Ray
Ray is an AI compute engine that consists of a core distributed runtime and a set of AI libraries for accelerating ML workloads. It is designed to be general-purpose, meaning that it can performantly run any kind of workload. Ray allows users to seamlessly scale the same code from a laptop to a cluster, making it a unified way to scale Python and AI applications. The core distributed runtime provides a set of key abstractions, including tasks, actors, and objects, which enable efficient and scalable execution of AI workloads. The AI libraries, including Data, Train, Tune, RLlib, and Serve, provide a range of functionalities for simplifying ML compute, such as scalable datasets, distributed training, hyperparameter tuning, reinforcement learning, and serving. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations.
- Category
- LLM
- Pricing
- Open Source
Problem solved
Ray solves the problem of scaling AI and Python applications from a laptop to a cluster, which is increasingly important as ML workloads become more compute-intensive. By providing a unified framework for scaling, Ray enables developers to focus on writing code rather than worrying about infrastructure, making it easier to develop and deploy AI applications.
Use cases
- Distributed training of machine learning models
- Scalable hyperparameter tuning
- Reinforcement learning
- Serving of machine learning models
- Data processing and analysis
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Scalable and performant
- General-purpose and flexible
- Easy to use and integrate
- Growing ecosystem of community integrations
Cons
- Steep learning curve
- Requires significant resources for large-scale deployment
- Limited support for non-Python languages
Official website: https://github.com/ray-project/ray
Canonical LambdaBase page: https://www.lambdabase.com/tools/ray