GPUs and Deep Learning

Companies are becoming more reliant on Artificial Intelligence. AI has become a common factor in everything from predictive text to customer contact. When it comes to understanding machine learning and artificial intelligence, it can be helpful to familiarize yourself with the concept of deep learning and how it relates to Graphics Processing Units (GPUs).

While the concept of machine learning has existed for decades, the processing capacity offered by modern GPU-enabled computing solutions has opened up new opportunities for machine learning applications. Deep learning algorithms are one such application, existing as a subset of machine learning methodologies that seek to emulate the behavior of biological neural networks.

In this article, we will explore how GPUs and deep learning have contributed to the broader field of machine learning and artificial intelligence.

Topics Include:

• GPUs and Deep Learning
  • The Rise of GPU-based Computing
  • History of Machine Learning
• Deep Learning and Neural Networks
  • What is Feature Learning?
  • What are Neural Networks?
  • The Machine Learning Revolution: GPUs and Deep Learning
• How To Set Up vGPUs With OpenStack Nova
• GPU-Enabled Private Cloud Hosting and Deep Learning

GPUs and Deep Learning

The Rise of GPU-Based Computing and Machine Learning

The past decade has seen a marked increase in GPU-based computing, in part to meet the demand of our increasingly digital world, but also to take advantage of new opportunities in the realm of software. With the higher computational throughput offered by GPU-enabled technologies, software developers and engineers are able to develop vastly more complex algorithms that can process enormous amounts of data and form predictions using a technique known as machine learning. Whether the task is processing experimental data for scientific purposes or processing financial transactions, it can be done more quickly and more efficiently with GPU-enabled computing solutions and machine learning.

History of Machine Learning

The term machine learning was first coined in 1959 by researcher Arthur Samuel. Early applications of machine learning algorithms mainly involved pattern classification, wherein computers would be trained to recognize and classify patterns based on inputted sample data. One of the most well-known figures involved in the development of machine learning was the English mathematician Alan Turing.

Involved in decrypting wartime communications in World War II, Turing became a pioneer in the field of what would later become known as machine learning and artificial intelligence. One of the key questions Turing posed in his research could be phrased simply as “Can machines think?”. This fundamental question about the nature of machine intelligence inspired generations of researchers and engineers to determine whether or not machines are capable of the same level of cognition as a human mind. In pursuit of developing a truly artificially intelligent machine, the concept of machine learning was eventually established.

Among other objectives, the goal of research into the field of machine learning is to enhance and subsequently describe the cognitive potential of computers. Modern machine learning itself has two primary objectives, the first being the classification of data. This generally involves the categorization and organization of large amounts of data such as stock market transactions. The way this data is handled is dictated by a software model (sometimes referred to as an algorithm) that is developed in order to complete a specific task. The second objective of machine learning is to generate predictions based on the developed model. In the example of stock market transactions, a machine learning algorithm could generate predictions based on the available data.

Since the development of the concepts of machine learning and artificial intelligence, the technologies have seen numerous real-world applications in the realms of finance, medicine, biology, and many more. As the technology continues to mature, the development of new classifications of machine learning has become necessary. One such example is deep learning, an important subset of machine learning that leverages sophisticated neural networks to perform computational tasks.

Deep Learning and Neural Networks

As mentioned previously, deep learning is a subset of machine learning that primarily focuses on the use of artificial neural networks with what is referred to as representation learning or feature learning. Before we can dive into the concept of deep learning, we must first understand the concepts of feature learning and neural networks.

What is Feature Learning?

Feature learning is a technique that allows systems to automatically determine which representations are needed for feature detection or classification. Feature detection involves the identification of features, which in this context refers to a measurable characteristic of a given phenomenon. Put simply, feature detection involves the identification of measurable characteristics from a set of given entities or phenomena. Feature learning leverages feature detection to aid in the classification of data by machine learning algorithms. An example of one such algorithm are neural networks.

What are Neural Networks?

Neural networks, in the biological sense, are networks of interconnected neurons. In the realm of computing, neural networks are composed of networks of artificial neurons or nodes. These artificial neural networks form the backbone of modern machine learning, and more recently, deep learning.

Artificial neural networks primarily employ the use of artificial neurons, which are mathematical software functions developed as a model of biological neurons. These artificial neurons receive input and provide output in much the same way as a biological neuron, albeit using hardware and software rather than organic material. Large networks of these artificial neurons can be created, allowing them to perform computational tasks that would overwhelm traditional computational frameworks. As such, research into the field of artificial neural networks has been vital in the development of real-world applications of deep learning such as computer vision, speech recognition, and much more.

The Machine Learning Revolution: GPUs and Deep Learning

Deep learning, as mentioned previously, is a subset of machine learning that focuses on the use of artificial neural networks. The term “deep” is used to describe this concept as it refers to the use of multiple layers in the neural network. Deep learning has existed as a concept for decades, but it was not until the early 21st century that deep learning began to mature and differentiate itself from other forms of machine learning. In 2009, popular graphics hardware manufacturer Nvidia contributed to what has been referred to as a “big bang of machine learning“, as advances in hardware spurred on a renewed interest in deep learning applications.

Through the use of Nvidia GPUs, deep learning neural networks were trained, with some estimates stating that the GPU hardware could increase the speed of deep learning systems by 100 times. By employing GPU-based deep learning algorithms, artificial neural networks can be trained in a matter of days, rather than the weeks previously required. As such, it is clear why GPUs revolutionized the field of deep learning.

How To Set Up vGPUs With OpenStack Nova

With Jacob Hipps, OpenMetal’s Principal Engineer
Want to explore even further by using virtual GPUs within OpenStack? Watch an enlightening session that delves deep into the world of vGPUs with OpenStack Nova.

As an open source cloud computing platform, OpenStack Nova serves as the bedrock for building and managing virtual machines (VMs) in the cloud. Its flexible and scalable VM provisioning, resource management, and access control capabilities make it an indispensable project of the OpenStack ecosystem for cloud infrastructure.

During this session at OpenInfra Summit 2023, Jacob delves into the hardware requirements necessary to create a robust vGPU infrastructure, from GPUs to CPUs, memory to storage.

By the end of this comprehensive session, you’ll have the skills and confidence to leverage the power of vGPUs within OpenStack Nova.

Need vGPUs? Need GPUs? Schedule a meeting with an OpenMetal representative to discuss your needs.

GPU-Enabled Private Cloud Hosting and Deep Learning

For users looking to experiment with deep learning methodologies, it is important to have a computing solution that leverages state-of-the-art GPU hardware. OpenMetal now offers a range of GPU-enabled Private Cloud hosting solutions, all of which have the power and support you need to start learning how to employ deep learning technologies. Now that you have a better understanding of GPUs and deep learning, hopefully you can better decide which solution is right for you and your organization.