Subject archive for "ray"

Ray clusters with Domino accelerate data science innovation.
Machine Learning

Domino Unlocks the Power of Data Science with Ray 2 Clusters

OpenAI demonstrated the profound impact generative AI could have. Such techniques turn datasets into transformative tools and products. Tangible AI projects that are both inspiring and can save your company time and money. Better yet, you are in a good position to aim high.

By Thomas Dinsmore and Yuval Zukerman5 min read

Machine Learning

Lightning fast CPU-based image captioning pipelines with Deep Learning and Ray

Translation between Skeletal formulae and InChI labels is a challenging image captioning problem, especially when it involves large amounts of noisy data. In this blog post we share our experience from the BMS molecular translation challenge and show that CPU-based distributed learning can significantly shorten model training times.

By Jennifer Davis14 min read


Getting Started with Ray

In this blog post we give a quick introduction to Ray. We talk about the architecture and execution model, and present some of Ray's core paradigms such as remote functions and actors.

By Nikolay Manchev12 min read


Spark, Dask, and Ray: Choosing the Right Framework

Apache Spark, Dask, and Ray are three of the most popular frameworks for distributed computing. In this blog post we look at their history, intended use-cases, strengths and weaknesses, in an attempt to understand how to select the most appropriate one for specific data science use-cases.

By Nikolay Manchev15 min read

Data Science

Evaluating Ray: Distributed Python for Massive Scalability

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in additional insights, register for the upcoming Ray Summit.

By Dean Wampler14 min read

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