# Getting Started With AzureML Notebook VMs
(Notice this post is out of date please check out Azure Machine Learning Compute Instance’s a new post describing how to get started is coming soon!)
TLDR; Azure Machine Learning (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.
# Step 2: Create Azure Machine Learning Workspace
Follow the instructions in the gif above to create a new azure machine learning service instance. More information can be found below.
# Step 3: Navigate to Azure ML Compute
# Step 4: Click New Compute
# Step 5: Choose the VM Size and Deploy
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.
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
# Bonus Best Practice: Shutdown VM when not in use delete VM when using standard dependencies.
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.
Also check out setting up Remote Debugging on your new Azure Notebook VM with Visual Studio Code.
[danielsc/azureml-debug-training](https://github.com/danielsc/azureml-debug-training/blob/master/Setting up VSCode Remote on an AzureML Notebook VM.md)
# About the Author
Aaron (Ari) Bornstein 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.