{"id":1713,"date":"2019-02-05T00:00:00","date_gmt":"2019-02-05T00:00:00","guid":{"rendered":""},"modified":"2025-06-12T14:05:12","modified_gmt":"2025-06-12T21:05:12","slug":"fastai-on-azure-dsvm","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/fastai-on-azure-dsvm\/","title":{"rendered":"Build your own deep learning models on Azure Data Science Virtual Machines"},"content":{"rendered":"\n

As a modern developer, you may be eager to build your own deep learning models but aren\u2019t quite sure where to start. If this is you, I recommend you take a look at the deep learning course from fast.ai<\/a>. This new fast.ai course<\/a> helps software developers start building their own state-of-the-art deep learning models. Developers who complete this fast.ai course will become proficient in deep learning techniques in multiple domains including computer vision, natural language processing, recommender algorithms, and tabular data.<\/p>\n\n\n\n

\"Fast.ai<\/figure>\n\n\n\n

You\u2019ll also want to learn about Microsoft\u2019s Azure Data Science Virtual Machine (DSVM)<\/a>. Azure DSVM empowers developers like you with the tools you need to be productive with this fast.ai course today on Azure, with virtually no setup required. Using fast cloud-based GPU virtual machines (VMs), at the most competitive rates, Azure DSVM saves you time that would otherwise be spent in installation, configuration, and waiting for deep learning models to train.<\/p>\n\n\n\n

Here is how you can effectively run the fast.ai course examples on Azure.<\/p>\n\n\n\n

Running the fast.ai deep learning course on Azure DSVM<\/h2>\n\n\n\n

While there are several ways in which you can use Azure for your deep learning course, one of the easiest ways is to leverage Azure Data Science Virtual Machine (DSVM)<\/a>. Azure DSVM is a family of virtual machine (VM) images that are pre-configured with a rich curated set of tools and frameworks for data science, deep learning, and machine learning.<\/p>\n\n\n\n

Using Azure DSVM, you can utilize tools like Jupyter notebooks and necessary drivers to run on powerful GPUs. In result saving time that would otherwise be spent installing, configuring, and troubleshooting any compatibility issues on your system. Azure DSVM is offered on both Linux and Windows editions. Azure VMs provides a neat extension mechanism that the DSVM can leverage, allowing you to automatically configure your VM to your needs.<\/p>\n\n\n\n

Microsoft provides an extension to the DSVM specifically for the fast.ai course, making the process so simple you can answer a couple of questions and get your own instance of DSVM provisioned in a few minutes. The fast.ai extension<\/a> installs all the necessary libraries you need to run the course Jupyter notebooks and also pull down the latest course notebooks from the fast.ai GitHub repository. So in a very short time, you\u2019ll be ready to start running your course samples.<\/p>\n\n\n\n

Getting started with Azure DSVM and fast.ai<\/h2>\n\n\n\n

Here\u2019s how simple it is to get started:<\/h3>\n\n\n\n

1. Sign in or sign up for an Azure subscription<\/h3>\n\n\n\n

If you don\u2019t have an Azure subscription you can start off with a free trial subscription<\/a> to explore any Azure service for 30 days and access to a set of popular services free for 12 months. Please note that free trial subscriptions do not give access to GPU resources. For GPU access, you need to sign up for an Azure pay-as-you-go subscription<\/a> or use the Azure credits from the Visual Studio subscriptions if you have one. Once you have created your subscription, you can login to the Azure portal.<\/p>\n\n\n\n

2. Create a DSVM instance with fast.ai extension<\/h3>\n\n\n\n

You can now create a DSVM with the fast.ai extension by selecting one of the links below. Choose one depending on whether you prefer a Windows or a Linux environment for your course.<\/p>\n\n\n\n