Showing 893 open source projects for "void-linux"

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  • 1
    elasticsearch-learning-to-rank

    elasticsearch-learning-to-rank

    Plugin to integrate Learning to Rank

    The Elasticsearch Learning to Rank plugin uses machine learning to improve search relevance ranking. It's powering search at places like Wikimedia Foundation and Snagajob.
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  • 2
    The Operator Splitting QP Solver

    The Operator Splitting QP Solver

    The Operator Splitting QP Solver

    OSQP uses a specialized ADMM-based first-order method with custom sparse linear algebra routines that exploit structure in problem data. The algorithm is absolutely division-free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works. OSQP has an easy interface to generate customized embeddable C code with no memory manager required. OSQP supports many interfaces including C/C++, Fortran, Matlab, Python, R, Julia, Rust.
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  • 3
    Advanced Solutions Lab

    Advanced Solutions Lab

    This repos contains notebooks for the Advanced Solutions Lab

    This repository contains Jupyter notebooks meant to be run on Vertex AI. This is maintained by Google Cloud’s Advanced Solutions Lab (ASL) team. Vertex AI is the next-generation AI Platform on the Google Cloud Platform. The material covered in this repo will take a software engineer with no exposure to machine learning to an advanced level.
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  • 4
    Autodistill

    Autodistill

    Images to inference with no labeling

    Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. You can use Autodistill on your own hardware, or use the Roboflow hosted version of Autodistill to label images in the cloud.
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  • 5
    Alibi Explain

    Alibi Explain

    Algorithms for explaining machine learning models

    Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.
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  • 6
    KerasTuner

    KerasTuner

    A Hyperparameter Tuning Library for Keras

    KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search...
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  • 7
    Linfa

    Linfa

    A Rust machine learning framework

    linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust. Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks.
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  • 8
    TensorFlow Datasets

    TensorFlow Datasets

    TFDS is a collection of datasets ready to use with TensorFlow,

    TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets.
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  • 9
    Burn

    Burn

    Burn is a new comprehensive dynamic Deep Learning Framework

    Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. Burn emphasizes performance, flexibility, and portability for both training and inference. Developed in Rust, it is designed to empower machine learning engineers and researchers across industry and academia.
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  • 10
    MLDatasets.jl

    MLDatasets.jl

    Utility package for accessing common Machine Learning datasets

    This package represents a community effort to provide a common interface for accessing common Machine Learning (ML) datasets. In contrast to other data-related Julia packages, the focus of MLDatasets.jl is specifically on downloading, unpacking, and accessing benchmark datasets. Functionality for the purpose of data processing or visualization is only provided to a degree that is special to some datasets.
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  • 11
    OneFlow

    OneFlow

    OneFlow is a deep learning framework designed to be user-friendly

    OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous...
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  • 12
    Aerosolve

    Aerosolve

    A machine learning package built for humans

    Aerosolve is an open-source machine learning library developed by Airbnb, designed for interpretable and human-friendly modeling. Built around sparse, human-intuitive features (like geography, pricing), it supports feature quantization, interaction specification, and rule-based priors—enabling domain experts to contribute directly to model behavior.
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  • 13
    DiffEqFlux.jl

    DiffEqFlux.jl

    Pre-built implicit layer architectures with O(1) backprop, GPUs

    DiffEqFlux.jl is a Julia library that combines differential equations with neural networks, enabling the creation of neural differential equations (neural ODEs), universal differential equations, and physics-informed learning models. It serves as a bridge between the DifferentialEquations.jl and Flux.jl libraries, allowing for end-to-end differentiable simulations and model training in scientific machine learning. DiffEqFlux.jl is widely used for modeling dynamical systems with learnable...
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  • 14
    NeuralPDE.jl

    NeuralPDE.jl

    Physics-Informed Neural Networks (PINN) Solvers

    NeuralPDE.jl is a Julia library for solving partial differential equations (PDEs) using physics-informed neural networks and scientific machine learning. Built on top of the SciML ecosystem, it provides a flexible and composable interface for defining PDEs and training neural networks to approximate their solutions. NeuralPDE.jl enables hybrid modeling, data-driven discovery, and fast PDE solvers in high dimensions, making it suitable for scientific research and engineering applications.
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  • 15
    Llama Cookbook

    Llama Cookbook

    Solve end to end problems using Llama model family

    The Llama Cookbook is the official Meta LLaMA guide for inference, fine‑tuning, RAG, and multi-step use-cases. It offers recipes, code samples, and integration examples across provider platforms (WhatsApp, SQL, long context workflows), enabling developers to quickly harness LLaMA models
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  • 16
    Tensorforce

    Tensorforce

    A TensorFlow library for applied reinforcement learning

    Tensorforce is an open-source deep reinforcement learning framework built on TensorFlow, emphasizing modularized design and straightforward usability for applied research and practice.
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  • 17
    PyKEEN

    PyKEEN

    A Python library for learning and evaluating knowledge graph embedding

    PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information). PyKEEN is a Python package for reproducible, facile knowledge graph embeddings. PyKEEN has a function pykeen.env() that magically prints relevant version information about PyTorch, CUDA, and your operating system that can be used for debugging. If you’re in a Jupyter Notebook, it will be pretty-printed as an HTML table.
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  • 18
    PyGAD

    PyGAD

    Source code of PyGAD, Python 3 library for building genetic algorithms

    PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems. PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function.
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  • 19
    Elyra

    Elyra

    Elyra extends JupyterLab with an AI centric approach

    Elyra is a set of AI-centric extensions to JupyterLab Notebooks. The Elyra Getting Started Guide includes more details on these features. A version-specific summary of new features is located on the releases page.
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  • 20
    huggingface_hub

    huggingface_hub

    The official Python client for the Huggingface Hub

    The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine-learning apps hosted on the Hub. You can also create and share your own models, datasets, and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python.
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  • 21
    cuML

    cuML

    RAPIDS Machine Learning Library

    cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU...
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  • 22
    gensim

    gensim

    Topic Modelling for Humans

    Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. The target audience is the natural language processing (NLP) and information retrieval (IR) community.
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  • 23
    TorchRec

    TorchRec

    Pytorch domain library for recommendation systems

    TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel,...
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  • 24
    OpenRLHF

    OpenRLHF

    An Easy-to-use, Scalable and High-performance RLHF Framework

    OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures.
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  • 25
    MiniSom

    MiniSom

    MiniSom is a minimalistic implementation of the Self Organizing Maps

    MiniSom is a minimalistic and Numpy-based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. 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...
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