Google Cloud with Intel Cloud Optimization Modules
Artificial intelligence (AI) applications are among the most widely used software applications being produced, particularly on cloud computing platforms that may provide minimal beginning costs and quick scaling by giving users simple access to specific hardware and accelerators. Data and application development, analysis, and management capabilities are available from Google Cloud Platform (GCP), a renowned cloud service provider. AI Platform, Video Intelligence API, and Natural Language API are GCP AI and machine learning development tools. For your AI projects, using a platform such as GCP may streamline your development process and provide you with access to strong hardware tailored to your requirements.
Additional improvements in model performance may be achieved using pre-made software modifications designed for various uses. Developers may observe quicker and less resource-intensive model deployment and inference by putting these software enhancements into practice. Finding and incorporating these efficiencies into processes, however, may be a laborious and time-consuming task. Having access to extensive documentation and guidelines in an open-source setting enables developers to overcome obstacles by implementing new optimizing designs, making it easier to improve the performance of their models.
Intel Cloud Optimization Modules: What Are They?
The codified Intel AI software improvements included in the open-source codebases that make up the Intel Cloud Optimization Modules are intended primarily for AI developers operating in production settings. To improve the capabilities of AI-integrated cloud systems, these modules provide a collection of reference designs that are cloud-native. Developers may assure maximum performance on Intel CPU and GPU technologies and increase the efficiency of their workloads by using these optimization techniques.
These modules for intel cloud optimization are accessible on a number of widely used cloud platforms, such as GCP. The modules improve workloads on GCP and boost performance by using specially designed tools, end-to-end AI software, and optimizations. For a range of use applications, including computer vision, transfer learning, and natural language processing (NLP), these improvements help boost machine learning models.
An open-source GitHub repository containing all pertinent documentation is included in each content package for each module. This includes a cheat sheet that lists the most pertinent code for each module, a whitepaper that provides additional information about the module and its relationships, and a series of videos that provide practical instructions on how to implement the architectures. Attending office hours is an additional option for inquiries about certain implementations.
Modules for Intel Cloud Optimization for GCP
For GCP, there are Intel Cloud Optimization Modules that provide optimizations for Kubeflow pipelines and generative pre-trained transformer (GPT) models. Below, you may find out information about various GCP optimization modules:
Distributed Training using nanoGPT
Although large language models (LLMs) are becoming more and more common in Generative AI (GenAI) applications, in many use situations using smaller LLMs is adequate. Smaller models are simpler to create and implement, hence using a GPT model like nanoGPT (124M parameter) may lead to higher model performance. This module shows developers how to create a high-performance distributed training scenario from a normal single-node PyTorch training scenario and teaches them how to fine-tune a nanoGPT model on a cluster of Intel Xeon CPUs on GCP.
This module also incorporates frameworks and software enhancements such as the Intel Extension for PyTorch and oneAPI Collective Communications Library (oneCCL) to improve model performance in an effective multi-node training environment and speed up the fine-tuning process. An optimized LLM on a GCP cluster is the end product of this training, and it may quickly produce words or tokens that are appropriate for your particular purpose and dataset.
On Kubeflow Pipeline, XGBoost
Popular open-source project Kubeflow makes it easier and more scalable to install machine learning processes on Kubernetes. This module offers optimal training and models to forecast the likelihood of customer loan default, and it walks you through the implementation of Kubeflow on GCP. You will get knowledge on how to activate Intel Cloud Optimization for XGBoost and Intel daal4py in a Kubeflow pipeline by finishing this module. Additionally, you will discover how to install and configure a Kubeflow cluster on GCP with integrated AI acceleration via Intel AMX using Intel Xeon CPUs. In order to see how these improvements might enhance pipeline workflow, developers can also bring and construct their own Kubeflow pipelines.
Use Intel Cloud Optimization Modules to further your AI endeavors on GCP. By using Intel cloud optimizations and containers for well-known tools, these modules may assist you in developing accelerated AI models that work smoothly with your chosen GCP services and improve the capabilities of your projects. Check out these courses to see how you can advance AI, and register for office hours if you have any issues about 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.
