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
May 13, 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 2, Mistral, as OpenAI compatible API endpoint in the cloud.
The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.
AICI: Prompts as (Wasm) Programs
Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs
RayLLM - LLMs on Ray
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
Efficient AI Inference & Serving
RTP-LLM: Alibaba's high-performance LLM inference engine for diverse applications.
LLM (Large Language Model) FineTuning
This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
Finetune LLMs on K8s by using Runbooks
🪶 Lightweight OpenAI drop-in replacement for Kubernetes
Run GPU inference and training jobs on serverless infrastructure that scales with you.
A GPT-3.5 & GPT-4 Workload Trace to Optimize LLM Serving Systems
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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