Search Results for "modules" - Page 22
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Showing 3048 open source projects for "modules"
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Scene Framework
Android Single Activity Framework compatible with Fragment
Scene appears to be a ByteDance-hosted project — though at first glance its name is generic, implying it may relate to “scenes,” “rendering,” “storyboarding,” or perhaps “event handling.” Given ByteDance’s broad portfolio, Scene could be an internal or external library for structuring application “scenes” (UI, media, game, or module-level) or orchestrating workflows in a modular fashion. The repository may aim to help developers manage complex state, transitions, or UI/navigation flows in... -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
PokeeResearchOSS provides an open-source, agentic “deep research” model centered on a 7B backbone that can browse, read, and synthesize current information from the web. Instead of relying only on static training data, the agent performs searches, visits pages, and extracts evidence before forming answers to complex queries. It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository... -
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Playground Cheatsheet for Python
Playground and cheatsheet for learning Python
learn-python is another repository by Oleksii Trekhleb that serves as both a playground and an interactive cheatsheet for learning Python. It contains numerous Python scripts organized by topic (lists, dictionaries, loops, functions, classes, modules, etc.), each with code examples, explanations, test assertions, and links to further readings. The design supports “learn by doing”: you can modify the code, run the tests, see how behavior changes, and thus internalize Python language features, idioms, and good style practices (including linting and PEP8). Because it is organized in bite-sized chunks, it’s ideal for beginners or people refreshing their Python skills who want to revisit syntax and common patterns before moving into larger frameworks or applications. ... -
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fvcore
Collection of common code shared among different research projects
...It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A standout capability is FLOP and activation counting, which analyzes arbitrary PyTorch graphs to report cost by operator and by module for precise profiling. The file I/O layer (PathManager) abstracts local/remote storage so the same code can read from disks, cloud buckets, or HTTP endpoints. ... -
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FlashMLA
FlashMLA: Efficient Multi-head Latent Attention Kernels
FlashMLA is a high-performance decoding kernel library designed especially for Multi-Head Latent Attention (MLA) workloads, targeting NVIDIA Hopper GPU architectures. It provides optimized kernels for MLA decoding, including support for variable-length sequences, helping reduce latency and increase throughput in model inference systems using that attention style. The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to... -
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NGINX Admin’s Handbook
How to improve NGINX performance, security, and other important things
nginx-admins-handbook is a practical, in-depth guide for configuring, securing, and operating NGINX across real-world deployments. It distills years of research, notes, and field experience into a single handbook that complements the official docs with concrete rules, explanations, and curated external references. The handbook spans fundamentals and advanced topics alike, from HTTP and SSL/TLS basics to reverse proxy patterns, performance tuning, debugging workflows, and hardening... -
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Th3inspector
Best Tool For Information Gathering
...It exposes a single command-line entrypoint (Th3inspector.pl) with many switches for common reconnaissance tasks — examples include website info, whois, MX lookup, geo-IP, subdomain discovery, CMS detection, port scanning, and Cloudflare real-IP resolution — so a user can chain many checks from one script. The project provides simple install wrappers for Linux, Android/Termux and even instructions for Windows (Perl + CPAN modules), and includes helper scripts to update or install dependencies automatically. The repository is actively packaged as versioned releases in the README (current noted version 1.9) and is distributed under an MIT license. -
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Scalaz
Principled Functional Programming in Scala
Scalaz is a foundational functional-programming library for Scala that provides type classes, data types, and syntax to write pure, composable code. It implements classic abstractions such as Functor, Applicative, Monad, Monoid, Foldable, and Traverse, along with powerful transformers (ReaderT, StateT, WriterT, OptionT, and more) to structure effects. The library offers rich data structures—\/ (disjunction), Validation, NonEmptyList, IList, and Free—that help model errors, invariants, and... -
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Pry
A runtime developer console and IRB alternative
Pry is a powerful alternative to Ruby’s default REPL that doubles as a runtime exploration and debugging toolkit. It offers command-driven navigation of objects and modules, syntax-highlighted input, command history, and the ability to switch contexts or “cd” into objects to inspect their methods and state. Features like show-source and show-doc reveal the implementation or documentation of methods without leaving the console, while edit-method lets you jump into an editor, modify code, and reload it on the fly. ... -
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Gradle Git Version
A Gradle plugin that uses `git describe` to produce a version string
...It works well in branching workflows by incorporating branch or distance information, making debug artifacts easy to trace back to source. By centralizing version logic around Git history, it eliminates manual bumps and reduces mistakes when cutting releases across multiple modules. -
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Vavr
Object-functional library that runs with Java 8+
...It is the best way to get started with Vavr. Vavr is open source and can be found on Github. It is distributed under the Apache 2.0 License. There are several additional modules that group around Vavr's core. All of these are supported by the Vavr open-source organization. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. -
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Axon
Nx-powered Neural Networks
...You should be able to use any of the APIs without dependencies on others. By decoupling the APIs, Axon gives you full control over each aspect of creating and training a neural network. At the lowest-level, Axon consists of a number of modules with functional implementations of common methods in deep learning. -
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TorchRec
Pytorch domain library for recommendation systems
...Pipelined training overlaps dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance. Optimized kernels for RecSys powered by FBGEMM. Quantization support for reduced precision training and inference. Common modules for RecSys. -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
...Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging... -
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Sming
Open source framework simplifying the creation of C++ applications
...Integrated host emulator to assist with developing, testing, and debugging libraries and applications on a PC before uploading them to an actual microcontroller. Built-in powerful wireless modules. Compatible with standard Libraries use popular hardware in a few lines of code. Simple yet powerful hardware API wrappers. HardwareSPI library provides advanced asynchronous SPI device master capability including the use of ESP8266 ‘overlapped’ mode for reduced pin usage and dual/quad I/O support. Modular C++ installable file system interface. -
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DIY Split-Flap Display
DIY split-flap display
This is a work in progress split-flap display. Each module can flip between 40 unique characters: letters, numbers, and a few symbols. Multiple modules fit perfectly alongside each other to build bigger displays. The primary design goal was to make something that's possible to fabricate at home in small or single quantities and can be customized and built by an intermediate hobbyist at a reasonable price. This meant using widely available materials and avoiding any tooling with a high upfront cost. ... -
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webpack DevServer
Serves a webpack app and updates the browser on changes
...This set of options is picked up by webpack-dev-server and can be used to change its behavior in various ways. When the server is started, there will be a message prior to the list of resolved modules. If you're using dev-server through the Node.js API, the options in devServer will be ignored. Pass the options as a second parameter instead: new WebpackDevServer(compiler, {...}). Be aware that when exporting multiple configurations only the devServer options for the first configuration will be taken into account and used for all the configurations in the array. ... -
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node-gyp
Node.js native addon build tool
node-gyp is a cross-platform command-line tool written in Node.js for compiling native addon modules for Node.js. It contains a vendored copy of the gyp-next project that was previously used by the Chromium team, extended to support the development of Node.js native addons. Note that node-gyp is not used to build Node.js itself. Multiple target versions of Node.js are supported (i.e. 0.8, ..., 4, 5, 6, etc.), regardless of what version of Node.js is actually installed on your system (node-gyp downloads the necessary development files or headers for the target version). node-gyp requires that you have installed a compatible version of Python, one of: v3.6, v3.7, v3.8, or v3.9. ... -
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roblox-ts
A TypeScript-to-Luau Compiler for Roblox
roblox-ts is an attempt to bridge the abilities of TypeScript to work in a Roblox environment. We break down your code into an abstract syntax tree and emit functionally similar structures in Luau so that the code behaves the same. -
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
NVIDIA NeMo is a scalable, cloud-native generative AI framework aimed at researchers and PyTorch developers working on large language models, multimodal models, and speech AI (ASR and TTS), with growing support for computer vision. It provides collections of domain-specific modules and reference implementations that make it easier to pre-train, fine-tune, and deploy very large models on multi-GPU and multi-node infrastructure. NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. The framework builds on PyTorch Lightning–style modular abstractions, so training scripts are composed from reusable components for data loading, models, optimizers, and schedulers, which simplifies experimentation and adaptation. ... -
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Project MINI RACK
Miniature rack builds, for portable or compact Homelabs
Project MINI RACK is a project dedicated to designing, documenting, and showcasing compact, portable rack-mount or small-form factor “mini” labs, suitable for home labs, edge deployments, or mobile network stacks. The repository contains build guides, hardware lists, layout ideas (10” wide racks, 1U/2U modules, custom shelves), 3D printable components, and real-world examples of mini homelab rigs. Its goal is to help hobbyists, devops engineers, or tinkerers understand how to build efficient, space-conscious infrastructure rather than large server racks. It also covers the ecosystem around small racks: power distribution units (PDUs), patch panels, cable management, 3D-printed rack shelves, and integration of SBCs, mini-PCs, switches, and storage. ... -
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TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
This repo is an optimized CUDA version of FIt-SNE algorithm with associated python modules. We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. You can install binaries with anaconda for CUDA version 10.1 and 10.2 using conda install tsnecuda -c conda-forge. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. ... -
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Particle Firmware for the Electron, P1
Device OS (Firmware) for particle devices
The Photon uses an ARM Cortex M3 CPU based microcontroller. All of the code is built around the GNU GCC toolchain offered and maintained by ARM. The build requires version 5.3.1 20160307 or newer of ARM GCC and will print an error message if the version is older than this. In order to turn your source code into binaries, you will need a tool called make. Windows users need to explicitly install make on their machines. Make sure you can use it from the terminal window. The entire Particle... -
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Ansible-lint
Best practices checker for Ansible
Ansible Lint is a command-line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain. Ansible lint is also supposed to help users upgrade their code to work with newer versions of Ansible. Due to this reason we recommend using it with the newest version of Ansible, even if the version used in production...
