Master the OpenVINO Toolkit
Table of Contents:
- Introduction to Intel OpenVino on Red Hat OpenShift Data Science Platform
- Managed OpenShift and Red Hat OpenShift Data Science
- Partner Ecosystem in Red Hat OpenShift Data Science
- Benefits of Running Red Hat OpenShift Data Science with Intel
- Introduction to Intel OpenVino
- Model Optimization with OpenVino
- Model Deployment with OpenVino
- Optimized Performance with OpenVino
- Industries that Benefit from OpenVino
- Demo of Intel OpenVino on Red Hat OpenShift Data Science Platform
Introduction to Intel OpenVino on Red Hat OpenShift Data Science Platform
In this article, we will explore the integration of Intel OpenVino with Red Hat OpenShift Data Science Platform. We will discuss the benefits of using this combination and how it can enhance the performance and efficiency of data science models. We will also provide a demo of Intel OpenVino on the Red Hat OpenShift Data Science Platform.
Managed OpenShift and Red Hat OpenShift Data Science
Before diving into the details of Intel OpenVino, let's understand the concept of managed OpenShift and Red Hat OpenShift Data Science. Managed OpenShift is an open hybrid cloud platform that provides self-service capabilities for deploying and managing applications. Red Hat OpenShift Data Science is an offering on top of the managed OpenShift platform that provides various Core data science tools such as Jupyter, TensorFlow, and PyTorch libraries.
Partner Ecosystem in Red Hat OpenShift Data Science
Red Hat OpenShift Data Science is not complete without its partner ecosystem. Various independent software vendors, including Intel, provide additional value and features on top of the platform. Intel OpenVino is one such partner that offers a model optimization and serving framework for enhanced performance and efficiency.
Benefits of Running Red Hat OpenShift Data Science with Intel
One of the main advantages of using Intel OpenVino with Red Hat OpenShift is the out-of-the-box acceleration provided by Intel hardware. As the default hardware in Amazon Web Services (AWS) is Intel, models developed and trained using Intel frameworks can leverage this acceleration. This not only speeds up the model development and training process but also improves the performance of deployed models in production.
Introduction to Intel OpenVino
Intel OpenVino is a model optimization and serving framework that offers several optimizations to enhance the performance of data science models. It includes techniques like quantization, accuracy-aware quantization, layer pruning, and operation Fusion. These optimizations result in faster computations, reduced memory footprint, and improved performance, making it an ideal solution for edge deployments with limited compute resources.
Model Optimization with OpenVino
OpenVino offers various techniques for model optimization. Quantization is a method that reduces the numerical precision of the model, resulting in faster computations without significant loss of accuracy. Accuracy-aware quantization allows users to set a threshold to maintain a certain level of accuracy during the quantization process. Layer pruning and sparsification remove unnecessary complexity from the model by removing nodes with zero weights. Operation fusion reduces the model's footprint by combining operations and taking AdVantage of Intel optimized instructions.
Model Deployment with OpenVino
In addition to model optimization, OpenVino also handles model deployment. Standard model deployments may encounter performance issues when deployed on different hardware configurations. OpenVino addresses this challenge by optimizing the model for the intended inference device, ensuring high performance irrespective of the hardware used. This makes model deployment more efficient and reliable, even in edge computing environments with limited resources.
Optimized Performance with OpenVino
Using OpenVino guarantees higher performance without sacrificing accuracy and business goals. By applying optimizations like quantization and operation fusion, models can achieve better performance while maintaining the desired level of accuracy. This is particularly crucial for industries such as telecommunications, healthcare, and government, where accelerated model inference can significantly improve computational efficiency, throughput, and latency.
Industries that Benefit from OpenVino
OpenVino finds applications in various industries due to its ability to enhance model inference performance. Telecommunications, healthcare, government, and many others can benefit from increased computational efficiency, higher throughput, lower latency, and improved energy costs. OpenVino allows for the deployment of models at the edge with confidence that they will deliver Timely results. It also enables the density of the compute footprint by accommodating more models on a single node.
Demo of Intel OpenVino on Red Hat OpenShift Data Science Platform
To demonstrate the integration of Intel OpenVino with Red Hat OpenShift Data Science Platform, we will provide a quick demo. Using the OpenVino toolkit operator, we will showcase how easy it is to load, infer, and deploy models using OpenVino within a Jupyter notebook. We will also explore the capabilities of the OpenVino model server, which offers high-performance model serving with customizable deployment parameters.
In conclusion, the integration of Intel OpenVino with Red Hat OpenShift Data Science Platform provides significant performance and efficiency improvements for data science models. With various optimizations and model deployment capabilities, OpenVino enhances computational efficiency, reduces latency, and improves overall model inference performance. It opens doors to a wide range of industries looking to leverage accelerated model inference and maximize their data science capabilities.