Easy and quick Distributed Computing
Develop any application at any scale.
Run the same code on your laptop, on a powerful multi-core machine, on any cloud provider, or on a Kubernetes cluster.
Utilize machine that is scalable libraries from the box for hyperparameter search, reinforcement learning, training, serving, and much more.
Ray is just a distributed execution framework that allows you to scale your applications and to leverage state of the art machine learning libraries.
Powered by Ray
вЂњAnt Financial has generated a multi-paradigm fusion motor along with Ray that combines streaming, graph processing, and machine learning in a single system to do real-time fraud detection and promotion that is online. RayвЂ™s freedom, scalability and efficiency allowed us to process huge amounts of dollars well worth of transactions during Double 11, the largest shopping time in the world.вЂќ
вЂњAt ASAPP, we try out machine learning models each day through our open source framework FlambГ©, and now we ultimately deploy a lot of those models to manufacturing where they provide an incredible number of real time customer interactions. We tried using more generic task distribution frameworks but they didnвЂ™t fit our needs until we found Ray. Using Ray has allowed us to quickly and reliably implement ML that is new t ling scale, and stepped on big groups of devices efficiently, enabling FlambГ© to develop and help our model training for both research and production.вЂќ
вЂњEricsson makes use of Ray to create distributed reinforcement systems that are learning interact with community nodes and simulators with RLlib also to tune device learning models hyper-parameters with Ray tune.вЂќ
вЂњCreating individualized device (chip) screening to reduce test cost, improve quality while increasing capacity for Intel manufacturing and testing process. Advanced Analytics makes use of Ray to speed up and scale their hyperparameter and model selection practices.вЂќ
вЂњAt JP Morgan, we use Ray to power the training of our deep reinforcement learning based trading that is electronic such as вЂ‹LOXMвЂ‹ and DeepX. Ray elements such as for example Tune and RLlib offer easy-to-use foundations and standard implementations to accelerate our research on algorithmic trading strategies.вЂќ
вЂњReal globe applications of reinforcement learning require distributing both training and simulation workloads вЂ“ often across a huge selection of machines. Our autonomous systems platform leverages Ray to accelerate our customersвЂ™ creation of intelligent systems across a diverse group of companies manufacturing that is including energy, smart structures and houses, and process control and automation.вЂќ
вЂњAt Primer AI, we use Ray to parallelize our information processing workflows and analytics pipelines for natural language processing. The serialization that is highly efficient a shared-memory object shop is really a perfect complement managing our data-intensive jobs. The easy-to-use API allows our data scientists to quickly write production-quality parallelized workflows that energy our core products.вЂќ
вЂњWe chose Ray because we needed to train many reinforcement learning agents simultaneously. It had been vital that you us to deliver outcomes quickly to individuals using Pathmind, our product applying reinforcement learning how to company simulations. Ray and RLlib made it an easy task escort service Meridian to do that using distributed compute within the public cloud.вЂќ
Futurewei makes use of Ray in its cloud services making it simple for AI developers to build distributed machine learning models. We use Ray Tune to measure up hyperparameter search jobs for automatic machine learning and usage RLlib make it possible for distributed reinforcement learning training.
Arimo, a Panasonic company, has been effectively applying real-world, enterprise-AI in manufacturing, avionics, automotive, and appliances at scale as a member of a 250,000-employee player that is global. We utilize Ray to power Human-First AI (H1st AI), an open-source framework that addresses the difficulties of collaborative and trust-worthy information science/machine learning. H1st accomplishes this by combining individual and ML models into full execution graphs, showing the workflow that is actual of solutions. Ray is the only real platform flexible enough to supply simple, distributed python execution, allowing H1st to orchestrate many graph instances operating in parallel, scaling sm thly from laptop computers to data centers.
We chose Ray because we needed to train numerous reinforcement learning agents simultaneously. It absolutely was vital that you us to supply outcomes quickly to people utilizing Pathmind, which simulation modelers use to use reinforcement learning to industrial operations and offer chains. We use Ray, RLlib and Ray Serve to ensure that organizations can quickly train RL in the cloud and feed RL-based decisions to their operations for use cases that are priced between optimal warehouse picking to routing autonomous vehicles that are guided.