Search Results for "rings-code" - Page 21
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Showing 2297 open source projects for "rings-code"
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FullTClash
General proxy performance testing tool based on Clash using Telegram
Back end part useClash project(It can also be called nowmihomo)The relevant code is used as the outing agent. The front end part uses Telegram API as the interactive interface, which needs to be used in conjunction with Telegram, that is, a Telegram robot (bot), FullTClash bot is a Telegram robot (hereinafter referred to as bot) carrying its test tasks. -
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MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
...Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details. The project initially aimed for a minimalistic implementation of the Self-Organizing Map (SOM) algorithm, focusing on simplicity in features, dependencies, and code style. Although it has expanded in terms of features, it remains minimalistic by relying only on the numpy library and emphasizing vectorization in coding style. -
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Shapash
Explainability and Interpretability to Develop Reliable ML models
Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria. -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
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imodelsX
Interpretable prompting and models for NLP
Interpretable prompting and models for NLP (using large language models). Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better... -
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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uvloop
Ultra fast asyncio event loop
uvloop is an ultra-fast, drop-in replacement of the built-in asyncio event loop. Together with asyncio and the power of async/await in Python 3.5, uvloop makes it easier than ever to write high-performance Python networking code. uvloop makes asyncio incredibly fast-- 2 to 4 times faster than nodejs, or any other Python asynchronous framework. The performance of asyncio when it is uvloop-based is almost comparable to that of Go programs. uvloop is written in Cython and is built on top of libuv, a high performance, fast and stable multiplatform asynchronous I/O library used by nodejs. -
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CycleGAN and pix2pix in PyTorch
Image-to-Image Translation in PyTorch
...This repo gives developers and researchers a convenient, modern (PyTorch-based) platform to train and test these methods — supporting both paired datasets (input to output) and unpaired datasets (domain-to-domain) with minimal changes. The code supports standard training and inference pipelines, and as of recent updates, compatibility with the latest Python and PyTorch versions (e.g. Python 3.11, PyTorch 2.4) as well as support for distributed/multi-GPU training for scalable workflows. Because of its flexibility, users can apply it to many tasks: e.g. style transfer between domains (e.g. season changes, art-to-photo, etc.), mapping sketches/edges to real images, image colorization, day-to-night, photo enhancement, and more. -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
...It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple languages and voicepacks and allows phoneme based generation for more accurate pronunciation and prosody. The server also offers per-word timestamped captions, which makes it useful for creating subtitles or aligning audio with text. ... -
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Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
Mesh R-CNN is a 3D reconstruction and object understanding framework developed by Facebook Research that extends Mask R-CNN into the 3D domain. Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh... -
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Thinc
A refreshing functional take on deep learning
...Develop faster and catch bugs sooner with sophisticated type checking. Trying to pass a 1-dimensional array into a model that expects two dimensions? That’s a type error. Your editor can pick it up as the code leaves your fingers. -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
...Spend less time manually tracking results in spreadsheets and text files. Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models. Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights. Set wandb.config once at the beginning of your script to save your hyperparameters, input settings (like dataset name or model type), and any other independent variables for your experiments. This is useful for analyzing your experiments and reproducing your work in the future. ... -
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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... -
14
Vedo
A python module for scientific analysis of 3D data
...Inspired by the vpython manifesto "3D programming for ordinary mortals", vedo makes it easy to work with 3D pointclouds, meshes and volumes, in just a few lines of code, even for less experienced programmers. vedo is based on VTK and numpy, with no other dependencies. Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud). Export meshes as ASCII or binary to VTK, STL, OBJ, PLY formats. Analysis tools like Moving Least Squares, mesh morphing and more. ... -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
...The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. Further, it is easy to fine-tune your own models. Our models are evaluated extensively and achieve state-of-the-art performance on various tasks. Further, the code is tuned to provide the highest possible speed. -
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FastAgency
The fastest way to bring multi-agent workflows to production
FastAgency is a framework that simplifies the creation and deployment of AI-driven automation agents. It provides a structured environment for developing AI assistants capable of handling various business and technical tasks. -
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TaskWeaver
A code-first agent framework for seamlessly planning analytics tasks
TaskWeaver is a multi-agent AI framework designed for orchestrating autonomous agents that collaborate to complete complex tasks. -
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Bot Framework SDK for Python
Build and connect intelligent bots that interact naturally
This repository contains code for the Python version of the Microsoft Bot Framework SDK, which is part of the Microsoft Bot Framework - a comprehensive framework for building enterprise-grade conversational AI experiences. This SDK enables developers to model conversation and build sophisticated bot applications using Python. SDKs for JavaScript and .NET are also available. -
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I hate money
A simple shared budget manager web application
...I hate money is written in python, using the flask framework. It’s developed with ease of use in mind and is trying to keep things simple. Hope you (will) like it! The code is distributed under a BSD beerware derivative: if you meet the people in person and you want to pay them a craft beer, you are highly encouraged to do so. -
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nbdev
Create delightful software with Jupyter Notebooks
nbdev is a notebook-driven development platform (by fast.ai/AnswerDotAI) enabling you to write code, tests, documentation, and deploy software, all from Jupyter Notebooks. It provides a unified literate programming workflow where you can tag notebook cells for export to Python modules, auto-generate documentation via Quarto (and host it on GitHub Pages), run tests embedded in notebooks, manage clean notebooks with Git-friendly metadata hooks, and seamlessly publish packages to PyPI/conda, all while keeping source and documentation in sync. -
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smolagents
Agents write python code to call tools and orchestrate other agents
This library is the simplest framework out there to build powerful agents. We provide our definition in this page, where you’ll also find tips for when to use them or not (spoilers: you’ll often be better off without agents). smolagents is a lightweight framework for building AI agents using large language models (LLMs). It simplifies the development of AI-driven applications by providing tools to create, train, and deploy language model-based agents. -
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GraalPy
A Python 3 implementation built on GraalVM
...It has first-class support for embedding in Java and can turn Python applications into fast, standalone binaries. GraalPy is ready for production running pure Python code and has experimental support for many popular native extension modules. -
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Slack Machine
A simple, yet powerful and extendable Slack bot
...More than just a bot, Slack Machine is a framework that helps you develop your Slack workspace into a ChatOps powerhouse. Slack Machine is built with an intuitive plugin system that lets you build bots quickly but also allows for easy code organization. A plugin can look as simple as this: -
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RenderCV
LaTeX CV generator from a YAML/JSON input file
RenderCV is a LaTeX CV/resume framework. It allows you to create a high-quality CV as a PDF from a YAML file with full Markdown syntax support and complete control over the LaTeX code. RenderCV offers built-in LaTeX and Markdown templates ready to produce high-quality CVs. However, the templates are entirely arbitrary and can easily be updated to leverage RenderCV's capabilities with your custom CV themes. -
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Pretty Jupyter
Creates dynamic html report from jupyter notebook.
Pretty Jupyter is an easy-to-use package that allows to create beautiful & dynamic HTML reports. Most of the features require little to no work to get working and greatly improve the quality of the output report, or even the developer’s comfort when creating the report. For example, tabs make some visualizations much more comfortable. The features are integrated directly into the output page, therefore there is no need to have an interpreter running in the backend. This makes the HTML easily...
