shidianshifen Goto Github PK

followers: 1.0 following: 2.0 repos: 29.0 gists: 0.0

Name: shidianshifen

Type: User

Company: Chinaunicom Digital Tech

Bio: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated.

Location: Beijing

shidianshifen's Projects

affine

There can be more than Notion and Miro. AFFiNE is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable and ready to use.

airflow-dag-creation-manager-plugin

A plugin for Airflow that create and manage your DAG with web UI.

casaos

CasaOS - A simple, easy-to-use, elegant open-source Personal Cloud system.

chatglm-6b

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

colossalai

Making large AI models cheaper, faster and more accessible

datahub

The Metadata Platform for the Modern Data Stack

graph-learn

An Industrial Graph Neural Network Framework

graphscope

🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba 来自阿里巴巴的一站式大规模图计算系统图分析图查询图机器学习

hadoop

Apache Hadoop

imageai

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

knowledge-repo

A next-generation curated knowledge sharing platform for data scientists and other technical professions.

metadata-hive-hook

Hive hook, obtain task information from Hive, fetch input/output tables and lineage information from HSQL.

minio

Multi-Cloud :cloud: Object Storage

mlflow

Open source platform for the machine learning lifecycle

moss

An open-source tool-augmented conversational language model from Fudan University

nanocube

photon

Crawler which extracts URLs, files, intel & endpoints from a target and is ridiculously fast.

pythonrobotics

Python sample codes for robotics algorithms.

ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.