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EnvironmentSetup.md

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Environment Setup

1.0 Creating an AzureML Compute Instance

To start with, we will create a Azure ML Compute Instance. The Compute Instance is an Azure VM and will serve as an interactive workstation in the cloud that serves as a Jupyter server.

  1. Open Azure Machine Learning Studio.
  2. Navigate to 'Compute Instances' tab in Compute and click on 'New'.
  3. 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.

2.0 Clone git Repository to Workspace storage

To clone this git repository onto the workspace, follow the steps below:

  1. To get started, first navigate to the JupyterLab instance running on the Compute Instance by clicking on the JupyterLab link shown below:

  2. After going through authentication, you will see the JupyterLab frontend. As you authenticate, make sure to use the same user to log in as was used to create the Compute Instance, or else your access will be denied. Next open an Terminal (either by File/New/Terminal, or by just clicking on Terminal in the Launcher Window).

  3. In the terminal window clone this repository by typing:

       git clone https://github.com/microsoft/solution-accelerator-many-models.git ./manymodels
  1. You will be prompted to provide your github username and for your password you will need to provide a personal access token. Please follow the steps here to create a personal access token.