Ray
Ray is an AI compute engine designed to optimize and scale workloads for data processing, model training, and more, simplifying MLOps integration.
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Description
Ray is an advanced AI compute engine designed to manage, execute, and optimize compute needs across various AI workloads, including data processing, model training, and model serving.
It offers a unified infrastructure through a flexible framework that simplifies scaling from local machines to cloud environments. Ray supports a wide range of AI and ML frameworks, making it compatible with major tools in the MLOps ecosystem.
Key components include:
- Ray Data: For efficient data processing.
- Ray Train: For scalable model training.
- Ray Tune: For hyperparameter tuning to improve model performance.
- Ray Serve: For serving machine learning models in production.
- Ray RLlib: For reinforcement learning applications.
With a strong community backing and numerous contributors, Ray is trusted by organizations like OpenAI for scaling AI model training effectively and efficiently.
Features
Flexible Scaling
Ray simplifies the scaling of applications from local run on a single machine to distributed computations across cloud environments.
Broad Framework Support
It supports a variety of AI and ML frameworks, enhancing compatibility with existing tools in the MLOps ecosystem.
Comprehensive Libraries
Ray includes specialized libraries for different AI tasks, covering data processing, model training, hyperparameter tuning, model serving, and reinforcement learning.
Community and Support
Ray has a robust community of contributors and users, providing support and continuous development for the framework.
Tags
Documentation & Support
- Installation
- Documentation
- Support
- Updates
- Online Support