Ray Training Program
Exclusive in-person or virtual Ray training, led by the creators of Ray. Connect with Ray experts to enhance your skills in Ray, GenAI, LLMs, and beyond.
Training Courses
Explore our carefully designed courses, tailored to ensure that every practitioner gains practical, hands-on knowledge.
Ray AI Libraries
Gain a comprehensive overview of Ray, scale common AI workloads, and empower your team to build with Ray.
Build and Optimize ML Platforms with Ray
Learn to leverage Ray to build your ML platform, reduce infra costs, and enable new workloads.
End-to-End LLM Workloads
Build end-to-end LLM workloads with Ray, including fine-tuning, deployment, batch inference, evaluations, and more.
Retrieval Augmented Generation (RAG)
Build, and deploy RAG applications while developing evaluation routines and applying Ray best practices.
Large-Scale Stable Diffusion Models
Enable large-scale data processing and distributed stable diffusion model training.
LLM Inference with Ray and vLLM
Build, tune, and scale high-performance inference workloads with Ray and vLLM.
Ray is the standard for building and scaling
high-performance AI applications.
At OpenAI, Ray allows us to iterate at scale much faster than we could before. We use Ray to train our largest models, including ChatGPT.
“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 1.37 million transactions per second. Ray allowed us to scale elastically to handle this load and to deploy ensembles of models in a fault tolerant manner.”
“Ray has brought significant value to our business, and has enabled us to rapidly pretrain, fine-tune and evaluate our LLMs.”
“Ray enables us to run deep learning workloads 12x faster, to reduce costs by 8x, and to train our models on 100x more data.”
“Ray has profoundly simplified the way we write scaalble distributed programs for Coheres’ LLM pipelines.”
“We use Ray to run a number of AI workloads at Samsara. Since implementing the platform, we’ve been able to scale the training of our deep learning models to hundreds of millions of inputs, and accelerate deploymnet while cutting inference costs by 50%.”
“We were able to improve the scalability by an order of magnitude, reduce the latency by over 90%, and improve the cost efficiency by over 90%. It was financially infeasible for us to approach that problem with any other distributed compute framework.”
