mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-19 17:17:04 -05:00
version 1.0.18
This commit is contained in:
Roope Astala
@@ -96,7 +96,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version AZUREML-SDK-VERSION of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.0.18 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -336,7 +336,7 @@
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"\n",
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"In this notebook you configured this notebook library to connect easily to an Azure ML workspace. You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
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"\n",
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"If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models. A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
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"If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models. A good place to start is the [train in notebook](./how-to-use-azureml/training/train-in-notebook) example that walks through a simplified but complete end to end machine learning process."
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]
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},
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{
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@@ -20,7 +20,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model in Azure.\n",
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"The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c2\u00a0in Azure.\n",
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" \n",
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"In this notebook, we will do the following:\n",
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" \n",
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@@ -1 +0,0 @@
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google-site-verification: googleade5d7141b3f2910.html
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@@ -119,7 +119,7 @@ bash automl_setup_linux.sh
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- Retrieving models for any iteration or logged metric
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- Specify automl settings as kwargs
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- [auto-ml-remote-batchai.ipynb](remote-batchai/auto-ml-remote-batchai.ipynb)
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- [auto-ml-remote-amlcompute.ipynb](remote-batchai/auto-ml-remote-amlcompute.ipynb)
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- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
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- Example of using automated ML for classification using remote AmlCompute for training
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- Parallel execution of iterations
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@@ -16,6 +16,7 @@ dependencies:
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-sdk[automl,notebooks,explain]
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- azureml-sdk[automl,explain]
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- azureml-widgets
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- pandas_ml
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@@ -16,7 +16,8 @@ dependencies:
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-sdk[automl,notebooks,explain]
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- azureml-sdk[automl,explain]
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- azureml-widgets
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- pandas_ml
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@@ -195,7 +195,7 @@
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" dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
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" dsvm_compute.wait_for_completion(show_output = True)\n",
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" print(\"Waiting one minute for ssh to be accessible\")\n",
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" time.sleep(60) # Wait for ssh to be accessible"
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" time.sleep(90) # Wait for ssh to be accessible"
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]
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},
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{
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@@ -0,0 +1,555 @@
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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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"# Automated Machine Learning\n",
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"_**Remote Execution using AmlCompute**_\n",
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"\n",
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"## Contents\n",
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"1. [Introduction](#Introduction)\n",
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"1. [Setup](#Setup)\n",
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"1. [Data](#Data)\n",
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"1. [Train](#Train)\n",
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"1. [Results](#Results)\n",
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"1. [Test](#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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"## Introduction\n",
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"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
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"\n",
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"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
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"\n",
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"In this notebook you would see\n",
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"1. Create an `Experiment` in an existing `Workspace`.\n",
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"2. Create or Attach existing AmlCompute to a workspace.\n",
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"3. Configure AutoML using `AutoMLConfig`.\n",
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"4. Train the model using AmlCompute\n",
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"5. Explore the results.\n",
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"6. Test the best fitted model.\n",
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"\n",
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"In addition this notebook showcases the following features\n",
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"- **Parallel** executions for iterations\n",
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"- **Asynchronous** tracking of progress\n",
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"- **Cancellation** of individual iterations or the entire run\n",
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"- Retrieving models for any iteration or logged metric\n",
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"- Specifying AutoML settings as `**kwargs`"
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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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"## Setup\n",
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"\n",
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"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
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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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"import logging\n",
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"import os\n",
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"import csv\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn import datasets\n",
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"\n",
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"import azureml.core\n",
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"from azureml.core.experiment import Experiment\n",
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"from azureml.core.workspace import Workspace\n",
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"from azureml.train.automl import AutoMLConfig"
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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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"ws = Workspace.from_config()\n",
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"\n",
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"# Choose a name for the run history container in the workspace.\n",
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"experiment_name = 'automl-remote-amlcompute'\n",
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"project_folder = './project'\n",
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"\n",
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"experiment = Experiment(ws, experiment_name)\n",
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"\n",
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"output = {}\n",
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"output['SDK version'] = azureml.core.VERSION\n",
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"output['Subscription ID'] = ws.subscription_id\n",
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"output['Workspace Name'] = ws.name\n",
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"output['Resource Group'] = ws.resource_group\n",
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"output['Location'] = ws.location\n",
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"output['Project Directory'] = project_folder\n",
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"output['Experiment Name'] = experiment.name\n",
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"pd.set_option('display.max_colwidth', -1)\n",
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"outputDf = pd.DataFrame(data = output, index = [''])\n",
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"outputDf.T"
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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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"### Create or Attach existing AmlCompute\n",
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"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
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"\n",
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"**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.\n",
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"\n",
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"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) 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."
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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 azureml.core.compute import AmlCompute\n",
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"from azureml.core.compute import ComputeTarget\n",
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"\n",
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"# Choose a name for your cluster.\n",
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"amlcompute_cluster_name = \"automlcl\"\n",
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"\n",
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"found = False\n",
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"# Check if this compute target already exists in the workspace.\n",
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"cts = ws.compute_targets\n",
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"if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
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" found = True\n",
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||||
" print('Found existing compute target.')\n",
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||||
" compute_target = cts[amlcompute_cluster_name]\n",
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||||
" \n",
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||||
"if not found:\n",
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||||
" print('Creating a new compute target...')\n",
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||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
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||||
" #vm_priority = 'lowpriority', # optional\n",
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" max_nodes = 6)\n",
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"\n",
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||||
" # Create the cluster.\n",
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" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
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" \n",
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||||
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
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||||
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
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" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
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||||
" \n",
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||||
" # For a more detailed view of current AmlCompute status, use get_status()."
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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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},
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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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"## Data\n",
|
||||
"For remote executions, you need to make the data accessible from the remote compute.\n",
|
||||
"This can be done by uploading the data to DataStore.\n",
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||||
"In this example, we upload scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data."
|
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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": [
|
||||
"data_train = datasets.load_digits()\n",
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"\n",
|
||||
"if not os.path.isdir('data'):\n",
|
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" os.mkdir('data')\n",
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" \n",
|
||||
"if not os.path.exists(project_folder):\n",
|
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" os.makedirs(project_folder)\n",
|
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" \n",
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||||
"pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
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"pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
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"\n",
|
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"ds = ws.get_default_datastore()\n",
|
||||
"ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
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||||
"\n",
|
||||
"from azureml.core.runconfig import DataReferenceConfiguration\n",
|
||||
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
|
||||
" path_on_datastore='bai_data', \n",
|
||||
" path_on_compute='/tmp/azureml_runs',\n",
|
||||
" mode='download', # download files from datastore to compute target\n",
|
||||
" overwrite=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": null,
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to AmlCompute\n",
|
||||
"conda_run_config.target = compute_target\n",
|
||||
"conda_run_config.environment.docker.enabled = True\n",
|
||||
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# set the data reference of the run coonfiguration\n",
|
||||
"conda_run_config.data_references = {ds.name: dr}\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile $project_folder/get_data.py\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"def get_data():\n",
|
||||
" X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
" y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
"\n",
|
||||
" return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
|
||||
"\n",
|
||||
"**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_settings = {\n",
|
||||
" \"iteration_timeout_minutes\": 2,\n",
|
||||
" \"iterations\": 20,\n",
|
||||
" \"n_cross_validations\": 5,\n",
|
||||
" \"primary_metric\": 'AUC_weighted',\n",
|
||||
" \"preprocess\": False,\n",
|
||||
" \"max_concurrent_iterations\": 5,\n",
|
||||
" \"verbosity\": logging.INFO\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task = 'classification',\n",
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" path = project_folder,\n",
|
||||
" run_configuration=conda_run_config,\n",
|
||||
" data_script = project_folder + \"/get_data.py\",\n",
|
||||
" **automl_settings\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
|
||||
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run = experiment.submit(automl_config, show_output = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results\n",
|
||||
"\n",
|
||||
"#### Loading executed runs\n",
|
||||
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Widget for Monitoring Runs\n",
|
||||
"\n",
|
||||
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
|
||||
"\n",
|
||||
"You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
|
||||
"\n",
|
||||
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(remote_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait until the run finishes.\n",
|
||||
"remote_run.wait_for_completion(show_output = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"#### Retrieve All Child Runs\n",
|
||||
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"children = list(remote_run.get_children())\n",
|
||||
"metricslist = {}\n",
|
||||
"for run in children:\n",
|
||||
" properties = run.get_properties()\n",
|
||||
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
|
||||
" metricslist[int(properties['iteration'])] = metrics\n",
|
||||
"\n",
|
||||
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
|
||||
"rundata"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Cancelling Runs\n",
|
||||
"\n",
|
||||
"You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Cancel the ongoing experiment and stop scheduling new iterations.\n",
|
||||
"# remote_run.cancel()\n",
|
||||
"\n",
|
||||
"# Cancel iteration 1 and move onto iteration 2.\n",
|
||||
"# remote_run.cancel_iteration(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_model = remote_run.get_output()\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Best Model Based on Any Other Metric\n",
|
||||
"Show the run and the model which has the smallest `log_loss` value:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lookup_metric = \"log_loss\"\n",
|
||||
"best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Model from a Specific Iteration\n",
|
||||
"Show the run and the model from the third iteration:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"iteration = 3\n",
|
||||
"third_run, third_model = remote_run.get_output(iteration=iteration)\n",
|
||||
"print(third_run)\n",
|
||||
"print(third_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test\n",
|
||||
"\n",
|
||||
"#### Load Test Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"digits = datasets.load_digits()\n",
|
||||
"X_test = digits.data[:10, :]\n",
|
||||
"y_test = digits.target[:10]\n",
|
||||
"images = digits.images[:10]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Testing Our Best Fitted Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Randomly select digits and test.\n",
|
||||
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
|
||||
" print(index)\n",
|
||||
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
|
||||
" label = y_test[index]\n",
|
||||
" title = \"Label value = %d Predicted value = %d \" % (label, predicted)\n",
|
||||
" fig = plt.figure(1, figsize=(3,3))\n",
|
||||
" ax1 = fig.add_axes((0,0,.8,.8))\n",
|
||||
" ax1.set_title(title)\n",
|
||||
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
|
||||
" plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "savitam"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -128,7 +128,7 @@
|
||||
" dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
|
||||
" dsvm_compute.wait_for_completion(show_output=True)\n",
|
||||
" print(\"Waiting one minute for ssh to be accessible\")\n",
|
||||
" time.sleep(60) # Wait for ssh to be accessible"
|
||||
" time.sleep(90) # Wait for ssh to be accessible"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -77,7 +77,7 @@
|
||||
"source": [
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"aml_compute_target = \"aml-compute\"\n",
|
||||
"aml_compute_target = \"cpucluster\"\n",
|
||||
"try:\n",
|
||||
" aml_compute = AmlCompute(ws, aml_compute_target)\n",
|
||||
" print(\"found existing compute target.\")\n",
|
||||
@@ -280,7 +280,8 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Publish the pipeline"
|
||||
"## Run published pipeline\n",
|
||||
"### Publish the pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -290,7 +291,34 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\")\n",
|
||||
"print(published_pipeline1.id)"
|
||||
"published_pipeline1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Get published pipeline\n",
|
||||
"\n",
|
||||
"You can get the published pipeline using **pipeline id**.\n",
|
||||
"\n",
|
||||
"To get all the published pipelines for a given workspace(ws): \n",
|
||||
"```css\n",
|
||||
"all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PublishedPipeline\n",
|
||||
"\n",
|
||||
"pipeline_id = published_pipeline1.id # use your published pipeline id\n",
|
||||
"published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -312,9 +340,9 @@
|
||||
"auth = InteractiveLoginAuthentication()\n",
|
||||
"aad_token = auth.get_authentication_header()\n",
|
||||
"\n",
|
||||
"rest_endpoint1 = published_pipeline1.endpoint\n",
|
||||
"rest_endpoint1 = published_pipeline.endpoint\n",
|
||||
"\n",
|
||||
"print(rest_endpoint1)\n",
|
||||
"print(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint1))\n",
|
||||
"\n",
|
||||
"# specify the param when running the pipeline\n",
|
||||
"response = requests.post(rest_endpoint1, \n",
|
||||
|
||||
@@ -165,7 +165,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# choose a name for your cluster\n",
|
||||
"aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpu-cluster\")\n",
|
||||
"aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpucluster\")\n",
|
||||
"cluster_min_nodes = os.environ.get(\"AML_COMPUTE_MIN_NODES\", 0)\n",
|
||||
"cluster_max_nodes = os.environ.get(\"AML_COMPUTE_MAX_NODES\", 1)\n",
|
||||
"vm_size = os.environ.get(\"AML_COMPUTE_SKU\", \"STANDARD_NC6\")\n",
|
||||
@@ -466,7 +466,35 @@
|
||||
"published_pipeline = pipeline_run.publish_pipeline(\n",
|
||||
" name=\"Inception_v3_scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n",
|
||||
"\n",
|
||||
"published_id = published_pipeline.id"
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Get published pipeline\n",
|
||||
"\n",
|
||||
"You can get the published pipeline using **pipeline id**.\n",
|
||||
"\n",
|
||||
"To get all the published pipelines for a given workspace(ws): \n",
|
||||
"```css\n",
|
||||
"all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PublishedPipeline\n",
|
||||
"\n",
|
||||
"pipeline_id = published_pipeline.id # use your published pipeline id\n",
|
||||
"published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
|
||||
"\n",
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -450,7 +450,35 @@
|
||||
"published_pipeline = pipeline_run.publish_pipeline(\n",
|
||||
" name=\"batch score style transfer\", description=\"style transfer\", version=\"1.0\")\n",
|
||||
"\n",
|
||||
"published_id = published_pipeline.id"
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get published pipeline\n",
|
||||
"\n",
|
||||
"You can get the published pipeline using **pipeline id**.\n",
|
||||
"\n",
|
||||
"To get all the published pipelines for a given workspace(ws): \n",
|
||||
"```css\n",
|
||||
"all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PublishedPipeline\n",
|
||||
"\n",
|
||||
"pipeline_id = published_pipeline.id # use your published pipeline id\n",
|
||||
"published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
|
||||
"\n",
|
||||
"published_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,253 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License.\n",
|
||||
"\n",
|
||||
"## Authentication in Azure Machine Learning\n",
|
||||
"\n",
|
||||
"This notebook shows you how to authenticate to your Azure ML Workspace using\n",
|
||||
"\n",
|
||||
" 1. Interactive Login Authentication\n",
|
||||
" 2. Azure CLI Authentication\n",
|
||||
" 3. Service Principal Authentication\n",
|
||||
" \n",
|
||||
"The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Interactive Authentication\n",
|
||||
"\n",
|
||||
"Interactive authentication is the default mode when using Azure ML SDK.\n",
|
||||
"\n",
|
||||
"When you connect to your workspace using workspace.from_config, you will get an interactive login dialog."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Also, if you explicitly specify the subscription ID, resource group and resource group, you will get the dialog."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace(subscription_id=\"my-subscription-id\",\n",
|
||||
" resource_group=\"my-ml-rg\",\n",
|
||||
" workspace_name=\"my-ml-workspace\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note the user you're authenticated as must have access to the subscription and resource group. If you receive an error\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"AuthenticationException: You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription. All the subscriptions that you have access to = ...\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"check that the you used correct login and entered the correct subscription ID."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In some cases, you may see a version of the error message containing text: ```All the subscriptions that you have access to = []```\n",
|
||||
"\n",
|
||||
"In such a case, you may have to specify the tenant ID of the Azure Active Directory you're using. An example would be accessing a subscription as a guest to a tenant that is not your default. You specify the tenant by explicitly instantiating _InteractiveLoginAuthentication_ with tenant ID as argument ([see instructions how to obtain tenant Id](#get-tenant-id))."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.authentication import InteractiveLoginAuthentication\n",
|
||||
"\n",
|
||||
"interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\")\n",
|
||||
"\n",
|
||||
"ws = Workspace(subscription_id=\"my-subscription-id\",\n",
|
||||
" resource_group=\"my-ml-rg\",\n",
|
||||
" workspace_name=\"my-ml-workspace\",\n",
|
||||
" auth=interactive_auth)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Azure CLI Authentication\n",
|
||||
"\n",
|
||||
"If you have installed azure-cli package, and used ```az login``` command to log in to your Azure Subscription, you can use _AzureCliAuthentication_ class.\n",
|
||||
"\n",
|
||||
"Note that interactive authentication described above won't use existing Azure CLI auth tokens. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.authentication import AzureCliAuthentication\n",
|
||||
"\n",
|
||||
"cli_auth = AzureCliAuthentication()\n",
|
||||
"\n",
|
||||
"ws = Workspace(subscription_id=\"my-subscription-id\",\n",
|
||||
" resource_group=\"my-ml-rg\",\n",
|
||||
" workspace_name=\"my-ml-workspace\",\n",
|
||||
" auth=cli_auth)\n",
|
||||
"\n",
|
||||
"print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Service Principal Authentication\n",
|
||||
"\n",
|
||||
"When setting up a machine learning workflow as an automated process, we recommend using Service Principal Authentication. This approach decouples the authentication from any specific user login, and allows managed access control.\n",
|
||||
"\n",
|
||||
"Note that you must have administrator privileges over the Azure subscription to complete these steps.\n",
|
||||
"\n",
|
||||
"The first step is to create a service principal. First, go to [Azure Portal](https://portal.azure.com), select **Azure Active Directory** and **App Registrations**. Then select **+New application registration**, give your service principal a name, for example _my-svc-principal_. You can leave application type as is, and specify a dummy value for Sign-on URL, such as _https://invalid_.\n",
|
||||
"\n",
|
||||
"Then click **Create**.\n",
|
||||
"\n",
|
||||
"![service principal creation]<img src=\"images/svc-pr-1.PNG\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The next step is to obtain the _Application ID_ (also called username) and create _password_ for the service principal.\n",
|
||||
"\n",
|
||||
"From the page for your newly created service principal, copy the _Application ID_. Then select **Settings** and **Keys**, write a description for your key, and select duration. Then click **Save**, and copy the _password_ to a secure location.\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id =\"get-tenant-id\"></a>\n",
|
||||
"\n",
|
||||
"Also, you need to obtain the tenant ID of your Azure subscription. Go back to **Azure Active Directory**, select **Properties** and copy _Directory ID_.\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, you need to give the service principal permissions to access your workspace. Navigate to **Resource Groups**, to the resource group for your Machine Learning Workspace. \n",
|
||||
"\n",
|
||||
"Then select **Access Control (IAM)** and **Add a role assignment**. For _Role_, specify which level of access you need to grant, for example _Contributor_. Start entering your service principal name and once it is found, select it, and click **Save**.\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now you are ready to use the service principal authentication. For example, to connect to your Workspace, see code below and enter your own values for tenant ID, application ID, subscription ID, resource group and workspace.\n",
|
||||
"\n",
|
||||
"**We strongly recommended that you do not insert the secret password to code**. Instead, you can use environment variables to pass it to your code, for example through Azure Key Vault, or through secret build variables in Azure DevOps. For local testing, you can for example use following PowerShell command to set the environment variable.\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"$env:AZUREML_PASSWORD = \"my-password\"\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from azureml.core.authentication import ServicePrincipalAuthentication\n",
|
||||
"\n",
|
||||
"svc_pr_password = os.environ.get(\"AZUREML_PASSWORD\")\n",
|
||||
"\n",
|
||||
"svc_pr = ServicePrincipalAuthentication(\n",
|
||||
" tenant_id=\"my-tenant-id\",\n",
|
||||
" service_principal_id=\"my-application-id\",\n",
|
||||
" service_principal_password=svc_pr_password)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"ws = Workspace(\n",
|
||||
" subscription_id=\"my-subscription-id\",\n",
|
||||
" resource_group=\"my-ml-rg\",\n",
|
||||
" workspace_name=\"my-ml-workspace\",\n",
|
||||
" auth=svc_pr\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "roastala"
|
||||
}
|
||||
],
|
||||
"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": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
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|
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@@ -227,7 +227,7 @@
|
||||
" private_key_file='./.ssh/id_rsa')\n",
|
||||
" attached_dsvm_compute = ComputeTarget.attach(workspace=ws,\n",
|
||||
" name=compute_target_name,\n",
|
||||
" attach_config=attach_config)\n",
|
||||
" attach_configuration=attach_config)\n",
|
||||
" attached_dsvm_compute.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user