MachineLearningNotebooks/how-to-use-azureml/training/logging-api/logging-api.ipynb

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   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 06. Logging APIs\n",
    "This notebook showcase various ways to use the Azure Machine Learning service run logging APIs, and view the results in the Azure portal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [Configuration](../../../configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
    "\n",
    "```\n",
    "(myenv) $ conda install -y tqdm matplotlib\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate Azure ML SDK installation and get version number for debugging purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "install"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core import Experiment, Run, Workspace\n",
    "import azureml.core\n",
    "import numpy as np\n",
    "\n",
    "# Check core SDK version number\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": [
    "ws = Workspace.from_config()\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set experiment\n",
    "Create a new experiment (or get the one with such name)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = Experiment(workspace=ws, name='logging-api-test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log metrics\n",
    "We will start a run, and use the various logging APIs to record different types of metrics during the run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# start logging for the run\n",
    "run = exp.start_logging()\n",
    "\n",
    "# log a string value\n",
    "run.log(name='Name', value='Logging API run')\n",
    "\n",
    "# log a numerical value\n",
    "run.log(name='Magic Number', value=42)\n",
    "\n",
    "# Log a list of values. Note this will generate a single-variable line chart.\n",
    "run.log_list(name='Fibonacci', value=[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89])\n",
    "\n",
    "# create a dictionary to hold a table of values\n",
    "sines = {}\n",
    "sines['angle'] = []\n",
    "sines['sine'] = []\n",
    "\n",
    "for i in tqdm(range(-10, 10)):\n",
    "    # log a metric value repeatedly, this will generate a single-variable line chart.\n",
    "    run.log(name='Sigmoid', value=1 / (1 + np.exp(-i)))\n",
    "    angle = i / 2.0\n",
    "    \n",
    "    # log a 2 (or more) values as a metric repeatedly. This will generate a 2-variable line chart if you have 2 numerical columns.\n",
    "    run.log_row(name='Cosine Wave', angle=angle, cos=np.cos(angle))\n",
    "        \n",
    "    sines['angle'].append(angle)\n",
    "    sines['sine'].append(np.sin(angle))\n",
    "\n",
    "# log a dictionary as a table, this will generate a 2-variable chart if you have 2 numerical columns\n",
    "run.log_table(name='Sine Wave', value=sines)\n",
    "\n",
    "run.complete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even after the run is marked completed, you can still log things."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log an image\n",
    "This is how to log a _matplotlib_ pyplot object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "angle = np.linspace(-3, 3, 50)\n",
    "plt.plot(angle, np.tanh(angle), label='tanh')\n",
    "plt.legend(fontsize=12)\n",
    "plt.title('Hyperbolic Tangent', fontsize=16)\n",
    "plt.grid(True)\n",
    "\n",
    "run.log_image(name='Hyperbolic Tangent', plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload a file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also upload an abitrary file. First, let's create a dummy file locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile myfile.txt\n",
    "\n",
    "This is a dummy file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's upload this file into the run record as a run artifact, and display the properties after the upload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "props = run.upload_file(name='myfile_in_the_cloud.txt', path_or_stream='./myfile.txt')\n",
    "props.serialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the run detail page in Azure portal. Make sure you checkout the various charts and plots generated/uploaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get all the metrics in that run back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.get_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also see the files uploaded for this run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.get_file_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also download all the files locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.makedirs('files', exist_ok=True)\n",
    "\n",
    "for f in run.get_file_names():\n",
    "    dest = os.path.join('files', f.split('/')[-1])\n",
    "    print('Downloading file {} to {}...'.format(f, dest))\n",
    "    run.download_file(f, dest)   "
   ]
  }
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