English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The input data pipeline is an essential component of each machine learning(ML) training job. It is responsible for reading massive amounts of trainingdata, processing batches of samples using complex transformations, and loadingthem onto training nodes at low latency and high throughput. Performant inputdata systems are becoming increasingly critical, driven by skyrocketing datavolumes and training throughput demands. Unfortunately, current input datasystems cannot fully leverage key performance optimizations, resulting inhugely inefficient infrastructures that require significant resources -- orworse -- underutilize expensive accelerators.  To address these demands, we present cedar, a programming model and frameworkthat allows users to easily build, optimize, and execute input data pipelines.cedar presents an easy-to-use programming interface, allowing users to defineinput data pipelines using composable operators that support arbitrary MLframeworks and libraries. Meanwhile, cedar transparently applies a complex andextensible set of optimization techniques (e.g., offloading, caching,prefetching, fusion, and reordering). It then orchestrates processing across acustomizable set of local and distributed compute resources in order tomaximize processing performance and efficiency, all without user input. Onaverage across six diverse input data pipelines, cedar achieves a 2.49x, 1.87x,2.18x, and 2.74x higher performance compared to tf.data, tf.data service, RayData, and PyTorch's DataLoader, respectively.

cedar: Composable and Optimized Machine Learning Input Data Pipelines