mirror of
https://github.com/Azure/MachineLearningNotebooks.git
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710 lines
23 KiB
Plaintext
710 lines
23 KiB
Plaintext
{
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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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""
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"# Quickstart: Learn how to get started with Azure ML Job Submission\n",
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"\n",
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"In this quickstart, you train a machine learning model by submitting a Job to a compute target. \n",
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"When training, it is common to start on your local computer, and then later scale out to a cloud-based cluster. \n",
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"\n",
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"All you need to do is define the environment for each compute target within a script run configuration. Then, when you want to run your training experiment on a different compute target, specify the run configuration for that compute.\n",
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"\n",
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"This quickstart trains a simple logistic regression using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
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"\n",
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"You will learn how to:\n",
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"\n",
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"> * Download a dataset and look at the data\n",
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"> * Train an image classification model by submitting a batch job to a compute resource\n",
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"> * Review training results, find and register the best model"
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Connect to your workspace and create an experiment\n",
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"\n",
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"You start with importing some libraries and creating an experiment to track the runs in your workspace. A workspace can have multiple experiments, and all the users that have access to the workspace can collaborate on them. "
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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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"gather": {
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"logged": 1612965838618
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import azureml.core\n",
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"from azureml.core import Workspace\n",
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"from azureml.core import Experiment\n",
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"\n",
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"# connect to your workspace\n",
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"ws = Workspace.from_config()\n",
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"\n",
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"experiment_name = \"get-started-with-jobsubmission-tutorial\"\n",
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"exp = Experiment(workspace=ws, name=experiment_name)"
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"## Import Data\n",
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"\n",
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"Before you train a model, you need to understand the data that you are using to train it. In this section you will:\n",
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"\n",
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"* Download the MNIST dataset\n",
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"* Display some sample images\n",
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"\n",
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"### Download the MNIST dataset\n",
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"\n",
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"Use Azure Open Datasets to get the raw MNIST data files. [Azure Open Datasets](https://docs.microsoft.com/azure/open-datasets/overview-what-are-open-datasets) are curated public datasets that you can use to add scenario-specific features to machine learning solutions for more accurate models. Each dataset has a corresponding class, `MNIST` in this case, to retrieve the data in different ways.\n",
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"\n",
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"Follow this [how-to](https://aka.ms/azureml/howto/createdatasets) if you want to learn more about Datasets and how to use them.\n"
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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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"gather": {
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"logged": 1612965850391
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from azureml.core import Dataset\n",
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"from azureml.opendatasets import MNIST\n",
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"\n",
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"data_folder = os.path.join(os.getcwd(), \"data\")\n",
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"os.makedirs(data_folder, exist_ok=True)\n",
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"\n",
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"mnist_file_dataset = MNIST.get_file_dataset()\n",
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"mnist_file_dataset.download(data_folder, overwrite=True)\n",
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"\n",
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"mnist_file_dataset = mnist_file_dataset.register(\n",
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" workspace=ws,\n",
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" name=\"mnist_opendataset\",\n",
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" description=\"training and test dataset\",\n",
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" create_new_version=True,\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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Take a look at the data\n",
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"You will load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. Note this step requires a `load_data` function that's included in an `utils.py` file. This file is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays. \n"
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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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"gather": {
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"logged": 1612965857960
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"# make sure utils.py is in the same directory as this code\n",
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"from utils import load_data\n",
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"import glob\n",
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"\n",
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"\n",
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"# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n",
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"X_train = (\n",
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" load_data(\n",
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" glob.glob(\n",
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" os.path.join(data_folder, \"**/train-images-idx3-ubyte.gz\"), recursive=True\n",
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" )[0],\n",
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" False,\n",
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" )\n",
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" / 255.0\n",
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")\n",
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"X_test = (\n",
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" load_data(\n",
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" glob.glob(\n",
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" os.path.join(data_folder, \"**/t10k-images-idx3-ubyte.gz\"), recursive=True\n",
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" )[0],\n",
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" False,\n",
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" )\n",
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" / 255.0\n",
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")\n",
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"y_train = load_data(\n",
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" glob.glob(\n",
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" os.path.join(data_folder, \"**/train-labels-idx1-ubyte.gz\"), recursive=True\n",
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" )[0],\n",
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" True,\n",
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").reshape(-1)\n",
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"y_test = load_data(\n",
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" glob.glob(\n",
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" os.path.join(data_folder, \"**/t10k-labels-idx1-ubyte.gz\"), recursive=True\n",
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" )[0],\n",
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" True,\n",
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").reshape(-1)\n",
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"\n",
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"\n",
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"# now let's show some randomly chosen images from the training set.\n",
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"count = 0\n",
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"sample_size = 30\n",
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"plt.figure(figsize=(16, 6))\n",
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"for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
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" count = count + 1\n",
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" plt.subplot(1, sample_size, count)\n",
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" plt.axhline(\"\")\n",
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" plt.axvline(\"\")\n",
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" plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n",
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" plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n",
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"plt.show()"
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"## Submit your training job\n",
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"\n",
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"In this quickstart you submit a job to run on the local compute, but you can use the same code to submit this training job to other compute targets. With Azure Machine Learning, you can run your script on various compute targets without having to change your training script. \n",
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"\n",
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"To submit a job you need:\n",
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"* A directory\n",
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"* A training script\n",
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"* Create a script run configuration\n",
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"* Submit the job \n",
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"\n",
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"\n",
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"### Directory and training script \n",
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"\n",
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"You need a directory to deliver the necessary code from your computer to the remote resource. A directory with a training script has been created for you and can be found in the same folder as this notebook.\n",
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"\n",
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"Take a few minutes to examine the training script."
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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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"gather": {
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"logged": 1612965865707
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"with open(\"sklearn-mnist-batch/train.py\", \"r\") as f:\n",
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" print(f.read())"
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"Notice how the script gets data and saves models:\n",
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"\n",
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"+ The training script reads an argument to find the directory containing the data. When you submit the job later, you point to the dataset for this argument:\n",
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"`parser.add_argument('--data-folder', type=str, dest='data_folder', help='data directory mounting point')`\n",
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"\n",
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"\n",
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"+ The training script saves your model into a directory named outputs. <br/>\n",
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"`joblib.dump(value=clf, filename='outputs/sklearn_mnist_model.pkl')`<br/>\n",
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"Anything written in this directory is automatically uploaded into your workspace. You'll access your model from this directory later in the tutorial.\n",
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"\n",
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"The file `utils.py` is referenced from the training script to load the dataset correctly. This script is also copied into the script folder so that it can be accessed along with the training script."
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Configure the training job\n",
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"\n",
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"Create a [ScriptRunConfig]() object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Configure the ScriptRunConfig by specifying:\n",
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"\n",
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"* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
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"* The compute target. In this case you will point to local compute\n",
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"* The training script name, train.py\n",
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"* An environment that contains the libraries needed to run the script\n",
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"* Arguments required from the training script. \n",
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"\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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"An Environment defines Python packages, environment variables, and Docker settings that are used in machine learning experiments. Here you will be using a curated environment that has already been made available through the workspace. \n",
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"\n",
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"Read [this article](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments) if you want to learn more about Environments and how to use them."
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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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"gather": {
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"logged": 1612965877458
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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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.environment import Environment\n",
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"from azureml.core.conda_dependencies import CondaDependencies\n",
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"\n",
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"# use a curated environment that has already been built for you\n",
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"\n",
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"env = Environment.get(workspace=ws, name=\"AzureML-Scikit-learn-0.20.3\")"
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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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"Create a [ScriptRunConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.scriptrunconfig?preserve-view=true&view=azure-ml-py) object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. A script run configuration is used to configure the information necessary for submitting a training run as part of an experiment. \n",
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"\n",
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"Read more about configuring and submitting training runs [here](https://docs.microsoft.com/azure/machine-learning/how-to-set-up-training-targets). "
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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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"gather": {
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"logged": 1612965882781
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
|
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"nteract": {
|
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"transient": {
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"deleting": false
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}
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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 ScriptRunConfig\n",
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"\n",
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"args = [\"--data-folder\", mnist_file_dataset.as_mount(), \"--regularization\", 0.5]\n",
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"\n",
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"script_folder = \"sklearn-mnist-batch\"\n",
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"src = ScriptRunConfig(\n",
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" source_directory=script_folder,\n",
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" script=\"train.py\",\n",
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" arguments=args,\n",
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" compute_target=\"local\",\n",
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" environment=env,\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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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Submit the job\n",
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"\n",
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"Run the experiment by submitting the ScriptRunConfig object. After this there are many options for monitoring your run. You can either navigate to the experiment \"get-started-with-jobsubmission-tutorial\" in the left menu item Experiments to monitor the run (quick link to the run details page in the cell output below), or you can monitor the run inline in this notebook by using the Jupyter widget activated below."
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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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"gather": {
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"logged": 1612965911435
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},
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
|
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"nteract": {
|
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"transient": {
|
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"run = exp.submit(config=src)\n",
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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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"nteract": {
|
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Jupyter widget\n",
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"\n",
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"Watch the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes.\n",
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"\n"
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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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"gather": {
|
|
"logged": 1612966026710
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},
|
|
"jupyter": {
|
|
"outputs_hidden": false,
|
|
"source_hidden": false
|
|
},
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"from azureml.widgets import RunDetails\n",
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"\n",
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"RunDetails(run).show()"
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]
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},
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{
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"cell_type": "markdown",
|
|
"metadata": {
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
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"source": [
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"if you want to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
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]
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},
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{
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"cell_type": "markdown",
|
|
"metadata": {
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"source": [
|
|
"### Get log results upon completion\n",
|
|
"\n",
|
|
"Model training happens in the background. You can use `wait_for_completion` to block and wait until the model has completed training before running more code. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"gather": {
|
|
"logged": 1612966045110
|
|
},
|
|
"jupyter": {
|
|
"outputs_hidden": false,
|
|
"source_hidden": false
|
|
},
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# specify show_output to True for a verbose log\n",
|
|
"run.wait_for_completion(show_output=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"source": [
|
|
"### Display run results\n",
|
|
"\n",
|
|
"You now have a trained model. Retrieve all the metrics logged during the run, including the accuracy of the model:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"gather": {
|
|
"logged": 1612966059052
|
|
},
|
|
"jupyter": {
|
|
"outputs_hidden": false,
|
|
"source_hidden": false
|
|
},
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(run.get_metrics())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"source": [
|
|
"## Register model\n",
|
|
"\n",
|
|
"The last step in the training script wrote the file `outputs/sklearn_mnist_model.pkl` in a directory named `outputs` on the compute where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"gather": {
|
|
"logged": 1612966064041
|
|
},
|
|
"jupyter": {
|
|
"outputs_hidden": false,
|
|
"source_hidden": false
|
|
},
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(run.get_file_names())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"source": [
|
|
"Register the model in the workspace so that you (or your team members with access to the workspace) can later query, examine, and deploy this model."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"gather": {
|
|
"logged": 1612966068862
|
|
},
|
|
"jupyter": {
|
|
"outputs_hidden": false,
|
|
"source_hidden": false
|
|
},
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# register model\n",
|
|
"model = run.register_model(\n",
|
|
" model_name=\"sklearn_mnist\", model_path=\"outputs/sklearn_mnist_model.pkl\"\n",
|
|
")\n",
|
|
"print(model.name, model.id, model.version, sep=\"\\t\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"nteract": {
|
|
"transient": {
|
|
"deleting": false
|
|
}
|
|
}
|
|
},
|
|
"source": [
|
|
"## Control Cost\n",
|
|
"\n",
|
|
"If you want to control cost you can stop the compute instance this notebook is running on by clicking the \"Stop compute\" button next to the status dropdown in the menu above.\n",
|
|
"\n",
|
|
" ## Next Steps\n",
|
|
"\n",
|
|
"In this quickstart, you have seen how to run jobs-based machine learning code in Azure Machine Learning. \n",
|
|
"\n",
|
|
"It is also possible to use automated machine learning in Azure Machine Learning service to find the best model in an automated fashion. To see how this works, we recommend that you follow the next quickstart in this series, [**Fraud Classification using Automated ML**](ClassificationWithAutomatedML.ipynb). This quickstart is focused on AutoML using the Python SDK."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"authors": [
|
|
{
|
|
"name": "cewidste"
|
|
}
|
|
],
|
|
"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": "3.6.9"
|
|
},
|
|
"notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
|
|
"nteract": {
|
|
"version": "nteract-front-end@1.0.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
} |