How do you run TensorFlow on a remote storage system?

Problem It becomes increasingly more popular among data scientists to train models based on frameworks like TensorFlow on a local server or cluster while using remote shared storages like S3 or Google Cloud Storage to store a massive amount of the input data. This stack provides high flexibility and cost efficiency, especially requires no dev-ops … Continued

Evolution of big data stacks under computational and storage separation architecture

Shanghai *

A new generation of open source big data, represented by Alluxio, born at the University of California at Berkeley, looks at this issue. Different from systems such as designing storage tight coupling to achieve low-cost reliable storage HDFS, by providing a virtual data storage layer defined and implemented by software for data applications, abstracting and integrating cloudy, hybrid cloud, multi-data center and other environments The underlying files and objects, and through intelligent workload analysis and data management, make data close to computing and provide data locality, big data and machine learning applications can be achieved with the same performance and lower cost.