# Lab 1 - Environment Setup ## Create a Azure Machine Learning Workspace To get started we need to setup a few resources in Azure. For this we are going to use the Azure CLI. If you don’t have the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/?WT.mc_id=aiapril-blog-heboelma\&view=gaic-github-latest) installed on your machine you can follow the [tutorial on MS Docs](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?WT.mc_id=gaic-github-heboelma\&view=azure-cli-latest) here. ### Install the Azure Machine Learning CLI extension To install the machine learning extension, use the following command: ``` az extension add -n azure-cli-ml ``` ### Create a resource group 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 ``` az group create --name --location ``` ### Create the workspace To create a new workspace where the services are automatically created, use the following command: ``` az ml workspace create -w -g ``` > You can now view your workspace by visiting ![Azure Machine Learning studio](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F73f6fb6e259d619e7f5abcb858215210f4a96fdf.png?generation=1597759372555732\&alt=media) ### Create a Compute Cluster 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 computetarget create amlcompute -n gpu-cluster --min-nodes 1 --max-nodes 1 --vm-size STANDARD_NC6 -w -g ``` > View your created Azure Machine Learning Compute cluster on > > *Creating compute can take a few minutes to complete* ![Create Azure Machine Learning Compute](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F4f026a8a3d50afbd06e215add1bf68830b0daa2b.png?generation=1597759372809521\&alt=media) ### Create a Compute instance To train our model we are going to use a notebook. To run a notebook in Azure Machine Learning studio we need to create a Compute Instance. *Choose a unique name* ``` az ml computetarget create computeinstance -n --vm-size Standard_D2_V2 -w -g ``` > View your created Azure Machine Learning Compute cluster on > > *Creating compute can take a few minutes to complete* ![Create Azure Machine Learning Compute Instance](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F01797e1d3b38c4f97754f8ed5e77058da7d47a6d.png?generation=1597759373131966\&alt=media) ### Create a Notebook * Navigate to the Notebook section in Azure Machine Learning Workspace. * Create a new file with name 'simpsons' and File type 'notebook' ![Create new folder](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F90321df9948ad93cc2b341dd5e906ec0b969dec8.png?generation=1597759372658288\&alt=media) ### Setup completed If everything went correctly you should be looking at a screen that looks like the one below and see that your notebook is running on your created Compute Instance. ![Create new folder](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Fd2b7a02ddbfae2aea040ed4819300aed226aef70.png?generation=1597759372720246\&alt=media) [**Continue with lab 2 >**](https://workshops.henkboelman.com/developers-guide-to-azure-ai/azure-machine-learning/lab-2)