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Lab 1 - AML Workspace Setup
To get started we need to setup a few resources in Azure. Please follow this guide to setup your dev environment.
The Azure Machine Learning workspace must be created inside a resource group. You can use an existing resource group or create a new one. To create a new resource group, use the following command. Replace with the name to use for this resource group. Replace with the Azure region to use for this resource group:
Example name and location:
- resource group name: pytorchworkshop
- location: WestEurope or eastus
Location: Choose eastus or WestEurope, this is needed for the compute we are using later.
az group create --name <resource-group-name> --location <location>
To create a new workspace where the services are automatically created, use the following command:
az ml workspace create -w <workspace-name> -g <resource-group-name>
If the az ml command does not work run: az extension add -n ml -y
After every az ml command you have to type "-w <workspace-name> -g <resource-group-name>". You can make everything a bit easier by settings the values for this parameters by default.
az configure --defaults workspace=<workspace-name> group=<resource-group-name>
To train our model we need an Azure Machine Learning Compute cluster. To create a new compute cluster, use the following command.
This command will create an Azure Machine Learning Compute cluster with 1 node that is always on and is using STANDARD_NC6 virtual Machines.
To speed up the training process you can use a GPU enabled NC6 machine
az ml compute create --type amlcompute -n gpu-cluster --min-instances 0 --max-instances 1 --size STANDARD_NC6
Creating compute can take a few minutes to complete
To see the list of created compute in your workspace you can type:
az ml compute list --output table
The setup of your workspace with compute is now completed
- Created a resource group
- Created an Azure Machine Learning Workspace
- Created Azure Machine Learning Compute Cluster