mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-19 17:17:04 -05:00
version 1.0.8
This commit is contained in:
Roope Astala
@@ -1,328 +1,328 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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||||
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 06. Logging APIs\n",
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"This notebook showcase various ways to use the Azure Machine Learning service run logging APIs, and view the results in the Azure portal."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prerequisites\n",
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"Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
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"\n",
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"```\n",
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"(myenv) $ conda install -y tqdm matplotlib\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Validate Azure ML SDK installation and get version number for debugging purposes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"install"
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]
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},
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"outputs": [],
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"source": [
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"from azureml.core import Experiment, Run, Workspace\n",
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"import azureml.core\n",
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"import numpy as np\n",
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"\n",
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"# Check core SDK version number\n",
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"print(\"SDK version:\", azureml.core.VERSION)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initialize Workspace\n",
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"\n",
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"Initialize a workspace object from persisted configuration."
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]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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"metadata": {
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"tags": [
|
||||
"create workspace"
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]
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},
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"outputs": [],
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"source": [
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"ws = Workspace.from_config()\n",
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"print('Workspace name: ' + ws.name, \n",
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" 'Azure region: ' + ws.location, \n",
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" 'Subscription id: ' + ws.subscription_id, \n",
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" 'Resource group: ' + ws.resource_group, sep='\\n')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set experiment\n",
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"Create a new experiment (or get the one with such name)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"exp = Experiment(workspace=ws, name='logging-api-test')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Log metrics\n",
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"We will start a run, and use the various logging APIs to record different types of metrics during the run."
|
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tqdm import tqdm\n",
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"\n",
|
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"# start logging for the run\n",
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"run = exp.start_logging()\n",
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"\n",
|
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"# log a string value\n",
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"run.log(name='Name', value='Logging API run')\n",
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"\n",
|
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"# log a numerical value\n",
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"run.log(name='Magic Number', value=42)\n",
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"\n",
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"# Log a list of values. Note this will generate a single-variable line chart.\n",
|
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"run.log_list(name='Fibonacci', value=[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89])\n",
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"\n",
|
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"# create a dictionary to hold a table of values\n",
|
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"sines = {}\n",
|
||||
"sines['angle'] = []\n",
|
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"sines['sine'] = []\n",
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||||
"\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",
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" \n",
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" sines['angle'].append(angle)\n",
|
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" sines['sine'].append(np.sin(angle))\n",
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"\n",
|
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"# log a dictionary as a table, this will generate a 2-variable chart if you have 2 numerical columns\n",
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"run.log_table(name='Sine Wave', value=sines)\n",
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"\n",
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"run.complete()"
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]
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},
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{
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"cell_type": "markdown",
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||||
"metadata": {},
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"source": [
|
||||
"Even after the run is marked completed, you can still log things."
|
||||
]
|
||||
},
|
||||
{
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Log an image\n",
|
||||
"This is how to log a _matplotlib_ pyplot object."
|
||||
]
|
||||
},
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||||
{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"angle = np.linspace(-3, 3, 50)\n",
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"plt.plot(angle, np.tanh(angle), label='tanh')\n",
|
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"plt.legend(fontsize=12)\n",
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||||
"plt.title('Hyperbolic Tangent', fontsize=16)\n",
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||||
"plt.grid(True)\n",
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"\n",
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"run.log_image(name='Hyperbolic Tangent', plot=plt)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Upload a file"
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]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can also upload an abitrary file. First, let's create a dummy file locally."
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"%%writefile myfile.txt\n",
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"\n",
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"This is a dummy file."
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]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's upload this file into the run record as a run artifact, and display the properties after the upload."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"props = run.upload_file(name='myfile_in_the_cloud.txt', path_or_stream='./myfile.txt')\n",
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"props.serialize()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Examine the run"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"run"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can get all the metrics in that run back."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"run.get_metrics()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can also see the files uploaded for this run."
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]
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||||
},
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||||
{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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||||
"run.get_file_names()"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
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||||
"You can also download all the files locally."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
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"os.makedirs('files', exist_ok=True)\n",
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"\n",
|
||||
"for f in run.get_file_names():\n",
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" dest = os.path.join('files', f.split('/')[-1])\n",
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" print('Downloading file {} to {}...'.format(f, dest))\n",
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" run.download_file(f, dest) "
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]
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}
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||||
],
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"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "haining"
|
||||
}
|
||||
"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": [
|
||||
"# 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 notebook](../../../configuration.ipynb) 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) "
|
||||
]
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "haining"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user