Tag: Ray

AI Kubernetes Machine Learning Ray Vertex AI Nov. 11, 2024

Serving Models with Ray Serve - Ray Serve is a scalable model serving library built on Ray. It allows you to deploy and serve machine learning models at scale, handling incoming requests with low latency while dynamically scaling model replicas.

AI Infrastructure Machine Learning Ray Oct. 28, 2024

Running Training Jobs with Ray Jobs - Ray Jobs, an open-source framework, simplifies distributed computing for machine learning workloads. With Ray Jobs, you can define your training job, submit it to a Ray cluster, and monitor its progress using the Ray Dashboard. Ray Jobs automates cluster management, enabling scalable computation and cost efficiency by terminating clusters after job completion.

Google Kubernetes Engine Official Blog Ray Sept. 23, 2024

Accelerate Ray in production with new Ray Operator on GKE - The Ray Operator on Google Kubernetes Engine (GKE) simplifies the deployment and management of Ray clusters for distributed AI/ML workloads. It offers declarative APIs, integrated logging and monitoring, TPU support, and features to reduce startup latency. With Ray on GKE, organizations can scale their AI applications efficiently and reliably, taking advantage of GKE's managed container orchestration service.

AI Google Kubernetes Engine Kubernetes Ray Sept. 16, 2024

Intro to Ray on GKE - An overview of Ray and Ray Operator for GKE.

Official Blog Ray Vertex AI May 20, 2024

Announcing general availability of Ray on Vertex AI

Cloud SQL Google Kubernetes Engine Official Blog Ray May 6, 2024

RAG in production faster with Ray, LangChain and HuggingFace - A quickstart solution and reference architecture for retrieval augmented generation (RAG) applications, designed to accelerate your journey to production on Google Kubernetes Engine (GKE), and Cloud SQL for PostgreSQL and pgvector, using Ray, LangChain, and Hugging Face.