Build deep learning model training apps using CNCF Fluid with Amazon EKS

Build deep learning model training apps using CNCF Fluid with Amazon EKS Deep learning data loading challenges and solutions Data loading bottleneck Challenges of managing parallel file systems The advantages of the file system built by Amazon EKS and CNCF Fluid Solution overview Achieve training data caching using and elastic high throughput file system using Amazon EKS and CNCF Fluid Achieve deep learning model training resource and workflow orchestration using Amazon EKS, KubeRay, and Ray Train Architecture design explanation Technical implementation Prerequisites Provision infrastructure on Amazon EKS Set up Fluid with JuiceFSRuntime on Amazon EKS Training data preparation and caching Amazon ECR image creation Create Ray Cluster Ray Job submission Ray Job monitoring Observability (optional) Cleaning up Conclusion About the authors Machine learning (ML) intensive companies face significant challenges in efficiently managing training data. This post introduces a solution to build an ephemeral, cloud-native elastic high-throughput file system. By the end of this post, you will learn how to implement the elastic high-throughput file system using Amazon Elastic Kubernetes Service (Amazon EKS ) and CNCF Fluid , set up efficient data caching mechanisms, and orchestrate training workflows using KubeRay. This post targets DevOps engineers, MLOps specialists, and infrastructure architects who are responsible for building and optimizing ML training infrastructure in cloud environments, particularly those working with Amazon EKS and looking to enhance their deep learning training pipelines. You will need one hour to read through this post and two hours for the demo implementation. The cost for the resources is approximately $4 USD per hour. The following sections outline the deep learning data loading challenges and solutions. Data loading poses a major performance bottleneck in deep learning training systems, especially in large-scale operations. This challenge stems from two key issues: the need to repeatedly access many small files, and the constant back-and-forth communication between storage and computing systems. Distributed systems, complex datasets, random data access patterns, and extensive data augmentation amplify these problems during training. The significance of this bottleneck varies across different models and datasets but consistently remains a critical concern, particularly given the high costs of GPU computing resources. This makes it essential to optimize data loading for better hardware efficiency and faster training times.