GPU Azure Instances for High Performance Computing

GPU Azure instances are a game-changer for high-performance computing. They offer a scalable and on-demand solution for applications that require intense computational power.

These instances are powered by NVIDIA Tesla V100 and V100S GPUs, which provide up to 15 teraflops of double-precision floating-point performance. This level of performance is essential for applications like scientific simulations, data analytics, and machine learning.

You can choose from various GPU instance types, including NC6, NC12, NC24, and NCv2, each with varying numbers of GPU cores and memory. The NC6 instance, for example, comes with 1 GPU core and 56 GB of memory.

Scaling up to the NC24 instance, you get 8 GPU cores and 224 GB of memory, making it ideal for large-scale workloads.

For your interest: Azure Spot Virtual Machines

Deploying a container group with GPU resources using a Resource Manager template is a viable option. You can create a file named gpudeploy.json and copy the following JSON into it to deploy a container instance with a V100 GPU that runs a TensorFlow training job against the MNIST dataset.

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The deployment takes several minutes to complete, and you'll need to supply the name of a resource group that was created in a region such as eastus that supports GPU resources. You can then view the log output by running the az container logs command.

It's essential to keep track of your containers and monitor them in the Azure portal or check the status of a container group with the az container show command to avoid unexpected long-running containers.

Expand your knowledge: Azure Container Instances

Creating a container group with GPU resources can take up to 8-10 minutes due to the time it takes to provision and configure a GPU VM in Azure.

You'll be billed for the resources consumed by your container group, which is calculated from the time it takes to pull your first container's image until the group terminates.

Pricing details can be found on the Azure website.

Container instances with GPU resources come pre-provisioned with NVIDIA CUDA drivers and container runtimes, allowing you to use container images developed for CUDA workloads.

We currently support up to CUDA 11.

In preview, GPU resources in container groups are subject to certain limitations.

You need to be aware that preview has its own set of rules when it comes to using GPU resources in container groups.

In preview, the following limitations apply when using GPU resources in container groups. Specifically, preview limitations are in place to ensure that GPU resources are not overutilized.

It's essential to understand these limitations to avoid any potential issues or errors when working with GPU resources in container groups.

These limitations are designed to provide a safe and controlled environment for testing and experimentation with GPU resources in container groups.

When deploying GPU resources, it's essential to set CPU and memory resources appropriate for the workload, up to the maximum values shown in the table for each SKU.

The maximum resources per SKU vary depending on the OS and GPU SKU. For example, on Linux with a V100 GPU, the maximum CPU is 6, memory is 112 GB, and storage is 50 GB for a single GPU.

If this caught your attention, see: Azure Gpu Cost

You can deploy up to 4 GPUs per V100 SKU, but the maximum CPU and memory resources increase accordingly. For example, with 4 GPUs, the maximum CPU is 24 and memory is 448 GB.

Here's a summary of the maximum resources per SKU for a V100 GPU on Linux:

It's worth noting that the default CPU limits for V100 SKUs are initially set to 0, so you'll need to submit an Azure support request to request an increase in an available region.

Clean up resources as soon as you're done working with them to avoid unnecessary expenses.

Using GPU resources can be expensive, so monitor your containers in the Azure portal to keep track of their status.

Check the status of a container group with the az container show command to see if any containers are running unexpectedly.

Delete containers you no longer need with the az container delete command to free up resources.

This will help prevent unexpected costs and keep your resources organized.

Spot Instance is a cost-saving option to deploy VMs in the cloud, offering up to 90% discount compared to pay-as-you-go prices.

You can get a significant discount, but actual discounts may vary based on region, Microsoft Azure Virtual Machines type, and compute capacity available.

Spot Instances are best suited for non-critical loads, which can run on unused capacity in the Azure infrastructure.

If there's no capacity available, the VM will be deallocated automatically, so make sure to plan accordingly.

Here are some key benefits of Spot Instances:

Riskfuel is a great example of how startups can use GPU Azure instances to transform industries. They're using accelerated valuation models powered by NVIDIA on Azure to provide on-demand access to fast valuation and risk sensitivity calculations.

You can get discounts on Microsoft Azure Virtual Machines, including different types of discounts with prerequisites that are explained below.

Riskfuel's solution is particularly useful for the over-the-counter (OTC) derivatives market, where speed and accuracy are crucial. By providing fast valuation and risk sensitivity calculations, they're helping customers make better decisions.

There are various discounts available on VMs, each with its own set of rules and requirements.

GPU (Graphic Processing Unit) optimized virtual machines provide high graphic performance, designed for compute-intensive, graphic-intensive, and visualization workloads.

These virtual machines are available with single or multiple GPUs, and they're perfect for applications that require high-end graphics support, such as professional design and engineering applications.

GPU optimized virtual machines are available in various sizes, including those with NVIDIA Tesla accelerated platform and NVIDIA GRID 2.0 technology, which provide the highest-end graphics support available in the cloud today.

Here are some key features of GPU Azure instances:

GPU Azure instances are ideal for applications that require high-end graphics performance, such as professional design and engineering applications, and are available with various sizes and configurations to suit different needs.

With NVIDIA RTX Virtual Workstations, available on Azure Marketplace, creative and technical professionals can access the most demanding professional design and engineering applications from the cloud.

These virtual workstations can be accessed from anywhere, allowing professionals to maximize their productivity.

NVIDIA RTX Virtual Workstations are specifically designed for the most demanding applications, making them ideal for tasks that require high-performance computing.

You can find these virtual workstations on the Azure Marketplace, making it easy to get started with high-performance computing.

GPU optimized virtual machines are designed for compute-intensive, graphic-intensive, and visualization workloads, providing high graphic performance.

These virtual machines are available with single or multiple GPUs, offering flexibility and scalability for different workloads.

By using GPU optimized virtual machines, professionals can take advantage of high-performance computing capabilities and improve their productivity.

High Performance Computing is a type of workload that can be handled by Azure's Virtual Machines. These VMs use hardware designed and optimized for compute-intensive and network-intensive applications.

The A8-A11 series uses Intel Xeon E5-2670 @ 2.6 GHz, while the H-series uses Intel Xeon E5-2667 v3 @ 3.2 GHz. High performance compute virtual machines are also known as compute-intensive instances.

Some examples of workloads that can be run on H-series virtual machines include fluid dynamics, finite element analysis, and seismic processing.

Here are some key characteristics of H-series virtual machines:

To learn more about the specific characteristics of each H-series size, visit the URL provided in the article section: https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-hpc#using-hpc-pack.

GPU Azure instances are a powerful tool for a variety of tasks. They can be used for fluid dynamics simulations, which is a complex field that involves studying the behavior of fluids under different conditions.

Some specific examples of workloads that can be run on GPU Azure instances include seismic processing, reservoir simulation, and risk analysis. These tasks require intense computational power and can be time-consuming to complete on traditional hardware.

Here are some examples of workloads that can be run on GPU Azure instances:

Digitalizing manufacturing has become a game-changer for companies like BMW, which leveraged NVIDIA GPUs and Azure Machine Learning to power its fully automated control processes.

BMW's electric vehicle production system is now fully automated, thanks to the power of technology.

NVIDIA Omniverse Cloud APIs on Microsoft Azure bring data interoperability, collaboration, and physically-based visualization to software tools for design, building, and operating industrial digital twins.

This means that companies can now design, build, and operate digital twins more efficiently and effectively, leading to improved productivity and reduced costs.

By digitalizing manufacturing, companies can gain real-time insights into their production processes, allowing them to make data-driven decisions and optimize their operations.

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The H series is designed to handle a wide range of complex workloads.

Fluid dynamics is one of the many applications that can be run on the H series, allowing for simulations of fluid flow and behavior.

Finite element analysis is another example, enabling the modeling of complex structures and their behavior under various loads.

Seismic processing is also a key workload, helping to analyze and interpret seismic data for oil and gas exploration.

Reservoir simulation is another critical application, allowing for the modeling of oil and gas reservoirs to optimize extraction.

Risk analysis is a workload that can be run on the H series, helping to identify and mitigate potential risks in various industries.

Electronic design automation is also supported, enabling the design and simulation of electronic systems.

Rendering is a workload that can be run on the H series, helping to create high-quality images and graphics for various applications.

Spark is a workload that can be run on the H series, allowing for fast and efficient processing of large datasets.

Weather modeling is another example of a workload that can be run on the H series, helping to predict and analyze weather patterns.

Quantum simulation is a workload that can be run on the H series, enabling the simulation of complex quantum systems.

Computational chemistry is another application that can be run on the H series, helping to model and simulate chemical reactions and systems.

Heat transfer simulation is a workload that can be run on the H series, allowing for the analysis and optimization of heat transfer in various systems.

Here is a list of some of the key workloads that can be run on the H series:

The N Series is designed to handle a variety of workloads, from virtual desktops to enterprise applications.

For virtual desktops, the N Series can support up to 100,000 virtual desktops per cluster, with each desktop requiring only 2 GB of RAM.

In addition to virtual desktops, the N Series can also handle enterprise applications such as email and collaboration software.

The N Series can support up to 10,000 users for email and collaboration software, with each user requiring 1 GB of RAM.

For database workloads, the N Series can support up to 50,000 users, with each user requiring 2 GB of RAM.

The N Series can also handle big data analytics workloads, supporting up to 10,000 users with each user requiring 4 GB of RAM.

In terms of storage, the N Series can support up to 100 TB of storage per node, making it ideal for large-scale data analytics workloads.

The N Series can also support up to 100,000 IOPS per node, making it suitable for high-performance applications such as video streaming.

Worth a look: Azure Data Studio Connect to Azure Sql

NVIDIA has introduced an AI foundry service on Microsoft Azure, catering to enterprises and startups.

This service allows for the development and tuning of custom generative AI applications, making it a valuable tool for those looking to deploy AI solutions on Azure.

The NVIDIA AI Foundry Service enables users to build custom generative applications using pretrained NVIDIA AI Foundation models.

NVIDIA NeMo and NVIDIA DGX Cloud are also part of this service, providing a comprehensive solution for AI development and deployment.

With NVIDIA AI Enterprise, users can transition to production and deploy their AI applications on Azure with ease.

This foundry service is specifically designed for custom generative AI applications, making it a unique offering in the market.

Additional reading: Azure Imds

Azure offers a range of virtual machine options for GPU instances, each designed to meet specific needs.

There are six classifications of virtual machines on Azure cloud: General purpose, Compute optimized, Memory optimized, Storage optimized, GPU, and High performance compute.

The Compute optimized VMs are perfect for applications that require high CPU-to-memory ratios, available in sizes Fsv2, Fs, and F.

Memory optimized VMs are ideal for applications that require high memory-to-CPU ratios, available in sizes Esv3, Ev3, M, GS, G, DSv2, DS, Dv2, and D.

Storage optimized VMs are designed for applications that require high disk throughput and IO, available in size Ls.

GPU VMs are specialized for heavy graphic rendering and video editing, available in sizes NV, NC, NCv2, and ND.

Here's a breakdown of the different VM types: