pdfcoffee

Chapter 12
• Nvidia Tesla K80
• Nvidia Tesla P4
• Nvidia Tesla T4
• Nvidia Tesla V100
Virtual machine on Microsoft Azure
For deep learning and prediction applications Azure provides machines with GPU
capabilities. These are called N-series machines.
According to the Microsoft Azure site (https://azure.microsoft.com/en-in/
pricing/details/virtual-machines/series/) there are three different N-series
offerings, each aimed at specific workloads:
• NC series: It focuses on high-performance computing and machine learning
applications. The latest version – NCsv3 – features Nvidia's Tesla V100 GPU.
• ND series: It focuses on training and inference scenarios for deep learning.
It uses the Nvidia Tesla P40 GPUs. The latest version – NDv2 – features the
Nvidia Tesla V100 GPUs.
• NV series: This supports powerful remote visualization applications and
other graphics-intensive workloads backed by the Nvidia Tesla M60 GPU.
All of them also offer optional InfiniBand interconnect to enable scale-up
performance. To create a virtual machine on Azure you need to follow three
basic steps:
1. Log in to Azure portal
2. Select Virtual machines as a resource and then select Create Virtual
Machine
3. Choose the configuration you require and launch it
In Azure also you need to request an increase in quota for certain compute instances.
Jupyter Notebooks on cloud
During development and testing of the model, many in the machine learning
community find using Jupyter Notebooks handy; they provide an integrated
environment to run and view the result. They are very useful when you are
collaborating or want to discuss code with a client. With LaTeX support, many
researchers are even shifting to present their research papers on Jupyter, and
hence it makes sense to have Jupyter Notebook environment on cloud.
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