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Welcome to the Deep Learning toolkit for the Microsoft Data Science Virtual Machine

Deploy to Azure

The data science virtual machine on Azure, based on Windows Server 2012, contains popular tools for data science modeling and development activities. Some tools include Microsoft R Server Developer Edition, Anaconda Python, Jupyter notebooks for Python and R, Visual Studio Community Edition with Python and R Tools, Power BI desktop, and SQL Server Express edition. Jump start modeling and development for your data science project using software commonly used for analytics and machine learning tasks in a variety of languages including R, Python, SQL, and C#.

This deep learning toolkit provides GPU versions of mxnet and CNTK for use on Azure GPU N-series instances. These GPUs use discrete device assignment, resulting in performance that is close to bare-metal, and are well-suited to deep learning problems that require large training sets and expensive computational training efforts. The deep learning toolkit also provides a set of sample deep learning solutions that use the GPU, including image classification on the CIFAR database and a word prediction sample from character inputs.

You can click on the "Deploy to Azure" button to immediately try out the DSVM with this extension installed. Hardware compute fees apply.

IMPORTANT NOTE: Before you proceed to use the Deploy to Azure button you must perform a one-time task to accept the terms of the data science virtual machine on your Azure subscription. You can do this by visiting Configure Programmatic Deployment. You must also accept the license terms in this repository.

After provisioning the data science virtual machine with the deep learning toolkit, see the README file in C:\dsvm\deep-learning, or on the desktop, for more information.

For more information on provisioning and using the Data Science VM, please check out the documentation page. You can also find a How-To Guide to the data science VM that demonstrates some of the things you can do on the VM.