> For the complete documentation index, see [llms.txt](https://workshops.henkboelman.com/developers-guide-to-azure-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://workshops.henkboelman.com/developers-guide-to-azure-ai/azure-machine-learning/lab-1.md).

# 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 <resource-group-name> --location <location>
```

### Create the workspace

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>
```

> You can now view your workspace by visiting <https://ml.azure.com>

![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 <workspace-name> -g <resource-group-name>
```

> View your created Azure Machine Learning Compute cluster on <https://ml.azure.com>
>
> *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 <name> --vm-size Standard_D2_V2 -w <workspace-name> -g <resource-group-name>
```

> View your created Azure Machine Learning Compute cluster on <https://ml.azure.com>
>
> *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.&#x20;
* 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)