Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
Oct 12, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A high-throughput and memory-efficient inference and serving engine for LLMs
Run any open-source LLMs, such as Llama 3.1, Gemma, as OpenAI compatible API endpoint in the cloud.
The easiest way to serve AI apps and models - Build reliable Inference APIs, LLM apps, Multi-model chains, RAG service, and much more!
SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
Superduper: Integrate AI models and machine learning workflows with your database to implement custom AI applications, without moving your data. Including streaming inference, scalable model hosting, training and vector search.
Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs
AICI: Prompts as (Wasm) Programs
RayLLM - LLMs on Ray
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
A throughput-oriented high-performance serving framework for LLMs
RTP-LLM: Alibaba's high-performance LLM inference engine for diverse applications.
LLM (Large Language Model) FineTuning
Efficient AI Inference & Serving
This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
Multi-node production AI stack. Run the best of open source AI easily on your own servers. Create your own AI by fine-tuning open source models. Integrate LLMs with APIs. Run gptscript securely on the server
Finetune LLMs on K8s by using Runbooks
Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on accelerating LLMs, currently focusing mainly on inference acceleration, and related works will be gradually added in the future. Welcome contributions!
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