Accelerate Deep Learning in Hybrid Cloud with Intel Analytics Zoo & Alluxio

Table of Contents

1. Introduction
2. The Need for Deep Learning in Big Data Systems
3. Introducing Intel Analytics Super Project
4. Architecture of Analytics Zoo
5. Key Features of Analytics Zoo
6. Introduction to Hybrid Solution with Electro
7. The Challenge of Hybrid Cloud Environment
8. Solution: Combining Analytics Zoo and Electro
9. Performance Evaluation of the Solution
10. Conclusion

Introduction

In recent years, as data continues to grow exponentially, the use of machine learning and deep learning for big data analytics has become increasingly popular. However, integrating these advanced technologies into the existing big data systems poses significant challenges. In this article, we will explore a hybrid solution that combines analytics and electro to provide accelerated deep learning on big data systems. We will also discuss the architecture of Analytics Zoo, its key features, and evaluate the performance of the hybrid solution.

The Need for Deep Learning in Big Data Systems

As data volumes increase, traditional data analytics methods struggle to provide excellent performance in machine learning and deep learning. Many industries are now embracing complex big data analytics using these advanced technologies. However, integrating machine learning or deep learning systems into big data pipelines is challenging, as they need to work seamlessly with other components. This challenge led to the development of Intel Analytics Super Project, a unified AI platform for big data systems.

Introducing Intel Analytics Super Project

Intel Analytics Super Project is designed to simplify the creation of end-to-end deep learning pipelines for big data users. It allows users to create prototypes on their laptops using sample data, experiment on development clusters with historical data, and deploy models to production cluster environments. The platform provides seamless access to production big data systems like Hadoop, HBase, and enables effortless deployment on distributed clusters.

Architecture of Analytics Zoo

Analytics Zoo is a unified data analytics and AI platform that supports various computing environments, including laptops, Kubernetes clusters, and Hadoop clusters on the cloud. It also seamlessly integrates with popular deep learning frameworks like TensorFlow and PyTorch, as well as distributed analytics systems like Spark and Blink. Analytics Zoo provides API support for distributed TensorFlow or Python training on Spark and offers a rich set of building models for areas like recommendation, time series, computer vision, and NLP.

Key Features of Analytics Zoo

Analytics Zoo offers several key features that make it a powerful platform for big data analytics and deep learning. It provides distributed TensorFlow and Python training and inference on Spark, enabling users to utilize native TensorFlow or PyTorch models. Analytics Zoo leverages Rayon Spark, a framework that allows users to run Ray programs directly on the Spark cluster. It also provides an EstimatorPipeline compatible with Spark DataFrames and Spark DataFrames Pipelines for creating complex data analytics systems. Additionally, Analytics Zoo offers scalable AutoML frameworks for time series prediction, making model selection, hyperparameter tuning, and feature selection much easier.

Introduction to Hybrid Solution with Electro

The hybrid cloud environment, where compute and data storage are located in different clusters, has become increasingly popular with the rise of cloud computing. However, this environment poses challenges for accessing and loading remote data, especially for computationally intensive workloads like deep learning and Spark ETL. To address this challenge, Intel developed a solution that combines Analytics Zoo and Electro. This solution deploys Analytics Zoo and Electro on the same cluster, allowing Analytics Zoo to access remote data storage through Electro and significantly improving data loading performance.

The Challenge of Hybrid Cloud Environment

In a hybrid cloud environment, where compute and data storage are located in different clusters, accessing remote data can introduce significant overhead. This is particularly problematic for workloads that require frequent data access, such as deep learning and Spark ETL. One possible solution is to copy the data from the remote storage to the local compute systems, but this process can be time-consuming and error-prone. To overcome this challenge, Intel's hybrid solution with Analytics Zoo and Electro provides immediate data availability for the compute system, resulting in faster processing times and lower costs.

Solution: Combining Analytics Zoo and Electro

The combined solution of Analytics Zoo and Electro addresses the challenge of remote data access in a hybrid cloud environment. By deploying Electro as an access layer to remote data storage, Analytics Zoo achieves faster data loading times. The solution utilizes Electro's memory and SSD levels of data access, providing faster data access for computational tasks. By leveraging the superior data loading performance of Electro, Analytics Zoo enables deep learning workflows on big data systems to be executed more efficiently.

Performance Evaluation of the Solution

To evaluate the performance of the hybrid solution, we conducted experiments using AWS as the environment. We utilized Analytic Zoo, Electro, and Apache Spark to train an Inception model on the ImageNet dataset. Comparing the average data loading times between experiments with and without Electro, we observed a speedup of approximately 1.5 times when using Electro with Analytic Zoo. The standard deviation was also smaller, indicating more consistent performance. These results demonstrate the significant benefits of running deep learning training workflows on Apache Spark with Electro and Analytic Zoo.

Conclusion

The hybrid solution with Analytic Zoo and Electro offers a powerful and efficient approach to accelerate deep learning on big data systems. By combining these technologies, users can overcome the challenges of remote data access in a hybrid cloud environment and significantly improve data loading times. The performance evaluation highlights the benefits of utilizing Electro with Analytic Zoo, enabling faster and more consistent training workflows. With this hybrid solution, organizations can leverage the power of deep learning in their big data analytics pipelines and achieve superior results.

Highlights

• Hybrid cloud environment poses challenges for accessing and loading remote data in big data systems.
• Intel Analytics Super Project provides a unified AI platform for creating end-to-end deep learning pipelines.
• Analytic Zoo offers a comprehensive set of features, including distributed TensorFlow training on Spark, machine learning workflows, and autoML frameworks.
• Electro enhances data loading performance in the hybrid cloud environment, enabling faster and more efficient deep learning training.
• Performance evaluation demonstrates the benefits of the hybrid solution, with significant speedup and reduced standard deviation of data loading times.

FAQ

Q: What is the benefit of using Electro in the hybrid cloud environment? A: Electro improves data loading performance in the hybrid cloud by providing immediate data availability for the compute system. This enables faster processing times and reduces overhead in accessing remote data.

Q: Can Analytic Zoo be used with other deep learning frameworks besides TensorFlow and PyTorch? A: Yes, Analytic Zoo is compatible with various deep learning frameworks, including TensorFlow and PyTorch. It provides a unified platform for training and inference, regardless of the underlying framework.

Q: Does the hybrid solution support real-time inference? A: Yes, the hybrid solution with Analytic Zoo and Electro supports distributed real-time inference. This enables efficient cluster serving for TensorFlow, Python, and other compatible models.

Q: Are there any specific industries or use cases where Analytic Zoo and Electro have been successfully applied? A: Analytic Zoo and Electro have been successfully applied in various industries and use cases, including recommendation systems, time series analysis, computer vision, and natural language processing. Organizations like JD.com and media companies have reported excellent results using these technologies in their applications.

Q: Are there any specific requirements for the compute and storage clusters in the hybrid solution? A: The compute and storage clusters should be appropriately provisioned to handle the workload demands. The performance evaluation conducted on AWS utilized R5 instances with 32 vCPUs and 160 GB memory per instance, but specific requirements may vary based on the workload and dataset size.

Resources

• Analytics Zoo GitHub
• Electro GitHub
• Intel Analytics Super Project