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 24, 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 multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.
A universal scalable machine learning model deployment solution
Serve, optimize and scale PyTorch models in production
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
AI + Data, online. https://vespa.ai
A high-performance inference system for large language models, designed for production environments.
A flexible, high-performance serving system for machine learning models
A scalable inference server for models optimized with OpenVINO™
An Alternative for Triton Inference Server. Boosting DL Service Throughput 1.5-4x by Ensemble Pipeline Serving with Concurrent CUDA Streams for PyTorch/LibTorch Frontend and TensorRT/CVCUDA, etc., Backends
Docs for torchpipe: https://github.com/torchpipe/torchpipe
Lineage metadata API, artifacts streams, sandbox, API, and spaces for Polyaxon
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Database system for AI-powered apps
A REST API for vLLM, production ready
A flexible, high-performance carrier for machine learning models(『飞桨』服务化部署框架)
RayLLM - LLMs on Ray
ClearML - Model-Serving Orchestration and Repository Solution
Friendli: the fastest serving engine for generative AI
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