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 recent success of AI is contributed by the adaptation of u sing deep learning in decision making process. To harness the power of deep learning, developers must not only rely on a computing framework, but a cloud platform to ensure resource utilization and computin g performance to ease the burden on users as well. Hence, ”how cloud resources should be orchestrated for deep learnin g?” becomes a fundamental question for cloud providers. In this work, we built an in-house OpenS tack cloud platform to enable various resource orchestrations, including virtual machine, container and bare-metal. Then we systematically evaluate the performance of different orchestration choices using Tensorfl ow image classification benchmarks to quantify the performance impact and discuss the challenges of addressin g these performance issues.

Comparison Between Bare-metal, Container and VM using Tensorflow Image Classification Benchmarks for Deep Learning Cloud Platform