Stars
The easy-to-use and developer-friendly enterprise CMS powered by Django
cube studio开源云原生一站式机器学习/深度学习/大模型AI平台，支持sso登录，大数据平台对接，notebook在线开发，拖拉拽任务流pipeline编排，多机多卡分布式训练，超参搜索，推理服务VGPU，边缘计算，标注平台，自动化标注，大模型微调，vllm大模型推理，llmops，私有知识库，AI模型应用商店，支持模型一键开发/推理/微调，支持国产cpu/gpu/npu芯片，支持R…
FlashMLA: Efficient MLA decoding kernels
SeaweedFS is a fast distributed storage system for blobs, objects, files, and data lake, for billions of files! Blob store has O(1) disk seek, cloud tiering. Filer supports Cloud Drive, cross-DC ac…
The Triton TensorRT-LLM Backend
TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorR…
A collection of memory efficient attention operators implemented in the Triton language.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A Cloud Native Batch System (Project under CNCF)
FastAPI framework, high performance, easy to learn, fast to code, ready for production
FlagPerf is an open-source software platform for benchmarking AI chips.
A fast reverse proxy to help you expose a local server behind a NAT or firewall to the internet.
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Megvii FILE Library - Working with Files in Python same as the standard library
京东风格的移动端 Vue 组件库，支持多端小程序(A Vue.js UI Toolkit for Mobile Web)
Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
A cloud-native Go microservices framework with cli tool for productivity.
Resource scheduling and cluster management for AI
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
Deep Learning Based Free Mobile Real-Time Face Landmark Detector. Contact:jack-yu-business@foxmail.com
[ICCV 2019] Enhancing Adversarial Example Transferability with an Intermediate Level Attack (https://arxiv.org/abs/1907.10823)