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# from Azure CLI

# login to Azure
az login

# get account list
az account list

# set subscription
az account set --subscription [subscription ID]

# set azure resource group
set AZURE_RESOURCE_GROUP=[resource group name]

# set storage account
set STORAGE_ACCOUNT_NAME=[storage account name]

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# example from <https://docs.microsoft.com/en-us/azure/machine-learning/preview/how-to-create-dsvm-hdi>

# first make sure you have a valid Azure authentication token
$ az account get-access-token

# if you don't have a valid token, please log in to Azure first. 
# if you already do, you can skip this step.
$ az login

# list all subscriptions you have access to
$ az account list -o table

# make sure you set the subscription you want to use to create DSVM as the current subscription
$ az account set -s <subscription name or Id>

# it is always a good idea to create a resource group for the VM and associated resources to live in.
# you can use any Azure region, but it is best to create them in the region where your Azure ML Experimentation account is, e.g. eastus2, westcentralus or australiaeast.
# also, only certain Azure regions has GPU-equipped VMs available.
$ az group create -n <resource group name> -l <azure region>

# now let's create the DSVM based on the JSON configuration file you created earlier.
# note we assume the mydsvm.json config file is placed in the "docs" sub-folder.
$ az group deployment create -g <resource group name> --template-uri https://raw.githubusercontent.com/Azure/DataScienceVM/master/Scripts/CreateDSVM/Ubuntu/azuredeploy.json --parameters @docs/mydsvm.json

# find the FQDN (fully qualified domain name) of the VM just created. 
# you can also use IP address from the next command if FQDN is not set.
$ az vm show -g <resource group name> -n <vm name> --query "fqdns"

# find the IP address of the VM just created
$ az vm show -g <resource group name> -n <vm name> --query "publicIps"