Ray (software)
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| Ray (software) | |
|---|---|
| Name | Ray |
| Developer | Anyscale |
| Released | 2017 |
| Programming language | Python, C++ |
| Operating system | Linux, macOS, Windows (via WSL) |
| License | Apache License 2.0 |
Ray (software) is an open-source distributed computing framework designed to scale Python and other language workloads from a laptop to large clusters. It provides primitives for parallel and distributed execution, enabling applications in machine learning, data processing, reinforcement learning, and robotics. Ray integrates task scheduling, actor semantics, and distributed object storage to simplify development of scalable, fault-tolerant systems.
History
Ray originated from research at the University of California, Berkeley's RoboLab and projects in the Berkeley AI Research (BAIR) lab, with contributions from researchers associated with MIT and Stanford University. Early work drew on ideas from projects at Google like MapReduce and Borg while reflecting influences from Apache Spark and Dask. Founders later formed Anyscale to commercialize the technology, and the project gained backing from investors including Andreessen Horowitz, Sequoia Capital, and NEA. Ray development has seen contributions from engineers with prior roles at Facebook, Amazon Web Services, and Microsoft Research. The project matured alongside competing frameworks such as Kubernetes-orchestrated systems and TensorFlow distributed runtimes. Over successive releases Ray integrated with ecosystems represented by PyTorch, TensorFlow, XGBoost, and Horovod, and was adopted in research from institutions like Carnegie Mellon University and Stanford University. Major milestones include integration with Kubernetes operators, the launch of Anyscale cloud offerings, and collaborations with cloud providers including Google Cloud Platform and Amazon Web Services.
Architecture and Components
Ray's architecture centers on a distributed control plane and worker processes, combining ideas from Actor model implementations and task graphs used in Apache Flink. Key components include a global control store inspired by Google Spanner and a distributed object store akin to designs from Ceph and Redis. The scheduler implements hierarchical strategies resembling solutions in Mesos and YARN, while remote functions and actor APIs parallel concepts from Erlang and Akka. Ray integrates with container orchestration systems such as Kubernetes and resource managers used by Slurm and HTCondor. Storage backends and data movement leverage technologies such as Apache Arrow and Parquet, and networking considerations often invoke protocols and libraries from gRPC and ZeroMQ.
Features and Capabilities
Ray provides primitives for task-parallel programming, actor-based stateful services, and flexible placement strategies comparable to capabilities in Celery and MPI. It offers libraries for reinforcement learning (RLlib) with algorithms studied at DeepMind and OpenAI, hyperparameter tuning (Tune) used in workflows popularized by NeurIPS and ICML, and distributed data processing (Dataset) aligned with patterns from Apache Spark SQL. Ray's streaming and scheduling features are used alongside tools like Kafka and RabbitMQ, and checkpointing integrates with systems such as S3, Google Cloud Storage, and HDFS. Security and authentication practices relate to standards used by OAuth and TLS, and observability often connects to stacks including Prometheus and Grafana.
Use Cases and Applications
Ray is used for large-scale training in contexts associated with ImageNet benchmarks and language modeling popularized by GPT-3 research. Robotics labs at institutions like Massachusetts Institute of Technology and ETH Zurich use Ray for simulation and control loops. Autonomous vehicle companies influenced by work at Waymo and Cruise use distributed simulation and perception pipelines that can employ Ray. In finance, firms following practices from Goldman Sachs and JPMorgan Chase use Ray for risk simulations and backtesting. Healthcare applications mirror studies from Johns Hopkins University and Mayo Clinic for distributed inference. Gaming companies influenced by Unity Technologies and Epic Games use Ray for scaling multiplayer simulation and reinforcement learning. Ray also underpins experimentation platforms used in ad tech firms adopting pipelines similar to those at Google Ads and Meta Platforms.
Performance and Scalability
Ray's performance characteristics have been benchmarked against systems such as Dask and Apache Spark, and its actor model draws comparisons to latency profiles seen in Erlang systems. Scalability efforts reference cluster management patterns pioneered by Borg and Kubernetes, and improvements often involve optimizations similar to those in RDMA-accelerated frameworks. For model-parallel and data-parallel training, Ray integrates with accelerators produced by NVIDIA and AMD, and distributed GPU scheduling considers technologies like CUDA and ROCm. Research comparisons sometimes use standard workloads from MLPerf and datasets such as CIFAR-10 and COCO for evaluation. Fault tolerance strategies recall designs from Raft and Paxos consensus research.
Adoption and Ecosystem
Ray's ecosystem includes libraries such as RLlib, Tune, and Serve, and integrates with machine learning frameworks like PyTorch, TensorFlow, and Scikit-learn. Cloud integrations are available through providers including Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Commercial and open-source tooling from organizations such as Anyscale, Databricks, and Hopsworks build on Ray for orchestration and developer experiences. Community contributions emerge from groups associated with UC Berkeley, CMU, and corporate labs at Intel and IBM Research. Conferences where Ray has been presented include KubeCon, NeurIPS, and Strata Data Conference, and training materials have appeared in workshops organized by O'Reilly Media and ACM.