Ray Cluster: Scalable distributed computing for AI workloads
Ray Cluster on Nebius provides a powerful, open-source framework for deploying and managing distributed computing environments, optimized for large-scale AI and machine learning workloads.
Scalability
Easily scale your AI workloads across clusters with dynamic resource adjustment based on demand, seamless expansion from local development to large-scale production and support for multi-node and multi-GPU environments.
Flexibility
Adapt to various ML frameworks with compatibility for popular ML libraries like PyTorch and TensorFlow and support for training/inference/reinforcement learning. Run diverse, large-scale data preparation workloads as part of your pipelines.
Performance
Optimize resource utilization for faster results through efficient task distribution across available resources, built-in GPU acceleration, and advanced scheduling algorithms for improved cluster efficiency.
Simplicity
Streamline deployment and management with the KubeRay operator for easy integration with Kubernetes, intuitive APIs for distributed computing tasks, and simplified cluster setup and configuration.
Observability
Gain insights into cluster performance with built-in monitoring and logging capabilities, real-time cluster state visualization, and easy integration with popular observability tools.
Ecosystem
Leverage a rich ecosystem of tools and libraries with access to Ray’s extensive library of scalable AI/ML applications, integration with popular data processing frameworks, and active community support with continuous improvements.
Leverage Ray Cluster for different sets of tasks
Data preparation
Large-scale machine learning
Inference
Reinforcement learning & simulation
Ready to scale your AI workloads?
Experience the power of distributed computing with Ray Cluster on Nebius.