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GPU for DL: Benefits and Drawbacks of On-Premises vs. Cloud

As technology advances and more organizations are implementing machine learning operations (MLOps), people are looking for ways to speed up processes. This is especially true for organizations working with deep learning (DL) processes which can be incredibly long to run. You can speed up this process by using graphical processing units (GPUs) on-premises or in the cloud.

GPUs are microprocessors that are specially designed to perform specific tasks. These units enable parallel processing of tasks and can be optimized to increase performance in artificial intelligence and deep learning processes.

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Monitoring NVIDIA GPU Usage in Kubernetes With Prometheus

If you’re familiar with the growth of ML/AI development in recent years, you’re likely to be aware of leveraging GPUs to speed up the intensive calculations required for tasks like Deep Learning. Using GPUs with Kubernetes allows you to extend the scalability of K8s to ML applications.

However, Kubernetes does not inherently have the ability to schedule GPU resources, so this approach requires the use of third-party device plugins. Additionally, there is no native way to determine utilization, per-device request statistics, or other metrics—this information is an important input to analyzing GPU efficiency and cost, which can be a significant expenditure.

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Deploy RAPIDs on GPU-Enabled Virtual Servers on a Virtual Private Cloud

Learn how to set up a GPU-enabled virtual server instance (VSI) on a Virtual Private Cloud (VPC) and deploy RAPIDS using IBM Schematics.

The GPU-enabled family of profiles provides on-demand, cost-effective access to NVIDIA GPUs. GPUs help to accelerate the processing time required for compute-intensive workloads, such as artificial intelligence (AI), machine learning, inferencing, and more. To use the GPUs, you need the appropriate toolchain - such as CUDA (an acronym for Compute Unified Device Architecture) - ready.

Let's start with a simple question.

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How to Use NVIDIA GPU Accelerated Libraries

Neural Network

How To Use NVIDIA GPU Accelerated Libraries for AI

If you are working on an AI project, then it's time to take advantage of NVIDIA GPU accelerated libraries if you aren't doing so already. It wasn't until the late 2000s when AI projects became viable with the assistance of neural networks trained by GPUs to drastically speed up the process. Since that time, NVIDIA has been creating some of the best GPUs for deep learning, allowing GPU accelerated libraries to become a popular choice for AI projects.

If you are wondering how you can take advantage of NVIDIA GPU accelerated libraries for your AI projects, this guide will help answer questions and get you started on the right path.

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NVIDIA’s GauGAN Gives Gauguin a Run for His Money

Artificial technology may not actually be turning us into superheroes, at least not in the timeframe posited by this piece in VentureBeat, but it is giving us some pretty sweet new abilities.

“Wouldn’t it be great if everyone could be an artist?” asks NVIDIA’s VP of Applied Deep Learning Research, Dr. Bryan Catanzaro. His excitement is palpable as he reveals one of NVIDIA’s newest AI projects in the above video, a generative adversarial network known as GauGAN that gives everyone the power to create lifelike works of art in minutes.