Intel Cloud Optimization Enhances AWS AI

Intel Cloud Optimization on AWS

Because it provides infrastructure and scalability, cloud computing is often used to create and operate large AI systems. Amazon Web Services (AWS), one of the largest and most prominent CSPs, offers hundreds of services to build any cloud application. The platform’s purpose-built databases and tools for AI and machine learning let developers and enterprises innovate faster, cheaper, and more agilely.

Developers may accelerate their innovation on popular hardware technologies and further boost model efficiency by utilizing pre-built optimizations and tools for a wide range of applications and use cases on AWS. It can take a lot of time and resources to find and implement the best tools and optimizations for your project. The pain of adding additional architectures to code can be mitigated for developers by providing comprehensive documentation and guides that make the implementation of these optimizations simple.

Intel Cloud Optimization Modules: What Are They?

Intel Cloud Optimization Modules are a set of cloud-native, open-source reference architectures designed with production AI developers in mind. They further optimize the potential of cloud-based solutions that easily connect with AI workloads. These modules enable developers to apply AI solutions that are optimized for Intel processors and GPUs, thereby increasing workload efficiency and achieving peak performance.

With specially designed tools to complement and enrich the cloud experience on AWS with pertinent codified Intel AI software optimizations, the cloud optimization modules are accessible for well-known cloud platforms like AWS. With end-to-end AI software and optimizations for a range of use cases, including computer vision and natural language processing, these optimizations provide numerous important advantages for driving AI solutions.

Every module has a content bundle that contains a whitepaper with additional details on the module and its contents as well as the open-source GitHub repository with all of the documentation. The content packages also include a cheat sheet that lists the most pertinent code for each module, a video series, practical implementation walkthroughs, and the opportunity to attend office hours if you have any special implementation-related issues.

AWS Cloud Intel Cloud Optimization Modules

AWS users can choose from a number of Intel Cloud Optimization Modules, which include optimizations for popular AWS tools like SageMaker and Amazon Elastic Kubernetes. Below, you may find out more about various AWS optimization modules:

GPT2-Modest Dispersed Instruction

Generative pre-trained transformers, or GPT models, are widely used in a range of fields as GenAI applications. Since compact models are easier to construct and deploy, building large language models (LLM) is often sufficient in many use situations. This module shows developers how to optimize a GPT2-small (124M parameter) model for high-performance distributed training on an AWS cluster of Intel Xeon CPUs.

Using software optimizations and frameworks such as the Intel Extension for PyTorch and oneAPI Collective Communications Library (oneCCL) to speed up the process and improve model performance in an effective multi-node training environment, the module walks through the whole lifecycle of fine-tuning an LLM on a configured AWS cluster. An LLM on AWS with the ability to produce words trained on your particular task and dataset for your use case is the end result.

SageMaker with XGBoost

A well-liked tool for creating, honing, and deploying machine learning applications on AWS, Amazon SageMaker comes with built-in Jupyter notebook instances and commonly used, optimized machine learning methods for faster model building. Working through this session will teach you how to activate the Intel AI Tools for accelerated models and inject your own training and inference code into a prebuilt SageMaker pipeline. This module accelerates an end-to-end custom machine learning pipeline on SageMaker by leveraging Intel Optimization for XGBoost. The Lambda container has all the parts needed to create custom AWS Lambda functions with XGBoost and Intel oneDAL optimizations, while the XGBoost oneDAL container comes with the oneAPI Data Analytics Library to speed up model algorithms.

Within Kubernetes, XGBoost

With an automatically managed service, Amazon Elastic Kubernetes Services (EKS) makes it simple for developers to launch, operate, and expand Kubernetes applications on AWS. Using EKS and Intel AI Tools, this module makes it easier for developers to create and launch accelerated AI applications on AWS. With Intel oneDAL optimizations, developers can learn how to construct an expedited Kubernetes cluster that makes use of Intel Optimization for XGBoost for AI workloads. The module makes use of Elastic Load Balancer (ELB), Amazon Container Registry (ECR), and Amazon Elastic Compute Cloud (EC2) in addition to EKS.

Use Intel Cloud Optimization Modules to improve your AI projects on AWS by leveraging Intel optimizations and containers for widely used tools. To further your projects, you can learn how to use strong software optimizations and construct accelerated models on your preferred AWS tools and services. Use these modules to maximize the potential of your AWS projects, and register for office hours if you have any inquiries concerning implementation!

They invite you to explore Intel’s additional AI Tools and Framework enhancements and discover the oneAPI programming paradigm, which is a unified, open, and standards-based framework that serves as the basis for Intel’s AI Software Portfolio. Additionally, visit the Intel Developer Cloud to test out the newest AI-optimized software and hardware to assist in creating and implementing your next cutting-edge AI projects!