05B - Working with Compute Targets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Compute Targets\n",
    "\n",
    "You've used the Azure Machine Learning SDK to run many experiments, all of them on your local compute (in this case, the Azure Machine Learning notebook VM). Now it's time to see how you can leverage cloud computing to increase the scalability of your compute contexts.\n",
    "\n",
    "## Connect to Your Workspace\n",
    "\n",
    "The first thing you need to do is to connect to your workspace using the Azure ML SDK.\n",
    "\n",
    "> **Note**: If the authenticated session with your Azure subscription has expired since you completed the previous exercise, you'll be prompted to reauthenticate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Workspace\n",
    "\n",
    "# Load the workspace from the saved config file\n",
    "ws = Workspace.from_config()\n",
    "print('Ready to use Azure ML {} to work with {}'.format(azureml.core.VERSION, ws.name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data for an Experiment\n",
    "\n",
    "In this lab, you'll use a dataset containing details of diabetes patients. Run the cell below to create this dataset (if you created it in the previous lab, the code will create a new version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Dataset\n",
    "\n",
    "default_ds = ws.get_default_datastore()\n",
    "\n",
    "if 'diabetes dataset' not in ws.datasets:\n",
    "    default_ds.upload_files(files=['./data/diabetes.csv', './data/diabetes2.csv'], # Upload the diabetes csv files in /data\n",
    "                        target_path='diabetes-data/', # Put it in a folder path in the datastore\n",
    "                        overwrite=True, # Replace existing files of the same name\n",
    "                        show_progress=True)\n",
    "\n",
    "    #Create a tabular dataset from the path on the datastore (this may take a short while)\n",
    "    tab_data_set = Dataset.Tabular.from_delimited_files(path=(default_ds, 'diabetes-data/*.csv'))\n",
    "\n",
    "    # Register the tabular dataset\n",
    "    try:\n",
    "        tab_data_set = tab_data_set.register(workspace=ws, \n",
    "                                name='diabetes dataset',\n",
    "                                description='diabetes data',\n",
    "                                tags = {'format':'CSV'},\n",
    "                                create_new_version=True)\n",
    "        print('Dataset registered.')\n",
    "    except Exception as ex:\n",
    "        print(ex)\n",
    "else:\n",
    "    print('Dataset already registered.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Compute Target\n",
    "\n",
    "In many cases, your local compute resources may not be sufficient to process a complex or long-running experiment that needs to process a large volume of data; and you may want to take advantage of the ability to dynamically create and use compute resources in the cloud.\n",
    "\n",
    "Azure ML supports a range of compute targets, which you can define in your workpace and use to run experiments; paying for the resources only when using them. When you set up the workspace in the first exercise, you created a compute cluster, so let's verify that exists (and if not, create it) so we can use it to run training experiments.\n",
    "\n",
    "> **Important**: Change *your-compute-cluster* to the name of your compute cluster in the code below before running it! Cluster names must be globally unique names between 2 to 16 characters in length. Valid characters are letters, digits, and the - character."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "cluster_name = \"your-compute-cluster\"\n",
    "\n",
    "try:\n",
    "    # Check for existing compute target\n",
    "    training_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
    "    print('Found existing cluster, use it.')\n",
    "except ComputeTargetException:\n",
    "    # If it doesn't already exist, create it\n",
    "    try:\n",
    "        compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS11_V2', max_nodes=2)\n",
    "        training_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
    "        training_cluster.wait_for_completion(show_output=True)\n",
    "    except Exception as ex:\n",
    "        print(ex)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run an Experiment on Remote Compute\n",
    "\n",
    "Now that you have created your compute, you can use it to run experiments. The following code creates a folder for experiment files (which may already exist from the previous lab, but run it anyway!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Create a folder for the experiment files\n",
    "experiment_folder = 'diabetes_training_tree'\n",
    "os.makedirs(experiment_folder, exist_ok=True)\n",
    "print(experiment_folder, 'folder created')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create the Python script file for the experiment. This will overwrite the script you used in the previous lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $experiment_folder/diabetes_training.py\n",
    "# Import libraries\n",
    "from azureml.core import Run\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the experiment run context\n",
    "run = Run.get_context()\n",
    "\n",
    "# load the diabetes data (passed as an input dataset)\n",
    "print(\"Loading Data...\")\n",
    "diabetes = run.input_datasets['diabetes'].to_pandas_dataframe()\n",
    "\n",
    "# Separate features and labels\n",
    "X, y = diabetes[['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']].values, diabetes['Diabetic'].values\n",
    "\n",
    "# Split data into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
    "\n",
    "# Train a decision tree model\n",
    "print('Training a decision tree model')\n",
    "model = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "\n",
    "# calculate accuracy\n",
    "y_hat = model.predict(X_test)\n",
    "acc = np.average(y_hat == y_test)\n",
    "print('Accuracy:', acc)\n",
    "run.log('Accuracy', np.float(acc))\n",
    "\n",
    "# calculate AUC\n",
    "y_scores = model.predict_proba(X_test)\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))\n",
    "run.log('AUC', np.float(auc))\n",
    "\n",
    "# plot ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "run.log_image(name = \"ROC\", plot = fig)\n",
    "plt.show()\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=model, filename='outputs/diabetes_model.pkl')\n",
    "\n",
    "run.complete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you're ready to run the experiment on the compute you created. \n",
    "\n",
    "> **Note**: The experiment will take quite a lot longer because a container image must be built with the conda environment, and then the cluster nodes must be started and the image deployed before the script can be run. For a simple experiment like the diabetes training script, this may seem inefficient; but imagine you needed to run a more complex experiment that takes several hours - dynamically creating more scalable compute may reduce the overall time significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from azureml.core import Environment, Experiment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "from azureml.train.estimator import Estimator\n",
    "from azureml.widgets import RunDetails\n",
    "\n",
    "# Create a Python environment for the experiment\n",
    "diabetes_env = Environment(\"diabetes-experiment-env\")\n",
    "diabetes_env.python.user_managed_dependencies = False # Let Azure ML manage dependencies\n",
    "diabetes_env.docker.enabled = True # Use a docker container\n",
    "\n",
    "# Create a set of package dependencies\n",
    "diabetes_packages = CondaDependencies.create(conda_packages=['scikit-learn','ipykernel','matplotlib', 'pandas'],\n",
    "                                             pip_packages=['azureml-sdk','pyarrow'])\n",
    "\n",
    "# Add the dependencies to the environment\n",
    "diabetes_env.python.conda_dependencies = diabetes_packages\n",
    "\n",
    "# Register the environment (just in case previous lab wasn't completed)\n",
    "diabetes_env.register(workspace=ws)\n",
    "registered_env = Environment.get(ws, 'diabetes-experiment-env')\n",
    "\n",
    "# Get the training dataset\n",
    "diabetes_ds = ws.datasets.get(\"diabetes dataset\")\n",
    "\n",
    "# Create an estimator\n",
    "estimator = Estimator(source_directory=experiment_folder,\n",
    "              inputs=[diabetes_ds.as_named_input('diabetes')],\n",
    "              compute_target = cluster_name, # Use the compute target created previously\n",
    "              environment_definition = registered_env,\n",
    "              entry_script='diabetes_training.py')\n",
    "\n",
    "# Create an experiment\n",
    "experiment = Experiment(workspace = ws, name = 'diabetes-training')\n",
    "\n",
    "# Run the experiment\n",
    "run = experiment.submit(config=estimator)\n",
    "# Show the run details while running\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While you're waiting for the experiment to run, you can check on the status of the compute in the widget above or in [Azure Machine Learning studio](https://ml.azure.com). You can also check the status of the compute using the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_state = training_cluster.get_status()\n",
    "print(cluster_state.allocation_state, cluster_state.current_node_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that it will take a while before the status changes from *steady* to *resizing* (now might be a good time to take a coffee break!). To block the kernel until the run completes, run the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the experiment has finished, you can get the metrics and files generated by the experiment run. This time, the files will include logs for building the image and managing the compute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get logged metrics\n",
    "metrics = run.get_metrics()\n",
    "for key in metrics.keys():\n",
    "        print(key, metrics.get(key))\n",
    "print('\\n')\n",
    "for file in run.get_file_names():\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can register the model that was trained by the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Model\n",
    "\n",
    "# Register the model\n",
    "run.register_model(model_path='outputs/diabetes_model.pkl', model_name='diabetes_model',\n",
    "                   tags={'Training context':'Azure ML compute'}, properties={'AUC': run.get_metrics()['AUC'], 'Accuracy': run.get_metrics()['Accuracy']})\n",
    "\n",
    "# List registered models\n",
    "for model in Model.list(ws):\n",
    "    print(model.name, 'version:', model.version)\n",
    "    for tag_name in model.tags:\n",
    "        tag = model.tags[tag_name]\n",
    "        print ('\\t',tag_name, ':', tag)\n",
    "    for prop_name in model.properties:\n",
    "        prop = model.properties[prop_name]\n",
    "        print ('\\t',prop_name, ':', prop)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**More Information**: For more information about compute targets in Azure Machine Learning, see the [documentation](https://docs.microsoft.com/azure/machine-learning/concept-compute-target)."
   ]
  }
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