Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
Ray
Scale Python and AI workloads from laptop to cluster effortlessly
Ray provides a unified runtime and libraries to scale data processing, training, hyperparameter tuning, reinforcement learning, and model serving across any infrastructure, from a single machine to large clusters.
Overview
Highlights
Unified core runtime with task, actor, and object abstractions
Scalable AI libraries for data, training, tuning, RL, and serving
Flexible deployment on laptops, clusters, cloud, or Kubernetes
Integrated monitoring via Ray Dashboard and Distributed Debugger
Pros
- Seamless scaling from local to large clusters
- Rich ecosystem of purpose‑built AI libraries
- Active community and extensive documentation
- Language‑native Python experience
Considerations
- Steeper learning curve for distributed concepts
- Runtime overhead can affect very small workloads
- Primarily Python‑centric, limited native support for other languages
- Debugging distributed failures may require additional effort
Managed products teams compare with
When teams consider Ray, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Data scientists needing to scale ML pipelines
- Engineers building distributed training jobs
- Teams deploying real‑time model serving at scale
- Researchers running large‑scale reinforcement learning experiments
Not ideal when
- Projects confined to single‑node execution
- Non‑Python workloads without a compatible API
- Environments with strict latency budgets and no tolerance for overhead
- Users seeking a fully managed SaaS solution without infrastructure handling
How teams use it
Distributed Hyperparameter Tuning
Find optimal model parameters across hundreds of CPUs in minutes using Ray Tune.
Large‑Scale Data Preprocessing
Process terabytes of data efficiently with Ray Data pipelines on a cluster.
Real‑Time Model Serving
Deploy scalable, low‑latency inference services using Ray Serve.
Reinforcement Learning at Scale
Run parallel RL simulations with RLlib to accelerate research cycles.
Tech snapshot
Python74%
C++19%
Java2%
TypeScript1%
Starlark1%
Cython1%
Frequently asked questions
What programming languages does Ray support?
Ray’s core APIs are Python‑first; other languages can interact via client libraries or RPC.
How do I install Ray?
Install with `pip install ray`; nightly wheels are available via the official installation page.
Can Ray run on Kubernetes?
Yes, Ray includes native support for deploying clusters on Kubernetes.
Does Ray provide monitoring tools?
Ray ships with the Ray Dashboard and a Distributed Debugger for real‑time observability.
Is Ray free for commercial use?
Ray is open source and can be used in commercial projects without licensing fees.
Project at a glance
Active- Stars
- 40,880
- Watchers
- 40,880
- Forks
- 7,135
LicenseApache-2.0
Repo age9 years old
Last commityesterday
Primary languagePython
Last synced yesterday