zhoujianwen

周健文's starred repositories

sealos

Sealos is a production-ready Kubernetes distribution that provides a one-stop solution for both public and private cloud. You can run any Docker image on sealos, start high availability databases like mysql/pgsql/redis/mongo, develop backend applications using node.js serverless

Language:TypeScriptApache-2.013405 190 1692

cpp-httplib

A C++ header-only HTTP/HTTPS server and client library

Language:C++MIT12395 184 1312

psutil

Cross-platform lib for process and system monitoring in Python

Language:PythonBSD-3-Clause10115 234 1724

concurrentqueue

A fast multi-producer, multi-consumer lock-free concurrent queue for C++11

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L-ink_Card

Smart NFC & ink-Display Card

Language:CGPL-3.07249 309 129

tensorflow_template_application

TensorFlow template application for deep learning

Language:PythonApache-2.01867 186 40

LabelMeAnnotationTool

Source code for the LabelMe annotation tool.

Language:JavaScriptMIT1382 64 93

gpushare-scheduler-extender

GPU Sharing Scheduler for Kubernetes Cluster

Language:GoApache-2.01357 39 147

simple_tensorflow_serving

Generic and easy-to-use serving service for machine learning models

Language:JavaScriptApache-2.0757 30 76

terracotta

A light-weight, versatile XYZ tile server, built with Flask and Rasterio :earth_africa:

Language:PythonMIT659 22 208

WorldWideWeb

Last publicly available revision of the world's first web browser. This is a source import from 0.15 for NextStep. Originally written by @timbl.

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WebRtc.NET

WebRTC for C# & C++/CLI

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pyresample

Geospatial image resampling in Python

Language:PythonLGPL-3.0344 16 215

filemap

File-Based Map-Reduce. Zero-install: easily use any collection of computers as a map-reduce cluster for command-line analytics.

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rtmpsharp

a fast and lightweight data-oriented rtmp(s) client library. now with .net core support.

Language:C#MIT210 370

dask-image

Distributed image processing

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flaskDemo

使用 Python+Flask+MySQL+Redis 开发简单接口实例

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apiAutoTest

Python+Requests+jsonpath+xlrd接口自动化测试工具，实现数据依赖，支持restful规范，sql断言以及测试前后数据隔离操作,自定义扩展方法，可作用于用例当中；video https://www.bilibili.com/video/BV1rr4y1r754/?vd_source=f824feef5305252d9a349520313fd4db

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page-ruler-redux

An awesome page ruler extension for google chrome

Language:JavaScriptBSD-3-Clause110 7 45

tensorflow-serving-tutorial

A tutorial of building tensorflow serving service from scratch

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grpc-nebula-c

微服务治理框架C++实现

Language:C++Apache-2.079 8 3

cog-best-practices

Best practices with cloud-optimized-geotiffs (COGs)

Language:Jupyter NotebookBSD-3-Clause77 17 8

consistent-hashing

Consistent Hashing implementation in Python

Language:Jupyter Notebook29 2 1

monitor-python

该项目为硬件实时监控系统，应用python、mysql、tornado、sqlalchemy、psutil、pyecharts等技术打造！

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tf_serving_cpp_client

C++ client of a GAN model hosted by TensorFlow Serving

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GDAL_Test

This project is mainly used to test the use of GDAL.

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grpc-nebula-samples-c

微服务治理框架使用示例(C++版本)

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image_cutting

Predicting DL model outputs for large images by cutting to smaller chunks

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RasterIOCut

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GraphN-GraphM-improved-version

随着现实世界中图处理需求的快速增长，大量迭代图处理作业同时在同一基础图上运行。而现有的并发图分析处理系统存在大量冗余数据存储和访问开销现有的并发图分析处理系统的存储系统则存在块表信息过大，对内存的利用效率不高等方面的问题，这些问题的原因一方面是块表信息的数据结构不合理，另一方面是块表信息过大后造成挤占可用内存空间的问题。为解决此问题我们在现有的并发图分析处理系统的存储系统GraphM上实现了包括块表信息存储结构改进，块表优先级调度策略，轻量级的内存管理系统等多项重大改进。改进后的系统称作GraphN。实验表明：通过优化块表信息的数据结构，减少非必要的文件读写开销，将会造成更多读写开销的块缓存在内存中等方式，GraphN的块表信息仅占GraphM块表信息大小的千分之一，且同条件下同一任务所需时间缩短20%以上。

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