Technology
Ray
Ray is the open-source AI Compute Engine: a unified, Python-native framework for scaling AI and machine learning workloads from a laptop to a cloud cluster.
Ray is your go-to for scaling Python applications, especially AI/ML. It abstracts distributed systems complexity, letting developers use a simple API (tasks, actors) to parallelize code across a cluster. The core runtime, Ray Core, powers a suite of specialized AI Libraries: Ray Data for scalable ingestion, Ray Train for distributed training, Ray Tune for hyperparameter optimization, Ray Serve for model deployment, and RLlib for reinforcement learning. This unified architecture enables seamless scaling of compute-intensive tasks, including large language models (LLMs) and GenAI, across heterogeneous compute (CPUs, GPUs) on any major cloud (AWS, GCP, Azure).
What builders pair with Ray
Projects using both technologies. Select a pairing to see a project.
12 more pairings
Pairing: Kubernetes
eomer: An Agentic Operating System for Data Science
Pairing: Docker
eomer: An Agentic Operating System for Data Science
Pairing: Python
Easy self-serve Ray Clusters on Kubernetes, no YAML.
Pairing: PyTorch
eomer: An Agentic Operating System for Data Science
Pairing: Amazon S3
Building a Sovereign Multi-GPU AI Infrastructure in a European Data Center (in Less Than One Year)
Pairing: Backend
eomer: An Agentic Operating System for Data Science
Recent Talks & Demos
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