#5 Training and Computer Vision Models on the Cloud using Azure ML Service.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #5 Train an image classification model with Azure Machine Learning\n",
    "\n",
    "![](https://github.com/ashleymcnamara/Developer-Advocate-Bit/blob/master/BIT_ML.png?raw=true)\n",
    "\n",
    "Photo credit to the talented Ashley McNamara\n",
    "\n",
    "#### Sometimes you need more computational power then locally available to train a model. Let's Train  a  Computer Vision model to the Cloud using the Azure Machine Learning Service to make this simple we will revisit the MNIST dataset from the third Notebook\n",
    "\n",
    "Based on the [Azure ML Documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml?WT.mc_id=cvworkshop-github-abornst)\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "\n",
    "> * Set up your development environment\n",
    "> * Access and examine the data\n",
    "> * Train a simple logistic regression model locally using the popular scikit-learn machine learning library \n",
    "> * Train multiple models on a remote cluster\n",
    "> * Review training results, find and register the best model\n",
    "\n",
    "You'll learn how to select a model and deploy it in part 6 of the workshop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages\n",
    "\n",
    "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "check version"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML SDK Version:  1.0.2\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import azureml\n",
    "from azureml.core import Workspace, Run\n",
    "\n",
    "# check core SDK version number\n",
    "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Login and Authenticate with Azure\n",
    "#### Generate credientials file becareful with this anyone who has acess to this file has acess to your account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python3 -m azure.cli login\n",
    "!python3 -m azure.cli ad sp create-for-rbac --sdk-auth > mycredentials.json\n",
    "!export AZURE_AUTH_LOCATION='mycredentials.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, json\n",
    "with open('mycredentials.json') as data_file:    \n",
    "    azure_credentials = json.load(data_file)\n",
    "\n",
    "# delete credentials file \n",
    "os.remove(\"mycredentials.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is an Azure ML Workspace and why do I need one?\n",
    "\n",
    "An AML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an AML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CVWorkshop_RG = 'cvdemo_rg'\n",
    "CVWorkshop_LOC = 'eastus2'\n",
    "CVWorkshop_WS = 'cv_ws'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up your Azure Machine Learning workspace\n",
    "\n",
    "### Option 1: You have workspace already\n",
    "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
    "\n",
    "If you have a workspace created another way, [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#create-workspace-configuration-file) describe how to get your subscription and workspace information.\n",
    "\n",
    "If this cell succeeds, you're done configuring this library!  Otherwise continue to follow the instructions in the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", azure_credentials['subscriptionId'])\n",
    "resource_group = os.environ.get(\"RESOURCE_GROUP\", CVWorkshop_RG)\n",
    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", CVWorkshop_WS)\n",
    "\n",
    "from azureml.core import Workspace\n",
    "\n",
    "try:\n",
    "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
    "    ws.write_config()\n",
    "    print('Workspace configuration succeeded. You are all set!')\n",
    "except:\n",
    "    print('Workspace not found. Run the cells below.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2: You don't have workspace yet\n",
    "\n",
    "\n",
    "#### Requirements\n",
    "\n",
    "Inside your Azure subscription, you will need access to a _resource group_, which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
    "\n",
    "To create or access an Azure ML Workspace, you will need to import the AML library and the following information:\n",
    "* A name for your workspace\n",
    "* Your subscription id\n",
    "* The resource group name\n",
    "\n",
    "#### Supported Azure Regions\n",
    "Specify a region where your workspace will be located from the list of [Azure Machine Learning regions](https://azure.microsoft.com/en-us/global-infrastructure/services/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Workspace class and check the azureml SDK version\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.create(name = CVWorkshop_WS,\n",
    "                      subscription_id = azure_credentials['subscriptionId'],\n",
    "                      resource_group = CVWorkshop_RG, \n",
    "                      location = CVWorkshop_LOC,\n",
    "                      create_resource_group = True,\n",
    "                      exist_ok = True)\n",
    "ws.get_details()\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to workspace\n",
    "\n",
    "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `ws`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "load workspace"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found the config file in: /data/aml_config/config.json\n",
      "cv_ws\teastus2\tcvdemo_rg\teastus2\n"
     ]
    }
   ],
   "source": [
    "# load workspace configuration from the config.json file in the current folder.\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.location, ws.resource_group, ws.location, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create experiment\n",
    "\n",
    "Create an experiment to track the runs in your workspace. A workspace can have muliple experiments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "create experiment"
    ]
   },
   "outputs": [],
   "source": [
    "experiment_name = 'sklearn-mnist'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "exp = Experiment(workspace=ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create or Attach existing AmlCompute\n",
    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
    "\n",
    "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "create mlc",
     "amlcompute"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating a new compute target...\n",
      "Creating\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "Minimum number of nodes requested have been provisioned\n",
      "{'vmSize': 'STANDARD_D2_V2', 'modifiedTime': '2018-12-08T08:23:03.182849+00:00', 'nodeStateCounts': {'preemptedNodeCount': 0, 'idleNodeCount': 0, 'runningNodeCount': 0, 'preparingNodeCount': 0, 'unusableNodeCount': 0, 'leavingNodeCount': 0}, 'currentNodeCount': 0, 'targetNodeCount': 0, 'provisioningState': 'Succeeded', 'provisioningStateTransitionTime': None, 'allocationState': 'Steady', 'allocationStateTransitionTime': '2018-12-08T08:22:58.550000+00:00', 'scaleSettings': {'nodeIdleTimeBeforeScaleDown': 'PT120S', 'maxNodeCount': 4, 'minNodeCount': 0}, 'vmPriority': 'Dedicated', 'creationTime': '2018-12-08T08:21:10.796522+00:00', 'errors': None}\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import AmlCompute\n",
    "from azureml.core.compute import ComputeTarget\n",
    "import os\n",
    "\n",
    "# choose a name for your cluster\n",
    "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpucluster\")\n",
    "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n",
    "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n",
    "\n",
    "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
    "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n",
    "\n",
    "\n",
    "if compute_name in ws.compute_targets:\n",
    "    compute_target = ws.compute_targets[compute_name]\n",
    "    if compute_target and type(compute_target) is AmlCompute:\n",
    "        print('found compute target. just use it. ' + compute_name)\n",
    "else:\n",
    "    print('creating a new compute target...')\n",
    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n",
    "                                                                min_nodes = compute_min_nodes, \n",
    "                                                                max_nodes = compute_max_nodes)\n",
    "\n",
    "    # create the cluster\n",
    "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
    "    \n",
    "    # can poll for a minimum number of nodes and for a specific timeout. \n",
    "    # if no min node count is provided it will use the scale settings for the cluster\n",
    "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
    "    \n",
    "     # For a more detailed view of current AmlCompute status, use the 'status' property    \n",
    "    print(compute_target.status.serialize())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now have the necessary packages and compute resources to train a model in the cloud. \n",
    "\n",
    "## Explore data\n",
    "\n",
    "Before you train a model, you need to understand the data that you are using to train it.  You also need to copy the data into the cloud so it can be accessed by your cloud training environment.  In this section you learn how to:\n",
    "\n",
    "* Download the MNIST dataset\n",
    "* Display some sample images\n",
    "* Upload data to the cloud\n",
    "\n",
    "### Download the MNIST dataset\n",
    "\n",
    "Download the MNIST dataset and save the files into a `data` directory locally.  Images and labels for both training and testing are downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('./data/test-labels.gz', <http.client.HTTPMessage at 0x7f058817ca58>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "\n",
    "os.makedirs('./data', exist_ok = True)\n",
    "\n",
    "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename='./data/train-images.gz')\n",
    "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename='./data/train-labels.gz')\n",
    "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename='./data/test-images.gz')\n",
    "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename='./data/test-labels.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display some sample images\n",
    "\n",
    "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `util.py` file. This file is included in the sample folder. Please make sure it is placed in the same folder as this notebook. The `load_data` function simply parses the compresse files into numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import load_data\n",
    "\n",
    "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n",
    "X_train = load_data('./data/train-images.gz', False) / 255.0\n",
    "y_train = load_data('./data/train-labels.gz', True).reshape(-1)\n",
    "\n",
    "X_test = load_data('./data/test-images.gz', False) / 255.0\n",
    "y_test = load_data('./data/test-labels.gz', True).reshape(-1)\n",
    "\n",
    "# now let's show some randomly chosen images from the traininng set.\n",
    "count = 0\n",
    "sample_size = 30\n",
    "plt.figure(figsize = (16, 6))\n",
    "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
    "    count = count + 1\n",
    "    plt.subplot(1, sample_size, count)\n",
    "    plt.axhline('')\n",
    "    plt.axvline('')\n",
    "    plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n",
    "    plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have an idea of what these images look like and the expected prediction outcome.\n",
    "\n",
    "### Upload data to the cloud\n",
    "\n",
    "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
    "\n",
    "The MNIST files are uploaded into a directory named `mnist` at the root of the datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "use datastore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AzureBlob cvwsstoragesiuuxjnq azureml-blobstore-0a19701f-cf5f-4472-88cc-093996fea2a5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "$AZUREML_DATAREFERENCE_500db3be53224e5b94f7a6d0f78d500e"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ws.get_default_datastore()\n",
    "print(ds.datastore_type, ds.account_name, ds.container_name)\n",
    "\n",
    "ds.upload(src_dir='./data', target_path='mnist', overwrite=True, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now have everything you need to start training a model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train on a remote Azure ML cluster\n",
    "\n",
    "For this task, submit the job to the remote training cluster you set up earlier.  To submit a job you:\n",
    "* Create a directory\n",
    "* Create a training script\n",
    "* Create an estimator object\n",
    "* Submit the job \n",
    "\n",
    "### Create a directory\n",
    "\n",
    "Create a directory to deliver the necessary code from your computer to the remote resource."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "script_folder = './sklearn-mnist'\n",
    "os.makedirs(script_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a training script\n",
    "\n",
    "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train.py` in the directory you just created. This training adds a regularization rate to the training algorithm, so produces a slightly different model than the local version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing ./sklearn-mnist/train.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile $script_folder/train.py\n",
    "\n",
    "import argparse\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "from azureml.core import Run\n",
    "from utils import load_data\n",
    "\n",
    "# let user feed in 2 parameters, the location of the data files (from datastore), and the regularization rate of the logistic regression model\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')\n",
    "parser.add_argument('--regularization', type=float, dest='reg', default=0.01, help='regularization rate')\n",
    "args = parser.parse_args()\n",
    "\n",
    "data_folder = os.path.join(args.data_folder, 'mnist')\n",
    "print('Data folder:', data_folder)\n",
    "\n",
    "# load train and test set into numpy arrays\n",
    "# note we scale the pixel intensity values to 0-1 (by dividing it with 255.0) so the model can converge faster.\n",
    "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0\n",
    "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0\n",
    "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n",
    "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep = '\\n')\n",
    "\n",
    "# get hold of the current run\n",
    "run = Run.get_context()\n",
    "\n",
    "print('Train a logistic regression model with regularizaion rate of', args.reg)\n",
    "clf = LogisticRegression(C=1.0/args.reg, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "print('Predict the test set')\n",
    "y_hat = clf.predict(X_test)\n",
    "\n",
    "# calculate accuracy on the prediction\n",
    "acc = np.average(y_hat == y_test)\n",
    "print('Accuracy is', acc)\n",
    "\n",
    "run.log('regularization rate', np.float(args.reg))\n",
    "run.log('accuracy', np.float(acc))\n",
    "\n",
    "os.makedirs('outputs', exist_ok=True)\n",
    "# note file saved in the outputs folder is automatically uploaded into experiment record\n",
    "joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the script gets data and saves models:\n",
    "\n",
    "+ The training script reads an argument to find the directory containing the data.  When you submit the job later, you point to the datastore for this argument:\n",
    "`parser.add_argument('--data-folder', type=str, dest='data_folder', help='data directory mounting point')`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "+ The training script saves your model into a directory named outputs. <br/>\n",
    "`joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')`<br/>\n",
    "Anything written in this directory is automatically uploaded into your workspace. You'll access your model from this directory later in the tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `utils.py` is referenced from the training script to load the dataset correctly.  Copy this script into the script folder so that it can be accessed along with the training script on the remote resource."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./sklearn-mnist/utils.py'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shutil\n",
    "shutil.copy('utils.py', script_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an estimator\n",
    "\n",
    "An estimator object is used to submit the run.  Create your estimator by running the following code to define:\n",
    "\n",
    "* The name of the estimator object, `est`\n",
    "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
    "* The compute target.  In this case you will use the AmlCompute you created\n",
    "* The training script name, train.py\n",
    "* Parameters required from the training script \n",
    "* Python packages needed for training\n",
    "\n",
    "In this tutorial, this target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the datastore (`ds.as_mount()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "configure estimator"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.train.estimator import Estimator\n",
    "\n",
    "script_params = {\n",
    "    '--data-folder': ds.as_mount(),\n",
    "    '--regularization': 0.8\n",
    "}\n",
    "\n",
    "est = Estimator(source_directory=script_folder,\n",
    "                script_params=script_params,\n",
    "                compute_target=compute_target,\n",
    "                entry_script='train.py',\n",
    "                conda_packages=['scikit-learn','matplotlib'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit the job to the cluster\n",
    "\n",
    "Run the experiment by submitting the estimator object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": [
     "remote run",
     "amlcompute",
     "scikit-learn"
    ]
   },
   "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>sklearn-mnist</td><td>sklearn-mnist_1544258427260</td><td>azureml.scriptrun</td><td>Starting</td><td><a href=\"https://mlworkspace.azure.ai/portal/subscriptions/04f7ec88-8e28-41ed-8537-5e17766001f5/resourceGroups/cvdemo_rg/providers/Microsoft.MachineLearningServices/workspaces/cv_ws/experiments/sklearn-mnist/runs/sklearn-mnist_1544258427260\" 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: sklearn-mnist,\n",
       "Id: sklearn-mnist_1544258427260,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Starting)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run = exp.submit(config=est)\n",
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the call is asynchronous, it returns a **Preparing** or **Running** state as soon as the job is started.\n",
    "\n",
    "## Monitor a remote run\n",
    "\n",
    "In total, the first run takes **approximately 10 minutes**. But for subsequent runs, as long as the script dependencies don't change, the same image is reused and hence the container start up time is much faster.\n",
    "\n",
    "Here is what's happening while you wait:\n",
    "\n",
    "- **Image creation**: A Docker image is created matching the Python environment specified by the estimator. The image is uploaded to the workspace. Image creation and uploading takes **about 5 minutes**. \n",
    "\n",
    "  This stage happens once for each Python environment since the container is cached for subsequent runs.  During image creation, logs are streamed to the run history. You can monitor the image creation progress using these logs.\n",
    "\n",
    "- **Scaling**: If the remote cluster requires more nodes to execute the run than currently available, additional nodes are added automatically. Scaling typically takes **about 5 minutes.**\n",
    "\n",
    "- **Running**: In this stage, the necessary scripts and files are sent to the compute target, then data stores are mounted/copied, then the entry_script is run. While the job is running, stdout and the ./logs directory are streamed to the run history. You can monitor the run's progress using these logs.\n",
    "\n",
    "- **Post-Processing**: The ./outputs directory of the run is copied over to the run history in your workspace so you can access these results.\n",
    "\n",
    "\n",
    "You can check the progress of a running job in multiple ways. This tutorial uses a Jupyter widget as well as a `wait_for_completion` method. \n",
    "\n",
    "### Jupyter widget\n",
    "\n",
    "Watch the progress of the run with a Jupyter widget.  Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "use notebook widget"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c4ab7468d6744f29be1d8f2d03c233a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'log_level': 'NOTSET', 'send_telemetry': False,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get log results upon completion\n",
    "\n",
    "Model training and monitoring happen in the background. Wait until the model has completed training before running more code. Use `wait_for_completion` to show when the model training is complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": [
     "remote run",
     "amlcompute",
     "scikit-learn"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: sklearn-mnist_1544258427260\n",
      "\n",
      "Streaming azureml-logs/20_image_build_log.txt\n",
      "=============================================\n",
      "\n",
      "2018/12/08 08:40:35 Using acb_vol_501d07a8-bbe7-4ece-9965-d4856442d9d5 as the home volume\n",
      "2018/12/08 08:40:35 Creating Docker network: acb_default_network, driver: 'bridge'\n",
      "2018/12/08 08:40:35 Successfully set up Docker network: acb_default_network\n",
      "2018/12/08 08:40:35 Setting up Docker configuration...\n",
      "2018/12/08 08:40:36 Successfully set up Docker configuration\n",
      "2018/12/08 08:40:36 Logging in to registry: cvwsacriceacvut.azurecr.io\n",
      "2018/12/08 08:40:37 Successfully logged in\n",
      "2018/12/08 08:40:37 Executing step ID: acb_step_0. Working directory: '', Network: 'acb_default_network'\n",
      "2018/12/08 08:40:37 Obtaining source code and scanning for dependencies...\n",
      "2018/12/08 08:40:38 Successfully obtained source code and scanned for dependencies\n",
      "Sending build context to Docker daemon  139.8kB\n",
      "\n",
      "Step 1/13 : FROM mcr.microsoft.com/azureml/base:0.2.0\n",
      "0.2.0: Pulling from azureml/base\n",
      "3b37166ec614: Pulling fs layer\n",
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      "7ff5eaad8a49: Waiting\n",
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      "c7fb3351ecad: Waiting\n",
      "2e3debadcbf7: Waiting\n",
      "ebbcacd28e10: Verifying Checksum\n",
      "ebbcacd28e10: Download complete\n",
      "c7fb3351ecad: Verifying Checksum\n",
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      "3b37166ec614: Verifying Checksum\n",
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      "504facff238f: Download complete\n",
      "3b37166ec614: Pull complete\n",
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      "2e3debadcbf7: Pull complete\n",
      "7ff5eaad8a49: Pull complete\n",
      "8697dd9e92dc: Verifying Checksum\n",
      "8697dd9e92dc: Download complete\n",
      "0431a9485aa5: Verifying Checksum\n",
      "0431a9485aa5: Download complete\n",
      "112403772eb3: Verifying Checksum\n",
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      "8697dd9e92dc: Pull complete\n",
      "a8ab5d81aeba: Verifying Checksum\n",
      "a8ab5d81aeba: Download complete\n",
      "112403772eb3: Pull complete\n",
      "0431a9485aa5: Pull complete\n",
      "a8ab5d81aeba: Pull complete\n",
      "Digest: sha256:bddfac8e9a4406c13f314cefefa24f3f67e959a8d8b26067096f7ade6f02ef12\n",
      "Status: Downloaded newer image for mcr.microsoft.com/azureml/base:0.2.0\n",
      " ---> b588362a9d48\n",
      "Step 2/13 : USER root\n",
      " ---> Running in 3e3ca5078173\n",
      "Removing intermediate container 3e3ca5078173\n",
      " ---> b27cf6d3b0d7\n",
      "Step 3/13 : RUN mkdir -p $HOME/.cache\n",
      " ---> Running in f8a86c159251\n",
      "Removing intermediate container f8a86c159251\n",
      " ---> 10abc600b248\n",
      "Step 4/13 : WORKDIR /\n",
      " ---> Running in ecb445086b2f\n",
      "Removing intermediate container ecb445086b2f\n",
      " ---> 4df17bae2feb\n",
      "Step 5/13 : COPY azureml-setup/99brokenproxy /etc/apt/apt.conf.d/\n",
      " ---> 8535891a6e3b\n",
      "Step 6/13 : RUN if dpkg --compare-versions `conda --version | grep -oE '[^ ]+$'` lt 4.4.0; then conda install conda==4.4.11; fi\n",
      " ---> Running in 3e4a09ecc84f\n",
      "Removing intermediate container 3e4a09ecc84f\n",
      " ---> 67ae18fd3f46\n",
      "Step 7/13 : COPY azureml-setup/mutated_conda_dependencies.yml azureml-setup/mutated_conda_dependencies.yml\n",
      " ---> 07ef5c1ba62b\n",
      "Step 8/13 : RUN ldconfig /usr/local/cuda/lib64/stubs && conda env create -p /azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247 -f azureml-setup/mutated_conda_dependencies.yml && ldconfig\n",
      " ---> Running in 557bf40dc7f1\n",
      "Solving environment: ...working... done\n",
      "\u001b[91m\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "matplotlib-3.0.1     | 6.6 MB    | #######5   |  76% \u001b[0m\u001b[91m\n",
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      "matplotlib-3.0.1     | 6.6 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "blas-1.0             | 6 KB      |            |   0% \u001b[0m\u001b[91m\n",
      "blas-1.0             | 6 KB      | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "six-1.11.0           | 21 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "six-1.11.0           | 21 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "python-dateutil-2.7. | 275 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "python-dateutil-2.7. | 275 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pip-18.1             | 1.8 MB    |            |   0% \u001b[0m\u001b[91m\n",
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      "\n",
      "libgfortran-ng-7.3.0 | 1.3 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "libgfortran-ng-7.3.0 | 1.3 MB    | ########2  |  83% \u001b[0m\u001b[91m\n",
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      "\n",
      "mkl-2018.0.3         | 198.7 MB  |            |   0% \u001b[0m\u001b[91m\n",
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      "mkl-2018.0.3         | 198.7 MB  | #########6 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########6 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  97% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########7 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  98% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########8 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 |  99% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | #########9 | 100% \u001b[0m\u001b[91m\n",
      "mkl-2018.0.3         | 198.7 MB  | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "tk-8.6.8             | 3.1 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | #######6   |  77% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | ########8  |  89% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | #########8 |  98% \u001b[0m\u001b[91m\n",
      "tk-8.6.8             | 3.1 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pyparsing-2.3.0      | 101 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "pyparsing-2.3.0      | 101 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libpng-1.6.35        | 335 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "libpng-1.6.35        | 335 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "mkl_random-1.0.1     | 373 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "mkl_random-1.0.1     | 373 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "xz-5.2.4             | 366 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "xz-5.2.4             | 366 KB    | #########9 | 100% \u001b[0m\u001b[91m\n",
      "xz-5.2.4             | 366 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pcre-8.42            | 251 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "pcre-8.42            | 251 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "ncurses-6.0          | 920 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "ncurses-6.0          | 920 KB    | #######9   |  79% \u001b[0m\u001b[91m\n",
      "ncurses-6.0          | 920 KB    | #########1 |  92% \u001b[0m\u001b[91m\n",
      "ncurses-6.0          | 920 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "freetype-2.9.1       | 822 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "freetype-2.9.1       | 822 KB    | ########5  |  86% \u001b[0m\u001b[91m\n",
      "freetype-2.9.1       | 822 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "openssl-1.0.2p       | 3.5 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "openssl-1.0.2p       | 3.5 MB    | #######5   |  76% \u001b[0m\u001b[91m\n",
      "openssl-1.0.2p       | 3.5 MB    | #########4 |  95% \u001b[0m\u001b[91m\n",
      "openssl-1.0.2p       | 3.5 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "fontconfig-2.13.0    | 291 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "fontconfig-2.13.0    | 291 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "gst-plugins-base-1.1 | 6.3 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | #######5   |  75% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | ########8  |  89% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | #########9 |  99% \u001b[0m\u001b[91m\n",
      "gst-plugins-base-1.1 | 6.3 MB    | ########## | 100% \u001b[0m\u001b[91m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "certifi-2018.10.15   | 139 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "certifi-2018.10.15   | 139 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pytz-2018.7          | 250 KB    |            |   0% \u001b[0m\u001b[91m\n",
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      "pytz-2018.7          | 250 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "numpy-base-1.15.4    | 4.2 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "numpy-base-1.15.4    | 4.2 MB    | #######6   |  76% \u001b[0m\u001b[91m\n",
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      "numpy-base-1.15.4    | 4.2 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "icu-58.2             | 22.5 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.5 MB   | #7         |  18% \u001b[0m\u001b[91m\n",
      "icu-58.2             | 22.5 MB   | #####2     |  53% \u001b[0m\u001b[91m\n",
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      "icu-58.2             | 22.5 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libstdcxx-ng-8.2.0   | 2.9 MB    |            |   0% \u001b[0m\u001b[91m\n",
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      "libstdcxx-ng-8.2.0   | 2.9 MB    | #########9 | 100% \u001b[0m\u001b[91m\n",
      "libstdcxx-ng-8.2.0   | 2.9 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "jpeg-9b              | 248 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "jpeg-9b              | 248 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libxml2-2.9.8        | 2.0 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "libxml2-2.9.8        | 2.0 MB    | #######7   |  77% \u001b[0m\u001b[91m\n",
      "libxml2-2.9.8        | 2.0 MB    | #########4 |  94% \u001b[0m\u001b[91m\n",
      "libxml2-2.9.8        | 2.0 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "setuptools-40.6.2    | 604 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "setuptools-40.6.2    | 604 KB    | ########4  |  84% \u001b[0m\u001b[91m\n",
      "setuptools-40.6.2    | 604 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "qt-5.9.6             | 86.7 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | 7          |   7% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #7         |  18% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ##8        |  29% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ###9       |  39% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #####      |  50% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ######     |  60% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #######1   |  72% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ########1  |  82% \u001b[0m\u001b[91m\n",
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      "qt-5.9.6             | 86.7 MB   | #########6 |  97% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | #########9 |  99% \u001b[0m\u001b[91m\n",
      "qt-5.9.6             | 86.7 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "mkl_fft-1.0.6        | 150 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "mkl_fft-1.0.6        | 150 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libedit-3.1          | 171 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "libedit-3.1          | 171 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "kiwisolver-1.0.1     | 83 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "kiwisolver-1.0.1     | 83 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "sip-4.19.8           | 290 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "sip-4.19.8           | 290 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "dbus-1.13.2          | 554 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "dbus-1.13.2          | 554 KB    | #########  |  91% \u001b[0m\u001b[91m\n",
      "dbus-1.13.2          | 554 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "tornado-5.1.1        | 663 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "tornado-5.1.1        | 663 KB    | ########2  |  83% \u001b[0m\u001b[91m\n",
      "tornado-5.1.1        | 663 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libgcc-ng-8.2.0      | 7.6 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "libgcc-ng-8.2.0      | 7.6 MB    | #######5   |  76% \u001b[0m\u001b[91m\n",
      "libgcc-ng-8.2.0      | 7.6 MB    | #########8 |  98% \u001b[0m\u001b[91m\n",
      "libgcc-ng-8.2.0      | 7.6 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "python-3.6.2         | 27.0 MB   |            |   0% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | ##3        |  23% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | #####7     |  58% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | #######5   |  75% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | ########8  |  88% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | #########7 |  98% \u001b[0m\u001b[91m\n",
      "python-3.6.2         | 27.0 MB   | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "pyqt-5.9.2           | 5.6 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | #######5   |  75% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | ########6  |  86% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | #########4 |  94% \u001b[0m\u001b[91m\n",
      "pyqt-5.9.2           | 5.6 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "zlib-1.2.11          | 120 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "zlib-1.2.11          | 120 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "scikit-learn-0.20.1  | 5.7 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "scikit-learn-0.20.1  | 5.7 MB    | #######5   |  76% \u001b[0m\u001b[91m\n",
      "scikit-learn-0.20.1  | 5.7 MB    | #########5 |  96% \u001b[0m\u001b[91m\n",
      "scikit-learn-0.20.1  | 5.7 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "wheel-0.32.3         | 35 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "wheel-0.32.3         | 35 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libxcb-1.13          | 502 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "libxcb-1.13          | 502 KB    | ########   |  81% \u001b[0m\u001b[91m\n",
      "libxcb-1.13          | 502 KB    | #########9 |  99% \u001b[0m\u001b[91m\n",
      "libxcb-1.13          | 502 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "readline-7.0         | 1.1 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "readline-7.0         | 1.1 MB    | ########2  |  83% \u001b[0m\u001b[91m\n",
      "readline-7.0         | 1.1 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "libuuid-1.0.3        | 16 KB     |            |   0% \u001b[0m\u001b[91m\n",
      "libuuid-1.0.3        | 16 KB     | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "sqlite-3.23.1        | 1.5 MB    |            |   0% \u001b[0m\u001b[91m\n",
      "sqlite-3.23.1        | 1.5 MB    | ########3  |  83% \u001b[0m\u001b[91m\n",
      "sqlite-3.23.1        | 1.5 MB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "expat-2.2.6          | 187 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "expat-2.2.6          | 187 KB    | ########## | 100% \u001b[0m\u001b[91m\n",
      "\n",
      "intel-openmp-2019.1  | 885 KB    |            |   0% \u001b[0m\u001b[91m\n",
      "intel-openmp-2019.1  | 885 KB    | #########1 |  92% \u001b[0m\u001b[91m\n",
      "intel-openmp-2019.1  | 885 KB    | ########## | 100% \u001b[0m\n",
      "Downloading and Extracting Packages\n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n",
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      "Collecting adal<2.0.0,>=0.6.0 (from msrestazure>=0.4.33->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
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      "  Downloading https://files.pythonhosted.org/packages/cf/ae/94e70d49044ccc234bfdba20114fa947d7ba6eb68a2e452d89b920e62227/paramiko-2.4.2-py2.py3-none-any.whl (193kB)\n",
      "Requirement already satisfied: pip in /azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247/lib/python3.6/site-packages (from azure-cli-core>=2.0.38->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1)) (18.1)\n",
      "Collecting pyopenssl>=17.1.0 (from azure-cli-core>=2.0.38->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/96/af/9d29e6bd40823061aea2e0574ccb2fcf72bfd6130ce53d32773ec375458c/pyOpenSSL-18.0.0-py2.py3-none-any.whl (53kB)\n",
      "Collecting azure-nspkg>=2.0.0 (from azure-storage-nspkg>=3.0.0->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/c4/0c/c562be95a9a2ed52454f598571cf300b1114d0db2aa27f5b8ed3bb9cd0c0/azure_nspkg-3.0.2-py3-none-any.whl\n",
      "Collecting pyasn1>=0.1.1 (from ndg-httpsclient->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/d1/a1/7790cc85db38daa874f6a2e6308131b9953feb1367f2ae2d1123bb93a9f5/pyasn1-0.4.4-py2.py3-none-any.whl (72kB)\n",
      "Collecting docker-pycreds>=0.3.0 (from docker->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/f5/e8/f6bd1eee09314e7e6dee49cbe2c5e22314ccdb38db16c9fc72d2fa80d054/docker_pycreds-0.4.0-py2.py3-none-any.whl\n",
      "Collecting websocket-client>=0.32.0 (from docker->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/26/2d/f749a5c82f6192d77ed061a38e02001afcba55fe8477336d26a950ab17ce/websocket_client-0.54.0-py2.py3-none-any.whl (200kB)\n",
      "Collecting azure-cli-command-modules-nspkg>=2.0.0 (from azure-cli-profile>=2.0.26->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/e6/c9/cdeeeabc550848e2a07caa66cba28aa057d23b6feaa824ceafd32c3f2226/azure_cli_command_modules_nspkg-2.0.2-py2.py3-none-any.whl\n",
      "Collecting isodate>=0.6.0 (from msrest>=0.5.1->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/9b/9f/b36f7774ff5ea8e428fdcfc4bb332c39ee5b9362ddd3d40d9516a55221b2/isodate-0.6.0-py2.py3-none-any.whl (45kB)\n",
      "Collecting requests-oauthlib>=0.5.0 (from msrest>=0.5.1->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/94/e7/c250d122992e1561690d9c0f7856dadb79d61fd4bdd0e598087dce607f6c/requests_oauthlib-1.0.0-py2.py3-none-any.whl\n",
      "Collecting pycparser (from cffi!=1.11.3,>=1.7->cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/68/9e/49196946aee219aead1290e00d1e7fdeab8567783e83e1b9ab5585e6206a/pycparser-2.19.tar.gz (158kB)\n",
      "Collecting portalocker==1.2.1 (from azure-cli-telemetry->azure-cli-core>=2.0.38->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/57/41/05e79e5516db1cc0c967b3202388cde729f871c871b0a07bf24ff11adfcf/portalocker-1.2.1-py2.py3-none-any.whl\n",
      "Collecting pynacl>=1.0.1 (from paramiko>=2.0.8->azure-cli-core>=2.0.38->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/27/15/2cd0a203f318c2240b42cd9dd13c931ddd61067809fee3479f44f086103e/PyNaCl-1.3.0-cp34-abi3-manylinux1_x86_64.whl (759kB)\n",
      "Collecting bcrypt>=3.1.3 (from paramiko>=2.0.8->azure-cli-core>=2.0.38->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/b8/09/905ec939994e2c49dcffff72f823802557f166b3815ea54c1db3671eed42/bcrypt-3.1.4-cp36-cp36m-manylinux1_x86_64.whl (54kB)\n",
      "Collecting oauthlib>=0.6.2 (from requests-oauthlib>=0.5.0->msrest>=0.5.1->azureml-core==1.0.2.*->azureml-defaults->-r /azureml-setup/condaenv.tmosa39c.requirements.txt (line 1))\n",
      "  Downloading https://files.pythonhosted.org/packages/e6/d1/ddd9cfea3e736399b97ded5c2dd62d1322adef4a72d816f1ed1049d6a179/oauthlib-2.1.0-py2.py3-none-any.whl (121kB)\n",
      "Building wheels for collected packages: SecretStorage, pathspec, tabulate, antlr4-python3-runtime, pyyaml, pycparser\n",
      "  Running setup.py bdist_wheel for SecretStorage: started\n",
      "  Running setup.py bdist_wheel for SecretStorage: finished with status 'done'\n",
      "  Stored in directory: /root/.cache/pip/wheels/4e/5b/1b/be8c8a830a0243af85b2946a0aece2c6743d7f7f946977ed67\n",
      "  Running setup.py bdist_wheel for pathspec: started\n",
      "  Running setup.py bdist_wheel for pathspec: finished with status 'done'\n",
      "  Stored in directory: /root/.cache/pip/wheels/45/cb/7e/ce6e6062c69446e39e328170524ca8213498bc66a74c6a210b\n",
      "  Running setup.py bdist_wheel for tabulate: started\n",
      "  Running setup.py bdist_wheel for tabulate: finished with status 'done'\n",
      "  Stored in directory: /root/.cache/pip/wheels/2a/85/33/2f6da85d5f10614cbe5a625eab3b3aebfdf43e7b857f25f829\n",
      "  Running setup.py bdist_wheel for antlr4-python3-runtime: started\n",
      "  Running setup.py bdist_wheel for antlr4-python3-runtime: finished with status 'done'\n",
      "  Stored in directory: /root/.cache/pip/wheels/ef/f6/18/ad300e691236a3408a99edc750484b56e8d6b11b2c38eacb10\n",
      "  Running setup.py bdist_wheel for pyyaml: started\n",
      "  Running setup.py bdist_wheel for pyyaml: finished with status 'done'\n",
      "  Stored in directory: /root/.cache/pip/wheels/ad/da/0c/74eb680767247273e2cf2723482cb9c924fe70af57c334513f\n",
      "  Running setup.py bdist_wheel for pycparser: started\n",
      "  Running setup.py bdist_wheel for pycparser: finished with status 'done'\n",
      "  Stored in directory: /root/.cache/pip/wheels/f2/9a/90/de94f8556265ddc9d9c8b271b0f63e57b26fb1d67a45564511\n",
      "Successfully built SecretStorage pathspec tabulate antlr4-python3-runtime pyyaml pycparser\n",
      "\u001b[91mrequests 2.20.1 has requirement idna<2.8,>=2.5, but you'll have idna 2.8 which is incompatible.\n",
      "\u001b[0mInstalling collected packages: applicationinsights, asn1crypto, pycparser, cffi, idna, cryptography, PyJWT, chardet, urllib3, requests, isodate, oauthlib, requests-oauthlib, msrest, azure-common, adal, msrestazure, azure-nspkg, azure-mgmt-nspkg, azure-mgmt-keyvault, ruamel.yaml, azure-storage-common, backports.weakref, backports.tempfile, azure-cli-nspkg, colorama, wheel, jmespath, argcomplete, tabulate, pygments, pyyaml, knack, humanfriendly, portalocker, azure-cli-telemetry, antlr4-python3-runtime, azure-mgmt-resource, pyasn1, pynacl, bcrypt, paramiko, pyopenssl, azure-cli-core, azure-storage-nspkg, azure-mgmt-authorization, azure-mgmt-containerregistry, ndg-httpsclient, azure-storage-blob, docker-pycreds, websocket-client, docker, SecretStorage, pathspec, contextlib2, azure-graphrbac, jsonpickle, azure-cli-command-modules-nspkg, azure-cli-profile, azure-mgmt-storage, azureml-core, azureml-defaults\n",
      "  Found existing installation: wheel 0.32.3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Uninstalling wheel-0.32.3:\n",
      "      Successfully uninstalled wheel-0.32.3\n",
      "Successfully installed PyJWT-1.7.1 SecretStorage-2.3.1 adal-1.2.0 antlr4-python3-runtime-4.7.1 applicationinsights-0.11.7 argcomplete-1.9.4 asn1crypto-0.24.0 azure-cli-command-modules-nspkg-2.0.2 azure-cli-core-2.0.52 azure-cli-nspkg-3.0.3 azure-cli-profile-2.1.2 azure-cli-telemetry-1.0.0 azure-common-1.1.16 azure-graphrbac-0.53.0 azure-mgmt-authorization-0.51.1 azure-mgmt-containerregistry-2.4.0 azure-mgmt-keyvault-1.1.0 azure-mgmt-nspkg-3.0.2 azure-mgmt-resource-2.0.0 azure-mgmt-storage-3.1.0 azure-nspkg-3.0.2 azure-storage-blob-1.4.0 azure-storage-common-1.4.0 azure-storage-nspkg-3.1.0 azureml-core-1.0.2 azureml-defaults-1.0.2 backports.tempfile-1.0 backports.weakref-1.0.post1 bcrypt-3.1.4 cffi-1.11.5 chardet-3.0.4 colorama-0.4.1 contextlib2-0.5.5 cryptography-2.4.2 docker-3.6.0 docker-pycreds-0.4.0 humanfriendly-4.17 idna-2.8 isodate-0.6.0 jmespath-0.9.3 jsonpickle-1.0 knack-0.5.1 msrest-0.6.2 msrestazure-0.5.1 ndg-httpsclient-0.5.1 oauthlib-2.1.0 paramiko-2.4.2 pathspec-0.5.9 portalocker-1.2.1 pyasn1-0.4.4 pycparser-2.19 pygments-2.3.0 pynacl-1.3.0 pyopenssl-18.0.0 pyyaml-3.13 requests-2.20.1 requests-oauthlib-1.0.0 ruamel.yaml-0.15.51 tabulate-0.8.2 urllib3-1.23 websocket-client-0.54.0 wheel-0.30.0\n",
      "#\n",
      "# To activate this environment, use:\n",
      "# > source activate /azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247\n",
      "#\n",
      "# To deactivate an active environment, use:\n",
      "# > source deactivate\n",
      "#\n",
      "\n",
      "\u001b[91m\n",
      "\u001b[0mRemoving intermediate container 557bf40dc7f1\n",
      " ---> 7d0053e3f8cd\n",
      "Step 9/13 : ENV PATH /azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247/bin:$PATH\n",
      " ---> Running in 0f9ff894336c\n",
      "Removing intermediate container 0f9ff894336c\n",
      " ---> 33eb1ccd7be2\n",
      "Step 10/13 : ENV LD_LIBRARY_PATH /azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247/lib:$LD_LIBRARY_PATH\n",
      " ---> Running in 6ad8dffc80bf\n",
      "Removing intermediate container 6ad8dffc80bf\n",
      " ---> 7adfb0b4722c\n",
      "Step 11/13 : COPY azureml-setup/spark_cache.py azureml-setup/log4j.properties /azureml-setup/\n",
      " ---> c1cb496af2a8\n",
      "Step 12/13 : RUN if [ $SPARK_HOME ]; then /bin/bash -c '$SPARK_HOME/bin/spark-submit \"--repositories\" \"https://mmlspark.azureedge.net/maven\" \"--packages\" \"com.microsoft.ml.spark:mmlspark_2.11:0.12\" \"--conf\" \"spark.app.name=Azure ML Experiment\" \"--conf\" \"spark.yarn.maxAppAttempts=1\" \"--driver-java-options\" \"-Dlog4j.configuration=file:./azureml-setup/log4j.properties\" \"--conf\" \"spark.eventLog.enabled=true\" \"--conf\" \"spark.eventLog.dir=./azureml-logs\" /azureml-setup/spark_cache.py'; fi\n",
      " ---> Running in 04ac6a37d625\n",
      "Removing intermediate container 04ac6a37d625\n",
      " ---> 7370fb7ed469\n",
      "Step 13/13 : CMD [\"bash\"]\n",
      " ---> Running in d5fd42d08276\n",
      "Removing intermediate container d5fd42d08276\n",
      " ---> 5c8a5f090f3d\n",
      "Successfully built 5c8a5f090f3d\n",
      "Successfully tagged cvwsacriceacvut.azurecr.io/azureml/azureml_5b151a28decbf536e38a41dc6807bcde:latest\n",
      "2018/12/08 08:44:18 Executing step ID: acb_step_1. Working directory: '', Network: 'acb_default_network'\n",
      "2018/12/08 08:44:18 Pushing image: cvwsacriceacvut.azurecr.io/azureml/azureml_5b151a28decbf536e38a41dc6807bcde:latest, attempt 1\n",
      "The push refers to repository [cvwsacriceacvut.azurecr.io/azureml/azureml_5b151a28decbf536e38a41dc6807bcde]\n",
      "7562395e41d3: Preparing\n",
      "d50030208555: Preparing\n",
      "6a9b8183cf96: Preparing\n",
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      "19d043c86cbc: Preparing\n",
      "8823818c4748: Preparing\n",
      "e7ff3c8d56c2: Waiting\n",
      "ce619be2681d: Waiting\n",
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      "2fc9fc8da07e: Waiting\n",
      "150ddd7a1544: Waiting\n",
      "75b79e19929c: Waiting\n",
      "4775b2f378bb: Waiting\n",
      "883eafdbe580: Waiting\n",
      "19d043c86cbc: Waiting\n",
      "8823818c4748: Waiting\n",
      "7562395e41d3: Pushed\n",
      "4addd18df424: Pushed\n",
      "6a9b8183cf96: Pushed\n",
      "0143c672afad: Pushed\n",
      "2fc9fc8da07e: Pushed\n",
      "ce619be2681d: Pushed\n",
      "150ddd7a1544: Pushed\n",
      "75b79e19929c: Pushed\n",
      "4775b2f378bb: Pushed\n",
      "883eafdbe580: Pushed\n",
      "e7ff3c8d56c2: Pushed\n",
      "19d043c86cbc: Pushed\n",
      "db9d63ec4abf: Pushed\n",
      "8823818c4748: Pushed\n",
      "d50030208555: Pushed\n",
      "latest: digest: sha256:39cbc80333321fc9be9d3b98e066685f2fef8f498aa9d92a6a9ed8cb78884e2b size: 3459\n",
      "2018/12/08 08:47:10 Successfully pushed image: cvwsacriceacvut.azurecr.io/azureml/azureml_5b151a28decbf536e38a41dc6807bcde:latest\n",
      "2018/12/08 08:47:10 Step ID: acb_step_0 marked as successful (elapsed time in seconds: 221.136149)\n",
      "2018/12/08 08:47:10 Populating digests for step ID: acb_step_0...\n",
      "2018/12/08 08:47:12 Successfully populated digests for step ID: acb_step_0\n",
      "2018/12/08 08:47:12 Step ID: acb_step_1 marked as successful (elapsed time in seconds: 171.972292)\n",
      "The following dependencies were found:\n",
      "- image:\n",
      "    registry: cvwsacriceacvut.azurecr.io\n",
      "    repository: azureml/azureml_5b151a28decbf536e38a41dc6807bcde\n",
      "    tag: latest\n",
      "    digest: sha256:39cbc80333321fc9be9d3b98e066685f2fef8f498aa9d92a6a9ed8cb78884e2b\n",
      "  runtime-dependency:\n",
      "    registry: mcr.microsoft.com\n",
      "    repository: azureml/base\n",
      "    tag: 0.2.0\n",
      "    digest: sha256:bddfac8e9a4406c13f314cefefa24f3f67e959a8d8b26067096f7ade6f02ef12\n",
      "  git: {}\n",
      "\n",
      "\n",
      "Run ID: ch1 was successful after 6m41s\n",
      "\n",
      "Streaming azureml-logs/60_control_log.txt\n",
      "=========================================\n",
      "\n",
      "Streaming log file azureml-logs/60_control_log.txt\n",
      "Streaming log file azureml-logs/80_driver_log.txt\n",
      "\n",
      "Streaming azureml-logs/80_driver_log.txt\n",
      "========================================\n",
      "\n",
      "Data folder: /mnt/batch/tasks/shared/LS_root/jobs/cv_ws/azureml/sklearn-mnist_1544258427260/mounts/workspaceblobstore/mnist\n",
      "(60000, 784)\n",
      "(60000,)\n",
      "(10000, 784)\n",
      "(10000,)\n",
      "Train a logistic regression model with regularizaion rate of 0.8\n",
      "/azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/azureml-envs/azureml_ac30040531f964b0a6ea95232a0cb247/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "Predict the test set\n",
      "Accuracy is 0.9204\n",
      "\n",
      "\n",
      "The experiment completed successfully. Finalizing run...\n",
      "Logging experiment finalizing status in history service\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: sklearn-mnist_1544258427260\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'logFiles': {'azureml-logs/20_image_build_log.txt': 'https://cvwsstoragesiuuxjnq.blob.core.windows.net/azureml/ExperimentRun/sklearn-mnist_1544258427260/azureml-logs/20_image_build_log.txt?sv=2018-03-28&sr=b&sig=gIoJZg4hyLHCPYtOf00BQIF%2Byd0qs%2FdcVLE6xuX2CxM%3D&st=2018-12-08T08%3A48%3A50Z&se=2018-12-08T16%3A58%3A50Z&sp=r',\n",
       "  'azureml-logs/60_control_log.txt': 'https://cvwsstoragesiuuxjnq.blob.core.windows.net/azureml/ExperimentRun/sklearn-mnist_1544258427260/azureml-logs/60_control_log.txt?sv=2018-03-28&sr=b&sig=Z0gvxObmg88QiqUeYKaMFrr8CwvjQ0PP2dvV%2F1w4YKM%3D&st=2018-12-08T08%3A48%3A50Z&se=2018-12-08T16%3A58%3A50Z&sp=r',\n",
       "  'azureml-logs/80_driver_log.txt': 'https://cvwsstoragesiuuxjnq.blob.core.windows.net/azureml/ExperimentRun/sklearn-mnist_1544258427260/azureml-logs/80_driver_log.txt?sv=2018-03-28&sr=b&sig=AQufv%2Fmpf%2BcvtU5bytE3eNqK6HRzY95UGXQNkSsXnGA%3D&st=2018-12-08T08%3A48%3A50Z&se=2018-12-08T16%3A58%3A50Z&sp=r',\n",
       "  'azureml-logs/azureml.log': 'https://cvwsstoragesiuuxjnq.blob.core.windows.net/azureml/ExperimentRun/sklearn-mnist_1544258427260/azureml-logs/azureml.log?sv=2018-03-28&sr=b&sig=j997p%2F2R1Ylb0Lu1kjp5ZoJFBB2B3KhD%2FQzbJwlVOJw%3D&st=2018-12-08T08%3A48%3A50Z&se=2018-12-08T16%3A58%3A50Z&sp=r'},\n",
       " 'properties': {'ContentSnapshotId': '6554963c-1b1e-4ffb-9e3b-16005559a3f0',\n",
       "  'azureml.runsource': 'experiment'},\n",
       " 'runDefinition': {'AmlCompute': {'ClusterMaxNodeCount': 1,\n",
       "   'Name': None,\n",
       "   'RetainCluster': False,\n",
       "   'VmPriority': None,\n",
       "   'VmSize': None},\n",
       "  'Arguments': ['--regularization',\n",
       "   '0.8',\n",
       "   '--data-folder',\n",
       "   '$AZUREML_DATAREFERENCE_workspaceblobstore'],\n",
       "  'AutoPrepareEnvironment': True,\n",
       "  'BatchAi': {'NodeCount': 0},\n",
       "  'Communicator': 0,\n",
       "  'ContainerInstance': {'CpuCores': 0, 'MemoryGb': 0, 'Region': None},\n",
       "  'DataReferences': {'workspaceblobstore': {'DataStoreName': 'workspaceblobstore',\n",
       "    'Mode': 'Mount',\n",
       "    'Overwrite': False,\n",
       "    'PathOnCompute': None,\n",
       "    'PathOnDataStore': None}},\n",
       "  'Environment': {'Docker': {'Arguments': [],\n",
       "    'BaseImage': 'mcr.microsoft.com/azureml/base:0.2.0',\n",
       "    'BaseImageRegistry': {'Address': None, 'Password': None, 'Username': None},\n",
       "    'Enabled': True,\n",
       "    'GpuSupport': False,\n",
       "    'Preparation': None,\n",
       "    'SharedVolumes': True},\n",
       "   'EnvironmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'},\n",
       "   'Python': {'CondaDependencies': {'dependencies': ['python=3.6.2',\n",
       "      {'pip': ['azureml-defaults']},\n",
       "      'scikit-learn',\n",
       "      'matplotlib'],\n",
       "     'name': 'project_environment'},\n",
       "    'CondaDependenciesFile': None,\n",
       "    'InterpreterPath': 'python',\n",
       "    'UserManagedDependencies': False},\n",
       "   'Spark': {'Packages': [{'Artifact': 'mmlspark_2.11',\n",
       "      'Group': 'com.microsoft.ml.spark',\n",
       "      'Version': '0.12'}],\n",
       "    'PrecachePackages': True,\n",
       "    'Repositories': ['https://mmlspark.azureedge.net/maven']}},\n",
       "  'ExposedPorts': None,\n",
       "  'Framework': 0,\n",
       "  'Hdi': {'YarnDeployMode': 2},\n",
       "  'History': {'OutputCollection': True},\n",
       "  'JobName': None,\n",
       "  'MaxRunDurationSeconds': None,\n",
       "  'Mpi': {'ProcessCountPerNode': 1},\n",
       "  'NodeCount': 1,\n",
       "  'PrepareEnvironment': None,\n",
       "  'Script': 'train.py',\n",
       "  'SourceDirectoryDataStore': None,\n",
       "  'Spark': {'Configuration': {'spark.app.name': 'Azure ML Experiment',\n",
       "    'spark.yarn.maxAppAttempts': '1'}},\n",
       "  'Target': 'cpucluster',\n",
       "  'Tensorflow': {'ParameterServerCount': 1, 'WorkerCount': 1}},\n",
       " 'runId': 'sklearn-mnist_1544258427260',\n",
       " 'startTimeUtc': '2018-12-08T08:52:22.194256Z',\n",
       " 'status': 'Finalizing',\n",
       " 'target': 'cpucluster'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.wait_for_completion(show_output=True) # specify True for a verbose log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display run results\n",
    "\n",
    "You now have a model trained on a remote cluster.  Retrieve the accuracy of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "get metrics"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.9204, 'regularization rate': 0.8}\n"
     ]
    }
   ],
   "source": [
    "print(run.get_metrics())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next tutorial you will explore this model in more detail.\n",
    "\n",
    "## Register model\n",
    "\n",
    "The last step in the training script wrote the file `outputs/sklearn_mnist_model.pkl` in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this  directory is automatically uploaded to your workspace.  This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n",
    "\n",
    "You can see files associated with that run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['azureml-logs/20_image_build_log.txt', 'azureml-logs/60_control_log.txt', 'azureml-logs/80_driver_log.txt', 'outputs/sklearn_mnist_model.pkl', 'driver_log', 'azureml-logs/azureml.log']\n"
     ]
    }
   ],
   "source": [
    "print(run.get_file_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Register the model in the workspace so that you (or other collaborators) can later query, examine, and deploy this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "register model from history"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Deprecated, use RunHistoryFacade.assets instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn_mnist\tsklearn_mnist:1\t1\n"
     ]
    }
   ],
   "source": [
    "# register model \n",
    "model = run.register_model(model_name='sklearn_mnist', model_path='outputs/sklearn_mnist_model.pkl')\n",
    "print(model.name, model.id, model.version, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy up Compute Resource when not in use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_target.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "In this Azure Machine Learning tutorial, you used Python to:\n",
    "\n",
    "> * Set up your development environment\n",
    "> * Access and examine the data\n",
    "> * Train a simple logistic regression locally using the popular scikit-learn machine learning library\n",
    "> * Train multiple models on a remote cluster\n",
    "> * Review training details and register the best model\n",
    "\n",
    "You are ready to deploy this registered model using the instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "authors": [
   {
    "name": "roastala"
   }
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