Getting Started With AzureML Notebook VMs
Tags: machinelearning, python, azure, ai
Published: 2019-12-24 08:58:02

---
title: Getting Started With AzureML Notebook VMs
published: true
date: 2019-12-24 08:42:34 UTC
tags: machinelearning,python,azure,ai
canonical_url: https://medium.com/microsoftazure/getting-started-with-azureml-notebook-vms-f637b6e09ed4
---

![](https://notebooks.azure.com/images/landing/hero-azure-notebooks.svg)

TLDR; [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml?WT.mc_id=blog-medium-abornst) (AML), a cloud-based environment you can use to train, deploy, automate, manage, and track ML models. In the following tutorial we will walk through how to set up an Azure Notebook VM.

### What are the benefits of AzureML Notebook VMs?

Azure ML Notebook VMs are cloud-based environments preloaded with everything you need to get started with ML and data science in Azure.

AML Notebook VMs are secure and easy-to-use, fully customizable and directly integrated into Azure Machine Learning Service, providing a code-first experience for data scientists to build and deploy models using AzureML

Azure ML Notebook VM Features:

- **Secure**  — Azure Active Directory login integrated with the AML Workspace, provides access to files stored in the workspace, implicitly configured for the workspace.
- **Scalable** — created with a few clicks in the AML workspace portal, managed from within the AML workspace portal. Since notebooks are managed by the AML Service compute can be scaled as needed.
- **Pre-Configured** — up-to-date AML Python Environment, GPU drivers, Pytorch, Tensorflow, Scikit learn, R etc.
- **Customizable ** — ssh to the machine, install your own tools (or drivers), changes persist when machines are shut down or restarted.

### Step 1: Login to Azure

If you don’t have an Azure Subscription you can get a free account using the link below.

[Create your Azure free account today | Microsoft Azure](https://azure.microsoft.com/en-us/free/?WT.mc_id=blog-medium-abornst)

### Step 2: Create Azure Machine Learning Workspace

![](https://cdn-images-1.medium.com/max/1024/0*N09HG5K133NUC45H.gif)

Follow the instructions in the gif above to create a new azure machine learning service instance. More information can be found below.

[Tutorial: Create your first ML experiment - Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup?WT.mc_id=blog-medium-abornst)

### Step 3: Navigate to Azure ML Compute

![](https://cdn-images-1.medium.com/max/1024/1*r9E_9IOr95ZxpA_2nOzC_g.png)

### Step 4: Click New Compute

![](https://cdn-images-1.medium.com/max/1024/1*GJom1HUXnuK803DjzrGRtw.png)

### Step 5: Choose the VM Size and Deploy

![](https://cdn-images-1.medium.com/max/1024/1*pzTToNLGuG9WRii4Q60X4g.png)

A list of VM Sizes and pricing can be found in the documentation below. The standard series is recommended for most projects and the N series are recommended for projects requiring GPU.

[Virtual Machine series | Microsoft Azure](https://azure.microsoft.com/en-us/pricing/details/virtual-machines/series/?WT.mc_id=blog-medium-abornst)

When you are done click create it should take about 5–10 mins to set up the new VM depending on the specified configuration.

### Step 6: Jupyter, JupyterLab or Open R Studio and Get Started Coding

![](https://cdn-images-1.medium.com/max/1024/1*5FXOhPFpHBdHhqCh5SbvGw.png)

![](https://cdn-images-1.medium.com/max/1024/1*W2t49hUltJddA_Mpyop9Tg.png)

### Bonus Best Practice: Shutdown VM when not in use delete VM when using standard dependencies.

![](https://cdn-images-1.medium.com/max/1024/1*u-ap5ZImFnzj2kmpg3w9GA.png)

Since all notebooks are persisted in the notebooks section of the Azure ML Service unlike a DSVM your work can be recovered and shared across multiple notebook VMs. So it is seamless to start with a standard VM for basic data processing and later switch to a N Series VM if GPU Compute is needed.

### Next Steps

Now that you have set up your first Notebook VM check out my previous post on 9 Advanced Tips for Production machine learning.

[9 Advanced Tips for Production Machine Learning](https://dev.to/azure/9-advanced-tips-for-production-machine-learning-4ccg)

### About the Author

[**Aaron (Ari) Bornstein**](https://www.linkedin.com/in/aaron-ari-bornstein-22aa7a77/) is an avid AI enthusiast with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.

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