awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索博客网站地图
项目关于媒体报道MCP 服务器
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
awesome-repositories.com博客
分类

3 个仓库

Awesome GitHub RepositoriesData Persistence

Mechanisms for saving and loading tabular data structures to ensure dataset consistency.

Distinct from Tabular Data Frameworks: Focuses on writing data to disk for preservation, while the parent is a general management framework.

Explore 3 awesome GitHub repositories matching data & databases · Data Persistence. Refine with filters or upvote what's useful.

Awesome Data Persistence GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • morvanzhou/tutorialsMorvanZhou 的头像

    MorvanZhou/tutorials

    12,952在 GitHub 上查看↗

    This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

    Implements mechanisms for saving and loading datasets to disk to ensure data persistence.

    Pythonmachine-learningmultiprocessingneural-network
    在 GitHub 上查看↗12,952
  • autogluon/autogluonautogluon 的头像

    autogluon/autogluon

    9,997在 GitHub 上查看↗

    AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

    Writes data frames to file paths using a consistent format to preserve datasets.

    Pythonautogluonautomated-machine-learningautoml
    在 GitHub 上查看↗9,997
  • hu17889/go_spiderhu17889 的头像

    hu17889/go_spider

    1,821在 GitHub 上查看↗

    Go Spider is a modular framework designed for building concurrent web scrapers and data extraction workflows. It provides a structured engine for orchestrating automated crawling tasks, managing request scheduling, and processing web content through a unified pipeline. The framework distinguishes itself through a highly configurable architecture that allows developers to inject custom logic for downloaders, schedulers, and storage components via interface-driven contracts. It manages network interactions using middleware-based request throttling and URL deduplication, ensuring that crawling o

    Routes processed data items to configurable outputs like console logs or local files.

    Gocrawlergopipeline
    在 GitHub 上查看↗1,821
  1. Home
  2. Data & Databases
  3. Tabular Data Frameworks
  4. Data Persistence