Search Results for "linux-gnome" - Page 11
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Showing 1024 open source projects for "linux-gnome"
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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AutoMLOps
Build MLOps Pipelines in Minutes
AutoMLOps is a service that generates, provisions, and deploys CI/CD integrated MLOps pipelines, bridging the gap between Data Science and DevOps. AutoMLOps provides a repeatable process that dramatically reduces the time required to build MLOps pipelines. The service generates a containerized MLOps codebase, provides infrastructure-as-code to provision and maintain the underlying MLOps infra, and provides deployment functionalities to trigger and run MLOps pipelines. AutoMLOps gives... -
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AtomAI
Deep and Machine Learning for Microscopy
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless... -
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its... -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for... -
Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Determined
Determined, deep learning training platform
The fastest and easiest way to build deep learning models. Distributed training without changing your model code. Determined takes care of provisioning machines, networking, data loading, and fault tolerance. Build more accurate models faster with scalable hyperparameter search, seamlessly orchestrated by Determined. Use state-of-the-art algorithms and explore results with our hyperparameter search visualizations. Interpret your experiment results using the Determined UI and TensorBoard, and... -
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. RecBole is developed based on Python and PyTorch for... -
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Vowpal Wabbit
Machine learning system which pushes the frontier of machine learning
Vowpal Wabbit is a machine learning system that pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state-of-the-art algorithms with performance in mind. The input format for... -
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CVPR 2025
Collection of CVPR 2025 papers and open source projects
CVPR 2025 curates accepted CVPR 2025 papers and pairs them with their corresponding code implementations when available, giving researchers and practitioners a fast way to move from reading to reproducing. It organizes entries by topic areas such as detection, segmentation, generative models, 3D vision, multi-modal learning, and efficiency, so you can navigate the year’s output efficiently. Each paper entry typically includes a title, author list, and links to the paper PDF and official or... -
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mlr3
mlr3: Machine Learning in R - next generation
mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via... -
Free and Open Source HR SoftwareGive your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++... -
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eos
A lightweight 3D Morphable Face Model library in modern C++
eos is a lightweight 3D Morphable Face Model fitting library that provides basic functionality to use face models, as well as camera and shape fitting functionality. It's written in modern C++11/14. MorphableModel and PcaModel classes to represent 3DMMs, with basic operations like draw_sample(). Supports the Surrey Face Model (SFM), 4D Face Model (4DFM), Basel Face Model (BFM) 2009 and 2017, and the Liverpool-York Head Model (LYHM) out-of-the-box. -
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Kubeflow pipelines
Machine Learning Pipelines for Kubeflow
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. A pipeline is a description of an ML workflow, including all of the components in the workflow and how they combine in the form of a graph. The pipeline includes the definition of the inputs (parameters) required to run the pipeline and the inputs and outputs of each component. A pipeline component is a self-contained set of user code, packaged as... -
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UpTrain
Your open-source LLM evaluation toolkit
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison... -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote... -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major... -
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Tribuo
Tribuo - A Java machine learning library
Tribuo* is a machine learning library written in Java. It provides tools for classification, regression, clustering, model development, and more. It provides a unified interface to many popular third-party ML libraries like xgboost and liblinear. With interfaces to native code, Tribuo also makes it possible to deploy models trained by Python libraries (e.g. scikit-learn, and pytorch) in a Java program. Tribuo is licensed under Apache 2.0. Remove the uncertainty around exactly which artifacts... -
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Hivemind
Decentralized deep learning in PyTorch. Built to train models
Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. Distributed training without a master node: Distributed Hash Table allows connecting computers in a decentralized network. Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too long to respond. Decentralized parameter... -
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Opacus
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source,... -
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Vearch
A distributed system for embedding-based vector retrieval
Vearch is the vector search infrastructure for deep learning and AI applications. Vearch is a distributed vector storage and retrieval system which can be easily extended to billions scale. Vearch implements a high-performance, lockless real-time vector indexing subsystem that utilizes various optimization techniques to support millisecond vector update and retrieval. End-to-end one-click deployment. Through the module of the plugin, a complete default visual search system can be deployed... -
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Metarank
A low code Machine Learning service that personalizes articles
Metarank is a service that can personalize any type of content: product listings, articles, recommendations and search results in 3 easy steps with a few lines of code. It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Metarank makes it easy not only for Amazon to do personalization but for everyone else. Ingest historical item listings, clicks and item metadata so Metarank... -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate... -
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AIF360
A comprehensive set of fairness metrics for datasets
This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it. The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. The... -
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FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to... -
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High-Level Training Utilities Pytorch
High-level training, data augmentation, and utilities for Pytorch
Contains significant improvements, bug fixes, and additional support. Get it from the releases, or pull the master branch. This package provides a few things. A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data... -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used...
