Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.
 
Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
 
Ray Core provides a simple and flexible API for building and running your distributed applications. You can often parallelize single machine code with little to zero code changes.
 
With a Ray cluster you can deploy your workloads on AWS, GCP, Azure or on premise. You can also use Ray cluster managers to run Ray on your existing Kubernetes, YARN, or Slurm clusters.

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.


Ray - A unified framework for scaling AI and Python applications

Rayon is a data-parallelism library for Rust. It is extremely lightweight and makes it easy to convert a sequential computation into a parallel one. It also guarantees data-race freedom. Rayon makes it drop-dead simple to convert sequential iterators into parallel ones: usually, you just change your foo.iter() call into foo.par_iter(), and Rayon does the rest.
 
Parallel iterators take care of deciding how to divide your data into tasks; it will dynamically adapt for maximum performance. If you need more flexibility than that, Rayon also offers the join and scope functions, which let you create parallel tasks on your own. For even more control, you can create custom threadpools rather than using Rayon's default, global threadpool.

Rayon is a data-parallelism library for Rust. It is extremely lightweight and makes it easy to convert a sequential computation into a parallel one. It also guarantees data-race freedom. Rayon makes it drop-dead simple to convert sequential iterators into parallel ones: usually, you just change your foo.iter() call into foo.par_iter(), and Rayon does the rest.

Rayon - A data parallelism library for Rust

The Stratosphere System is an open-source cluster/cloud computing framework for Big Data analytics. It comprises of An extensible higher level language (Meteor) to quickly compose queries for common and recurring use cases, A parallel programming model (PACT, an extension of MapReduce) to run user-defined operations, An efficient massively parallel runtime (Nephele) for fault tolerant execution of acyclic data flows. 
 
Stra­tosphere (named after the layer of the atmosphere above the clouds) explores the power of massively parallel computing for complex information manage­ment applications.

The Stratosphere System is an open-source cluster/cloud computing framework for Big Data analytics. It comprises of An extensible higher level language (Meteor) to quickly compose queries for common and recurring use cases, A parallel programming model (PACT, an extension of MapReduce) to run user-defined operations, An efficient massively parallel runtime (Nephele) for fault tolerant execution of acyclic data flows. 

StratoSphere - Cloud Computing Framework for Big Data Analytics

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Ray - A unified framework for scaling AI and Python applications

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