#计算机科学#Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
翻译 - 一个快速简单的框架,用于构建和运行分布式应用程序。 Ray与RLlib(可扩展的强化学习库)和Tune(可扩展的超参数调整库)打包在一起。
#计算机科学#A flexible, high-performance serving system for machine learning models
翻译 - 用于机器学习模型的灵活,高性能的服务系统
#搜索#AI + Data, online. https://vespa.ai
翻译 - Vespa是用于对大数据集进行低延迟计算的引擎。
#计算机科学#An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
翻译 - 用于将生产机器学习部署,管理和扩展到数千个模型的框架
#计算机科学#In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
#计算机科学#TorchServe是一个高性能、灵活且易于使用的工具,用于在生产级环境中提供PyTorch模型的服务。
#安卓#⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end ...
#计算机科学#Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.
#大语言模型#Database system for AI-powered apps
#计算机科学#TensorFlow template application for deep learning
翻译 - TensorFlow模板应用程序用于深度学习
#计算机科学#A comprehensive guide to building RAG-based LLM applications for production.
#前端开发#DELTA is a deep learning based natural language and speech processing platform. LF AI & DATA Projects: https://lfaidata.foundation/projects/delta/
翻译 - DELTA是基于深度学习的自然语言和语音处理平台。
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 ...
#大语言模型#RayLLM - LLMs on Ray
#计算机科学#A flexible, high-performance carrier for machine learning models(『飞桨』服务化部署框架)
#计算机科学#Generic and easy-to-use serving service for machine learning models
#计算机科学#A scalable inference server for models optimized with OpenVINO™
#自然语言处理#Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
#计算机科学#Lineage metadata API, artifacts streams, sandbox, API, and spaces for Polyaxon