This blog is the last one in the machine learning series. Our first blog introduced the what and why of our solution, and the second blog compared traditional and Alluxio solutions. This blog will demonstrate how to set up and benchmark the end-to-end performance of the training process.
This blog is the second in the machine learning series following the previous one, which discussed Alluxio’s solution to improve training performance and simplify data management. With the help of Alluxio, loading data from cloud storage, training and caching data can be done in a transparent and distributed way as a part of the training process, thus improving training performance and simplifying data management. In this blog 2 of the series, we focus on comparing traditional solutions with Alluxio’s.
In this blog, we provide an overview of Alluxio’s AI/ML model training solution. For more details about the reference architecture and benchmarking results, please refer to the full length whitepaper.
Alluxio 2.4.0 focuses on features critical to large scale, production deployments in Cloud and Hybrid Cloud environments. Enterprises leverage Alluxio at enormous scale in many dimensions, including number of files, total volume of data, requests per second, and number of concurrent clients.
This article aims to provide a different approach to help connect and make distributed files systems like HDFS or cloud storage systems look like a local file system to data processing frameworks: the Alluxio POSIX API. To explain the approach better, we used the TensorFlow + Alluxio + AWS S3 stack as an example.