MachineLearningNotebooks/01.getting-started/07.hyperdrive-with-sklearn/07.hyperdrive-with-sklearn.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 07. HyperDrive with scikit-learn\n",
    "- Create Batch AI cluster\n",
    "- Train on a single node\n",
    "- Set up Hyperdrive\n",
    "- Parameter sweep with Hyperdrive on Batch AI cluster\n",
    "- Monitor parameter sweep runs with run history widget\n",
    "- Find best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create An Experiment\n",
    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "experiment_name = 'hyperdrive-with-sklearn'\n",
    "experiment = Experiment(workspace = ws, name = experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a folder to store the training script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "script_folder = './samples/hyperdrive-with-sklearn'\n",
    "os.makedirs(script_folder, exist_ok = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Provision New Cluster\n",
    "Create a new Batch AI cluster using the following Python code.\n",
    "\n",
    "**Note**: As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create mlc",
     "batchai"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.compute import BatchAiCompute\n",
    "from azureml.core.compute import ComputeTarget\n",
    "\n",
    "# choose a name for your cluster\n",
    "batchai_cluster_name = ws.name + \"cpu\"\n",
    "\n",
    "found = False\n",
    "# see if this compute target already exists in the workspace\n",
    "for ct in ws.compute_targets():\n",
    "    print(ct.name, ct.type)\n",
    "    if (ct.name == batchai_cluster_name and ct.type == 'BatchAI'):\n",
    "        found = True\n",
    "        print('found compute target. just use it.')\n",
    "        compute_target = ct\n",
    "        break\n",
    "        \n",
    "if not found:\n",
    "    print('creating a new compute target...')\n",
    "    provisioning_config = BatchAiCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
    "                                                                #vm_priority = 'lowpriority', # optional\n",
    "                                                                autoscale_enabled = True,\n",
    "                                                                cluster_min_nodes = 1, \n",
    "                                                                cluster_max_nodes = 4)\n",
    "\n",
    "    # create the cluster\n",
    "    compute_target = ComputeTarget.create(ws,batchai_cluster_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 BatchAI cluster status, use the 'status' property    \n",
    "    print(compute_target.status.serialize())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression with scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shutil import copyfile\n",
    "# copy the diabetes_sklearn.py file to the project folder\n",
    "copyfile('./diabetes_sklearn.py', os.path.join(script_folder, 'diabetes_sklearn.py'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# review the diabetes_sklearn.py file if you'd like\n",
    "with open(os.path.join(script_folder, 'diabetes_sklearn.py'), 'r') as fin:\n",
    "    print (fin.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an estimator for the sklearn script\n",
    "You can use an estimator pattern to run the script. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "configure run"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.train.estimator import Estimator\n",
    "script_params = {\n",
    "    '--alpha': 0.1\n",
    "}\n",
    "\n",
    "sk_est = Estimator(source_directory = script_folder,\n",
    "                   script_params = script_params,\n",
    "                   compute_target = compute_target,\n",
    "                   entry_script = 'diabetes_sklearn.py',\n",
    "                   conda_packages = ['scikit-learn'])\n",
    "                   #custom_docker_base_image = 'ninghai/azureml:0.3') # use a custom image here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remote run",
     "batchai"
    ]
   },
   "outputs": [],
   "source": [
    "# start the job\n",
    "from azureml.core.experiment import Experiment\n",
    "\n",
    "run = experiment.submit(sk_est)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View run details\n",
    "**IMPORTANT**: please use Chrome to navigate the below URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remote run",
     "batchai"
    ]
   },
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "use notebook widget"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also check the Batch AI cluster and job status using az-cli commands:\n",
    "\n",
    "```shell\n",
    "# check cluster status. You can see how many nodes are running.\n",
    "$ az batchai cluster list\n",
    "\n",
    "# check job status. You can see how many jobs are running\n",
    "$ az batchai job list\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now Try a Hyperdrive run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "configure run"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.train.hyperdrive import *\n",
    "\n",
    "# parameter space to sweep over\n",
    "ps = RandomParameterSampling(\n",
    "    {\n",
    "        \"alpha\": uniform(0.0, 1.0)\n",
    "    }\n",
    ")\n",
    "\n",
    "# early termniation policy\n",
    "# check every 2 iterations and if the primary metric (epoch_val_acc) falls\n",
    "# outside of the range of 10% of the best recorded run so far, terminate it.\n",
    "etp = BanditPolicy(slack_factor = 0.1, evaluation_interval = 2)\n",
    "\n",
    "# Hyperdrive run configuration\n",
    "hrc = HyperDriveRunConfig(\n",
    "    estimator = sk_est,\n",
    "    hyperparameter_sampling = ps,\n",
    "    policy = etp,\n",
    "    # metric to watch (for early termination)\n",
    "    primary_metric_name = 'mse',\n",
    "    # terminate if metric falls below threshold\n",
    "    primary_metric_goal = PrimaryMetricGoal.MINIMIZE,\n",
    "    max_total_runs = 20,\n",
    "    max_concurrent_runs = 4,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hyperdrive run",
     "batchai"
    ]
   },
   "outputs": [],
   "source": [
    "# Start Hyperdrive run\n",
    "\n",
    "hr = experiment.submit(hrc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a widget to show runs\n",
    "Runs will automatically start to show in the following widget once rendered. You can keep the Notebook open and watch them \"grow\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "use notebook widget"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(hr).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: This is a sample image with 200 runs. Your result might look different.\n",
    "![img](../images/hyperdrive-sklearn.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check cluster status, pay attention to the # of running nodes\n",
    "# !az batchai cluster list -o table\n",
    "\n",
    "# check the Batch AI job queue. Notice the Job name is the run history Id. Pay attention to the State of the job.\n",
    "# !az batchai job list -o table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find best run\n",
    "Wait until all Hyperdrive runs finish before running the below cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "hr.get_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history",
     "get metrics"
    ]
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "runs = {}\n",
    "\n",
    "for r in tqdm(hr.get_children()):\n",
    "    metrics = r.get_metrics()\n",
    "    if ('mse' in metrics.keys()):\n",
    "        runs[r.id] = metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "best_run_id = min(runs, key = lambda k: runs[k]['mse'])\n",
    "best_run = runs[best_run_id]\n",
    "print('Best Run: alpha = {0:.4f}, MSE = {1:.4f}'.format(best_run['alpha'], best_run['mse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the best run [Optional] \n",
    "Note you will need to install `matplotlib` for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get metrics of alpha and mse for all runs\n",
    "metrics = np.array([[runs[r]['alpha'], runs[r]['mse']] for r in runs])\n",
    "\n",
    "# sort the metrics by alpha values\n",
    "metrics = np.array(sorted(metrics, key = lambda m: m[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.title('MSE over alpha', fontsize = 16)\n",
    "\n",
    "plt.plot(metrics[:,0], metrics[:,1], 'r--')\n",
    "plt.plot(metrics[:,0], metrics[:,1], 'bo')\n",
    "\n",
    "plt.xlabel('alpha', fontsize = 14)\n",
    "plt.ylabel('mean squared error', fontsize = 14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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