# 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](/files/-MF0sI2T59vgzYHlF8iw) ### 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](/files/-MF0sI2UKz2gdKABm0bF) ### 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](/files/-MF0sI2VIr3AfUb7ogxo) ### 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](/files/-MF0sI2W4DteMoH0XTQX) ### 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](/files/-MF0sI2XYQ67keYjv57m) [**Continue with lab 2 >**](/developers-guide-to-azure-ai/azure-machine-learning/lab-2.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://workshops.henkboelman.com/developers-guide-to-azure-ai/azure-machine-learning/lab-1.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.