Understanding your GPU Performance on Azure with GPU Monitor

So I get lots of questions from Academics. Many are now around performance and optimisation of cloud services. Or simply understanding what students are doing with the resources. Many are specifically around the measurement and management of Azure GPS being used in the teaching of DNN, ML and AIhttps://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpuThe most common is ‘what’s the best…


Running YOLO v2 on the Microsoft Data Science Virtual Machine

This week I attended the Industry Partner workshop at the Future of Infrastructure and Built Environment at the University of Cambridge. During the day I had a number of conversations with construction industry professionals and one of  the topics we got into talking about how technology from HoloLens, to Data Science is revolutionising the construction…


NVidia P100 and V100 coming to Azure

Corey Sanders announced this week for Super Computing Conference that Microsoft are launching a new VM size on Azure, the NCv3. This new size will offer the new NVIDIA Tesla V100 GPU. You can sign up for the preview today. NVIDIA® Tesla® V100 is the world’s most advanced data center GPU ever built to accelerate…


Microsoft Deep Learning Virtual Machine

The DLVM is a specially configured variant of the Data Science VM DSVM that is custom made to help users jump start deep learning on Azure GPU VMs. The DLVM uses the same underlying VM images of the DSVM and hence comes with the same set of data science tools and deep learning frameworks as…


Neural networks and deep learning with Microsoft Azure GPU

Guest blog by Yannis Assael from Oxford University  The rise of neural networks and deep learning is correlated with increased computational power introduced by general purpose GPUs. The reason is that the optimisation problems being solved to train a complex statistical model, are demanding and the computational resources available are crucial to the final solution….


Azure GPU Tensorflow Step-by-Step Setup

The following guide has been developed in collaboration with my colleague at Microsoft Christine Matheney and our work at Oxford and Stanford University. This guide will walk you through running your code on GPUs in Azure. Before we start, it cannot be stressed enough: do not leave the VM running when you are not using…


Learning more about the Microsoft Data Science Virtual Machine 4th April 6pm–7pm

Public webinar on DSVM This webinar focuses on demonstrating how the Data Science Virtual Machine (DSVM) in Microsoft Azure conveniently enables key end-to-end data analytics scenarios by providing users immediate access to a collection of the top data science and development tools of the industry, completely pre-configured, with worked out examples and sample code. We…


Setting up more than 18 GPU Instances on Azure using VMs or Containers

I have been getting a number of questions around the availability of Azure N Series GPU at present we have two SKUs NC (GPU Compute}_ and NV (GPU Visualisation)  This blog explains the differences between the SKUs and where NC vs NV hardware instances should be used https://blogs.msdn.microsoft.com/uk_faculty_connection/2017/01/10/azure-cloud-gpu-for-datascience-and-academic-activities-such-as-cloud-rendering/ DataCenter & OS Availability For NV machines…


Hijacking faces with Deep Learning techniques and Microsoft Azure

Guest Post by Team FaceJack Catalina Cangea, Laurynas Karazija, Edgar Liberis, Petar Veličković Team FaceJack working hard at Hack Cambridge ’17. Hijacking Faces Machine learning with deep neural networks (commonly dubbed “deep learning”) has taken the world by storm, smashing record after record in a wide variety of difficult tasks from different fields, including computer…


Deep Learning with Microsoft Cognitive Toolkit CNTK

Extracting value from large amounts of data {and making human sense of it is one of the primary challenge of data science   Introduction to Data Science 1.Find the data 2.Extract and acquire the data 3.Clean and transform the data 4.Understand the relationships in the data and build a model 5.Mine for additional data 6.Evaluate…