Faster Model Serving with Ray and Anyscale
Faster Model Serving with Ray and Anyscale | Ray Summit 2024
Ray Serve is an industry-leading ML platform for distributed model serving and deployment. In this Ray Summit 2024 breakout session, ...
Roblox Guest Blog: Fast and Efficient Online Model Serving - Anyscale
We show in detail how to make use of Ray, a distributed computing framework for Python, to serve several models on CPU machines. We share ...
ML library for model deployment and serving. Anyscale supports and further optimizes Ray Serve for improved performance, reliability, and scale. Book a Demo
Model Deployment and Serving at Scale - Anyscale
Serving ML models at scale is hard—but Anyscale is built for the challenge. Get simplified development and high-performance distributed compute—all in one ...
Ray Serve is a scalable model serving library for building online inference APIs. Serve is framework-agnostic, so you can use a single toolkit to serve ...
Low-latency Generative AI Model Serving with Ray, NVIDIA Triton ...
Anyscale is teaming with NVIDIA to combine the developer productivity of Ray Serve and RayLLM with the cutting-edge optimizations from ...
Ray: Productionizing and scaling Python ML workloads simply
“Ant Group has deployed Ray Serve on 240,000 cores for model serving. The peak throughput during Double 11, the largest online shipping day in the world, was ...
Training 1 Million ML Models in Record Time | Anyscale
Ray and Anyscale are used by companies like Instacart to speed up machine learning training workloads (often demand forecasting) by 10x ...
Ray Serve: Scalable and Programmable Serving - Ray Docs
Compared to these framework-specific solutions, Ray Serve doesn't perform any model-specific optimizations to make your ML model run faster. However, you ...
Fast model loading - Anyscale Docs
Anyscale provides a library to download model weights saved in safetensors format in remote storage directly to a GPU.
Enabling Cost-Efficient LLM Serving with Ray Serve - YouTube
Ray Serve is the cheapest and easiest way to deploy LLMs, and has served billions of tokens in Anyscale Endpoints. This talk discusses how ...
Ray Serve: Tackling the cost and complexity of serving AI in production
The combination of Ray Serve on Anyscale Services helps you optimize the full serving stack - across the model, application and hardware layers.
Opinions of RAY Framework : r/mlops - Reddit
I am currently working on training a model with Ray at work. We're fans of it but it's not super easy to just spin it up—tuning a multi-node ...
Deploying Many Models Efficiently with Ray Serve - YouTube
Comments1 ; Scaling AI Health Assistants: Challenges and Solutions. Anyscale · 268 views ; Lessons From Fine-Tuning Llama-2. Anyscale · 6.7K views.
RayTurbo: Anyscale's Ray Optimized Runtime
Ray is the AI Compute Engine powering workloads with leading performance. RayTurbo, Anyscale's optimized Ray engine, delivers better performance, scale, ...
Autoscaling Large AI Models up to 5.1x Faster on Anyscale
To demonstrate the effect of these optimizations, we ran an experiment to measure the time taken to scale up a single Ray Serve replica to host ...
How Massive-Scale ML is Made Easy With Ray & Anyscale
How Ray and Anyscale Make it Easy to Do Massive-Scale Machine Learning on Aerial Imagery ... Richard Decal is a machine learning scientist on a ...
Productionizing ML at scale with Ray Serve - YouTube
Comments3 ; Data Processing on Ray. Anyscale · 1.4K views ; Introduction to Model Deployment with Ray Serve. MLOps World: Machine Learning in ...
How to Scale Up Your FastAPI Application Using Ray Serve - Medium
Ray Serve is an infrastructure-agnostic, pure-Python toolkit for serving machine learning models at scale. Ray Serve runs on top of the ...
academy/ray-serve/e2e/tutorial.ipynb at main - GitHub
Ray tutorials from Anyscale. Contribute to anyscale ... 2 Model Serving with Ray Serve¶. Adapted from our documentation. By the end of ...