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HOL-Azure Machine Learning service.ipynb
HOL-Azure Machine Learning service.ipynb
 
 
README.md
README.md
 
 

README.md

Day#2 – AI & ML

HOL02: Azure Machine Learning service

  • create workspace (5min)
  • create ntb (5min)
  • part1 - data (15min)
  • part2 - train locally (15min)
  • part3 - train on remote automl (35min)
  • part4 - deployment (25min)

PREREQUISITIES

Create a workspace

A workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. It ties your Azure subscription and resource group to an easily consumed object in the SDK. If you already have an Azure Machine Learning service workspace, skip to the next section. Otherwise, create one now.

  1. Sign in to Azure portal by using the credentials for your Azure subscription.

  2. In the upper-left corner of Azure portal, select + Create a resource.

    Create a new resource

  3. Use the search bar to find Machine Learning.

  4. Select Machine Learning.

  5. In the Machine Learning pane, select Create to begin.

  6. Provide the following information to configure your new workspace:

    Field Description
    Workspace name Enter a unique name that identifies your workspace. In this example, we use docs-ws. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others.
    Subscription Select the Azure subscription that you want to use.
    Resource group Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use docs-aml.
    Location Select the location closest to your users and the data resources to create your workspace.
    Workspace edition Select Basic as the workspace type for this tutorial. The workspace type (Basic & Enterprise) determines the features to which you’ll have access and pricing. Everything in this tutorial can be performed with either a Basic or Enterprise workspace.

  7. After you are finished configuring the workspace, select Review + Create.

    [!Warning] It can take several minutes to create your workspace in the cloud.

    When the process is finished, a deployment success message appears.

  8. To view the new workspace, select Go to resource.

Create a cloud notebook server - access Jupyter via Compute Instance

Go to AML Studio and sign in and locate your newly created workspace

In your workspace in Azure Machine Learning studio, create a new compute instance from either the Compute section or in the Notebooks section when you are ready to run one of your notebooks.

Create a new resource

Hit next.

Create a new resource

Field Description
Compute name
  • Name is required and must be between 3 to 24 characters long.
  • Valid characters are upper and lower case letters, digits, and the - character.
  • Name must start with a letter
  • Name needs to be unique across all existing computes within an Azure region. You will see an alert if the name you choose is not unique
  • If - character is used, then it needs to be followed by at least one letter later in the name
    		•
    Virtual machine type Choose CPU or GPU. This type cannot be changed after creation
    Virtual machine size Supported virtual machine sizes might be restricted in your region. Check the availability list
    Enable/disable SSH access SSH access is disabled by default. SSH access cannot be. changed after creation. Make sure to enable access if you plan to debug interactively with VS Code Remote
    Advanced settings Optional. Configure a virtual network. Specify the Resource group, Virtual network, and Subnet to create the compute instance inside an Azure Virtual Network (vnet). For more information, see these network requirements for vnet .

    You can also create an instance

    The dedicated cores per region per VM family quota and total regional quota, which applies to compute instance creation. is unified and shared with Azure Machine Learning training compute cluster quota. Stopping the compute instance does not release quota to ensure you will be able to restart the compute instance.

    Launch Jupyter web interface

    After you created Compute Instance simply access the Jupyter from Compute page of your AML Studio.

    Create a new resource

    In next part of the lab you download a git repo.

    Download the GitHub repo with the Lab contents

    1. Navigate to you folder in Jupyter (it is your username) and select New->Terminal open_terminal

    2. Clone the GitHub repository for this lab in the terminal

    cd *<your_username>*

    git clone https://github.com/michalmar/azure-labs.git

    clone_repo

    After that you should see something similar: cloned_repo

    1. Go to lab notebook: HOL-Azure Machine Learning service.ipynb in HOL02-AML folder in the cloned repo.