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01-setup_azure_machine_learning.md

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Setting Up Azure Machine Learning

Prerequisites

To run through this workshop, you will need an Azure subscription and an Azure ML workspace. See instructions on how to create a workspace here.

Creating an AzureML compute cluster

We will do a number of actions that require a compute target to be exectuted on. We will start by creating a cluster of CPU VMs.

  1. Navigate to 'Compute' > 'Training Clusters' in the "Manage" section and click 'New'.

  2. Call the cluster 'cluster'.

    • For machine size choose 'Standard_D2_v2' (that is an inexpensive general purpose VM size at about $0.14/hour).
    • Set the 'minimum number of nodes' to 0 and the the 'maximum number of nodes' to 10. That way the cluster will scale automatically to up to 10 nodes as jobs require them.
    • Set the 'Idle seconds before scale down' to 10800. That means that nodes will be kept around for 3 hours before they are spun down. That way, during our workshop, jobs won't have to wait for spin-up. Make sure that number is lower if you are using a more expensive VM size.

Creating an AzureML Compute Instance

Next, we will create a Compute Instance. The Compute Instance will serve as an interactive workstation in the cloud that serves as a Jupyter server, but also hosts and instance of RStudio server and can run TensorBoard, Bokeh, Shiny or other apps used during the developement work of a data scientist.

  1. Navigate to 'Compute Instances' tab in Compute and click on 'New'.

  2. Choose some sufficiently unique name, keep the default VM type (STANDARD_DS3V2 -- a fairly inexpensive machine type costing about $0.27/hour) and click 'Create':

See here for details on creating AzureML Compute Instances.

Note that this machine will keep running until you stop it from the portal.

Setting Up Azure ML on Your Local Machine

If you want to use your local machine instead of a Compute Instance, please follow the steps you can find here.