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HomeSOFTWAREDeep learning with TensorflowHarnessing high-performance computing in driving generative AI

Harnessing high-performance computing in driving generative AI

By Dr. Sayed Peerzade, EVP & Chief Cloud Officer, Yotta Data Services

High-Performance Computing as-a-Service provides a significant amount of computational power and resources to train Generative AI models. Find out the benefits of High-Performance Computing as-a-Service and how to leverage its power for training AI models.

Are you curious about how a computer program can generate something entirely new? The emergence of ChatGPT, Stable Diffusion, DALL-E-2, and other new models, have demonstrated the incredible potential of artificial intelligence. From solving some mathematical problems in the 1960s, today, Artificial Intelligence can create realistic images, original content, and more.

Generative Artificial Intelligence is the AI system that can create new and original content, like images, text, or music, that previously did not exist. Generative AI models are often trained and operated in High-Performance Computing (HPC) systems, which provide the needed computing power and infrastructure. In this article, we will dive into the working of Generative AI, what it can do, and the role High-Performance Computing plays in training Generative AI models.

What is Generative AI?Generative AI, a subfield of Machine Learning (ML), is a subset of artificial intelligence that can generate new data, resembling a given training set. The underlying patterns and distributions in the data are learned, and the information is used to produce fresh, diverse data.

While there are many kinds of Generative AI models, the commonly used ones include the following:

Generative Adversarial Networks: These are Deep Learning models, comprising two neural networks, namely generator and discriminator, that compete with one another. The generator creates synthetic data that can misguide the discriminator, while the discriminator tries to identify the data as fake or real. This continues until the samples produced by the generator are indistinguishable from the real data. Generative Adversarial Networks have applications in image generation, natural language processing, video generation, and other areas. TensorFlow, an open-source software library for artificial intelligence, provides support for implementing Generative Adversarial Networks.

Transformer-based model: This uses self-attention mechanisms to process sequential data. The model learns dependencies among components in a sequence by attending to all elements in parallel. The attention mechanism is used to draw dependencies among elements. Text classification, machine translation, and language modelling are some of the popularly used models in the field. Some of the applications of transformer-based models include text translation, computer code generation, etc. GPT-3, BERT, and T5 are based on the transformer model.

Variational Autoencoders: They use a probabilistic approach to generate new data. The encoder codes the input data into a low-dimensional latent space. The latent representation is decompressed by the decoder and reproduced to the original data space. Open-source neural network library, Keras provides support for implementing Variational Autoencoders. The applications of Variational Autoencoders include image and video generation, data compression, data augmentation, etc.

Training Generative AI ModelsHigh-Performance Computing (HPC) provides the required computational resources and infrastructure to handle the data and algorithms to train a Generative AI model. Once a large dataset (text, images, or audio) is collected, it is transformed into a format acceptable for training the AI model. This entails using various parallel processors to run compute-intensive operations to update a model’s parameters. Once the training is complete, the model is evaluated to gauge how well it has learned to create new data resembling the original.

OpenAI, which launched its viral AI language model, ChatGPT, used Microsoft Azure’s HPC capabilities to train Machine Learning models. The AI research company has also used on-premises infrastructure and other cloud providers for its High-Performance Computing needs.

Applications of HPC in Generative AIHigh-Performance Computing has various applications in the field of Generative Artificial Intelligence, some of which include:

Image Generation: High-Performance Computing can be used to train Generative AI models for image creation, such as Generative Adversarial Networks and Variational Autoencoders. Unique and new images can be generated by these models based on input data.

Text Generation: High-Performance Computing can train Generative AI models to generate text, such as GPT-3. These models can create new and unique text in a certain language or style and be used for content creation or language translation.

Video Generation: Video generation using AI often involves using generative models, like GANs or VAEs. A high-end GPU and significant computational resources are necessary to process and analyse the large volume of data involved in video generation. HPC clusters, with their GPUs, offer the required resources to train and run these models.

Increasing Role of High-Performance Computing as-a-Service (HPCaaS)Initially, supercomputers were largely used by medical researchers and governments. However, the high cost associated with procurement, operations and maintenance of on-premises HPC infrastructure made it unaffordable for most businesses. Today, High-Performance Computing on the cloud, known as High-Performance Computing as-a-Service (HPCaaS), offers businesses a quicker, more scalable, and cost-effective alternative to reap the benefit of High-Performance Computing. Users can rent High-Performance Computing on a consumption-based subscription model and pay only for the resources they use.

Benefits of High-Performance Computing as-a-ServiceHigh-Performance Computing as-a-Service enables the execution of compute-intensive applications on the cloud, without CapEx investments in hardware infrastructure. It can help enterprises shorten the training time of Generative AI models, given its advantage of parallel computing and high-speed interconnects.

Providers of High-Performance Computing as-a-Service usually have experts responsible for managing computing resources. This, in turn, relieves the enterprise’s IT personnel to focus on more significant tasks. Finally, it offers a scalable computing environment that can adapt to the changing requirements of enterprises. Users can quickly respond to changing demands, without the need for extensive reconfiguration.

Path Forward: Generative AI and HPCaaSThe popularity of Generative AI in recent years has increased the demand for High-Performance Computing as-a-Service. Generative AI, which appears to hold a very promising future, has a lot of potential in a wide range of fields, including language processing, art, manufacturing, and healthcare. By leveraging High-Performance Computing as-a-Service, companies can train sophisticated generative models and produce high-quality outputs, without having to invest in expensive hardware.

With Yotta’s High-Performance Computing as-a-Service, powered by NVIDIA, enterprises can benefit from supercomputing performance, vast storage, and boundless scalability. Hosted on Yotta’s state-of-the-art data center with fail-safe power infrastructure, it assures 100% uptime while allowing users to pay on a consumption basis. Overall, High-Performance Computing as-a-Service provides an efficient, cost-effective, and flexible solution for Generative AI, enabling enterprises to leverage AI for driving growth.

 

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The information provided in our posts or blogs are for educational and informative purposes only. We do not guarantee the accuracy, completeness or suitability of the information. We do not provide financial or investment advice. Readers should always seek professional advice before making any financial or investment decisions based on the information provided in our content. We will not be held responsible for any losses, damages or consequences that may arise from relying on the information provided in our content.

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