "Streaming azureml-logs/65_job_prep-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt\n",
      "===============================================================================================================\n",
      "\n",
      "bash: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libtinfo.so.5: no version information available (required by bash)\n",
      "Starting job preparation. Current time:2019-10-24T14:39:17.119326\n",
      "Extracting the control code.\n",
      "Creating directory: azureml-logs/\n",
      "Retrieving project from URI: https://azuremlservice8628362969.blob.core.windows.net/azureml-blobstore-b7a492c5-1d27-4c35-99bf-ef22bdee0fbb/azureml/project_zip_1bbc7f7d8b4e461496e3d65b96db22f1?sv=2019-02-02&sr=b&sig=r5pG4eW23AoEQD0evvdvoaD0HXbrHdos8Uhw5Hi%2FTUA%3D&st=2019-10-24T14%3A24%3A15Z&se=2019-10-31T14%3A34%3A15Z&sp=r\n",
      "Download from datastores if requested.\n",
      "Download or mount from datasets if requested.\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 90\n",
      "\n",
      "Streaming azureml-logs/70_driver_log.txt\n",
      "========================================\n",
      "\n",
      "bash: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libtinfo.so.5: no version information available (required by bash)\n",
      "bash: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libtinfo.so.5: no version information available (required by bash)\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 141\n",
      "Entering Run History Context Manager.\n",
      "/azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "alpha is 0.00, and mse is 3424.32\n",
      "alpha is 0.10, and mse is 3372.65\n",
      "alpha is 0.20, and mse is 3325.29\n",
      "alpha is 0.30, and mse is 3302.67\n",
      "alpha is 0.40, and mse is 3295.74\n",
      "alpha is 0.50, and mse is 3298.91\n",
      "alpha is 0.60, and mse is 3308.70\n",
      "alpha is 0.70, and mse is 3322.90\n",
      "alpha is 0.80, and mse is 3340.02\n",
      "alpha is 0.90, and mse is 3359.09\n",
      "\n",
      "\n",
      "The experiment completed successfully. Finalizing run...\n",
      "Cleaning up all outstanding Run operations, waiting 300.0 seconds\n",
      "2 items cleaning up...\n",
      "Cleanup took 0.26182007789611816 seconds\n",
      "\n",
      "Streaming azureml-logs/75_job_post-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt\n",
      "===============================================================================================================\n",
      "\n",
      "bash: /azureml-envs/azureml_5d25805f2023a3fb30b9ada3e9fdf730/lib/libtinfo.so.5: no version information available (required by bash)\n",
      "Starting job release. Current time:2019-10-24T14:39:39.108283\n",
      "Logging experiment finalizing status in history service.\n",
      "Starting the daemon thread to refresh tokens in background for process with pid = 172\n",
      "Job release is complete. Current time:2019-10-24T14:39:43.412352\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: analysesamlcompute_1571927654_31de7d13\n",
      "Web View: https://mlworkspace.azure.ai/portal/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourceGroups/azuremlserviceresourcegroup/providers/Microsoft.MachineLearningServices/workspaces/azuremlservice/experiments/analysesamlcompute/runs/analysesamlcompute_1571927654_31de7d13\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'endTimeUtc': '2019-10-24T14:40:01.497601Z',\n",
       " 'inputDatasets': [],\n",
       " 'logFiles': {'azureml-logs/55_azureml-execution-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/azureml-logs/55_azureml-execution-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt?sv=2018-11-09&sr=b&sig=08j4lZv5G8kyWZJ8Ph4LLYV6ExNf7I0Kq4bSSl0VX8M%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'azureml-logs/65_job_prep-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/azureml-logs/65_job_prep-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt?sv=2018-11-09&sr=b&sig=teGua8WHMaEOGAXlb6UMN5%2BRj4CuFMlG5aQ2tiKekpg%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'azureml-logs/70_driver_log.txt': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/azureml-logs/70_driver_log.txt?sv=2018-11-09&sr=b&sig=v16iuDpW%2FOELJQjtn%2BYEgyg36wsM84yPPsQjKiCoyto%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'azureml-logs/75_job_post-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/azureml-logs/75_job_post-tvmps_4f7016796afdd59e6bb505add6e4ac1ccde9f4b49658b8a7188066b7eca3eb14_d.txt?sv=2018-11-09&sr=b&sig=mtUdRKQwEJBnn3%2FOHK6WZbpUPQd%2Bydkv3OewaBS8ca8%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'azureml-logs/process_info.json': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/azureml-logs/process_info.json?sv=2018-11-09&sr=b&sig=bV4ocU1kj7ismOKDMYm0x3Q4sy%2F1sk3M%2BxiUjjSgYBk%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'azureml-logs/process_status.json': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/azureml-logs/process_status.json?sv=2018-11-09&sr=b&sig=kLy8JQLGqAvzNerBDURRUKK7Jsi3xxeqwOJKNF7CM2k%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'logs/azureml/141_azureml.log': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/logs/azureml/141_azureml.log?sv=2018-11-09&sr=b&sig=BjqBYzu9IggfJBLaiqQYTHroZ7FgGkMk0Um6K2rf0fo%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r',\n",
       "  'logs/azureml/azureml.log': 'https://azuremlservice8628362969.blob.core.windows.net/azureml/ExperimentRun/dcid.analysesamlcompute_1571927654_31de7d13/logs/azureml/azureml.log?sv=2018-11-09&sr=b&sig=8u1LS3FLBdsCL8%2BMbkEutWZsaSXMM%2BAvvkBNXozGOMs%3D&st=2019-10-24T14%3A30%3A02Z&se=2019-10-24T22%3A40%3A02Z&sp=r'},\n",
       " 'properties': {'AzureML.DerivedImageName': 'azureml/azureml_30e66b3edd8c80fa7056c857b57fdf50',\n",
       "  'ContentSnapshotId': 'c8adb1c1-b4cc-4b2d-8458-6edba50e3cd7',\n",
       "  'ProcessInfoFile': 'azureml-logs/process_info.json',\n",
       "  'ProcessStatusFile': 'azureml-logs/process_status.json',\n",
       "  '_azureml.ComputeTargetType': 'batchai'},\n",
       " 'runDefinition': {'amlCompute': {'clusterMaxNodeCount': None,\n",
       "   'name': None,\n",
       "   'retainCluster': False,\n",
       "   'vmSize': None},\n",
       "  'arguments': [],\n",
       "  'communicator': 'None',\n",
       "  'containerInstance': {'cpuCores': 2, 'memoryGb': 3.5, 'region': None},\n",
       "  'data': {},\n",
       "  'dataReferences': {},\n",
       "  'docker': {'arguments': [],\n",
       "   'sharedVolumes': True,\n",
       "   'shmSize': '2g',\n",
       "   'useDocker': True},\n",
       "  'environment': {'docker': {'arguments': [],\n",
       "    'baseDockerfile': None,\n",
       "    'baseImage': 'mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04',\n",
       "    'baseImageRegistry': {'address': None, 'password': None, 'username': None},\n",
       "    'enabled': True},\n",
       "   'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'},\n",
       "   'inferencingStackVersion': None,\n",
       "   'name': 'myenv',\n",
       "   'python': {'baseCondaEnvironment': None,\n",
       "    'condaDependencies': {'channels': ['conda-forge'],\n",
       "     'dependencies': ['python=3.6.2',\n",
       "      {'pip': ['azureml-defaults==1.0.69.*']},\n",
       "      'scikit-learn'],\n",
       "     'name': 'azureml_5d25805f2023a3fb30b9ada3e9fdf730'},\n",
       "    'interpreterPath': 'python',\n",
       "    'userManagedDependencies': False},\n",
       "   'spark': {'packages': [], 'precachePackages': True, 'repositories': []},\n",
       "   'version': 'Autosave_2019-10-24T13:46:57Z_d7a01920'},\n",
       "  'exposedPorts': None,\n",
       "  'framework': 'Python',\n",
       "  'hdi': {'yarnDeployMode': 'Cluster'},\n",
       "  'history': {'directoriesToWatch': ['logs'],\n",
       "   'outputCollection': True,\n",
       "   'snapshotProject': True},\n",
       "  'jobName': None,\n",
       "  'maxRunDurationSeconds': None,\n",
       "  'mpi': {'processCountPerNode': 1},\n",
       "  'nodeCount': 1,\n",
       "  'script': 'train.py',\n",
       "  'sourceDirectoryDataStore': None,\n",
       "  'spark': {'configuration': {'spark.app.name': 'Azure ML Experiment',\n",
       "    'spark.yarn.maxAppAttempts': '1'}},\n",
       "  'target': 'cpu-cluster',\n",
       "  'tensorflow': {'parameterServerCount': 1, 'workerCount': 1}},\n",
       " 'runId': 'analysesamlcompute_1571927654_31de7d13',\n",
       " 'startTimeUtc': '2019-10-24T14:37:43.902319Z',\n",
       " 'status': 'Completed',\n",
       " 'target': 'cpu-cluster'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Résultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': [0.0,\n",
       "  0.1,\n",
       "  0.2,\n",
       "  0.30000000000000004,\n",
       "  0.4,\n",
       "  0.5,\n",
       "  0.6000000000000001,\n",
       "  0.7000000000000001,\n",
       "  0.8,\n",
       "  0.9],\n",
       " 'mse': [3424.3166882137343,\n",
       "  3372.649627810032,\n",
       "  3325.2946794678764,\n",
       "  3302.6736334017255,\n",
       "  3295.741064355809,\n",
       "  3298.9096058070622,\n",
       "  3308.7042707723226,\n",
       "  3322.8983149039614,\n",
       "  3340.0246620321604,\n",
       "  3359.0935697484424]}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.get_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "nigup"
   }
  ],
  "category": "training",
  "compute": [
   "AML Compute"
  ],
  "datasets": [
   "Diabetes"
  ],
  "deployment": [
   "None"
  ],
  "exclude_from_index": false,
  "framework": [
   "None"
  ],
  "friendly_name": "Train on Azure Machine Learning Compute",
  "index_order": 1,
  "kernelspec": {
   "display_name": "Python 3.6 - AzureML",
   "language": "python",
   "name": "python3-azureml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "tags": [
   "None"
  ],
  "task": "Submit an Azure Machine Leaarning Compute run"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}