Search Results for "linux software development" - Page 3
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Showing 127 open source projects for "linux software development"
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Track time for payroll, billing and productivityBecause time is money, and we understand how challenging it can be to keep track of employee hours. The constant reminder to log timesheets so your business can increase billables, run an accurate payroll and remove the guesswork from project estimates – we get it.
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Apify is a full-stack web scraping and automation platform helping anyone get value from the web.Actors are serverless cloud programs that extract data, automate web tasks, and run AI agents. Developers build them using JavaScript, Python, or Crawlee, Apify's open-source library. Build once, publish to Store, and earn when others use it. Thousands of developers do this - Apify handles infrastructure, billing, and monthly payouts.
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VibeTensor
Our first fully AI generated deep learning system
VibeTensor is a groundbreaking open-source research system software stack for deep learning that was uniquely generated almost entirely by AI coding agents under guided human supervision, demonstrating a new frontier in AI-assisted software engineering. It implements a PyTorch-style eager tensor library with a modern C++20 core that supports both CPU and CUDA backends, giving it the ability to manage tensors, automatic differentiation (autograd), and complex computation flows similar to... -
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GROBID
A machine learning software for extracting information
GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. Header extraction and parsing from article in PDF format. The... -
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Rhino
On-device Speech-to-Intent engine powered by deep learning
Rhino is Picovoice's Speech-to-Intent engine. It directly infers intent from spoken commands within a given context of interest, in real-time. The end-to-end platform for embedding private voice AI into any software in a few lines of code. Design with no limits on top of a modular platform. Create use-case-specific voice AI models in seconds. Develop voice features with a few lines of code using intuitive and cross-platform SDKs. Deliver voice AI everywhere: on-device, mobile, web browsers,... -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
This project is a comprehensive, code-first deep learning curriculum built around TensorFlow and Keras, designed to guide learners from foundational concepts to practical model deployment through hands-on experimentation. It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including... -
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DeepSpeed
Deep learning optimization library: makes distributed training easy
DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference. With DeepSpeed you can: 1. Train/Inference dense or sparse models with billions or trillions of parameters 2. Achieve excellent system throughput and efficiently scale to thousands of GPUs 3. Train/Inference on resource constrained GPU systems 4. Achieve unprecedented low latency and high throughput for inference 5. Achieve extreme... -
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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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Adaptive Intelligence
Adaptive Intelligence also known as "Artificial General Intelligence"
Adaptive Intelligence is the implementation of neural science, forensic psychology , behavioral science with machine-learning and artificial intelligence to provide advanced automated software platforms with the ability to adjust and thrive in dynamic environments by combining cognitive flexibility, emotional regulation, resilience, and practical problem-solving skills. -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN... -
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pipeless
A computer vision framework to create and deploy apps in minutes
Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new... -
Electronic Lab Notebook (ELN) SoftwareeLabJournal is an all-in-one Electronic Lab Notebook (ELN) software that includes sample tracking and protocol management modules.
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MELAGE is a neuroimaging software developed for visualizing and processing medical images, both Ultrasound and Magnetic Resonance Images (MRIs). Specially it has been prepared for neuroimaging of newborns, it is a versatile platform that allows the visualization of many types of medical images. It allows to load two, three and four-dimensional images of both techniques and in the case of 3D images it allows the simultaneous visualization of the three orthogonal planes which facilitates the...
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that... -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data... -
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SOD
An Embedded Computer Vision & Machine Learning Library
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well as commercial products.... -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data... -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to... -
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Knet
Koç University deep learning framework
Knet.jl is a deep learning package implemented in Julia, so you should be able to run it on any machine that can run Julia. It has been extensively tested on Linux machines with NVIDIA GPUs and CUDA libraries, and it has been reported to work on OSX and Windows. If you would like to try it on your own computer, please follow the instructions on Installation. If you would like to try working with a GPU and do not have access to one, take a look at Using Amazon AWS or Using Microsoft Azure. If... -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
AirSim is an open-source, cross platform simulator for drones, cars and more vehicles, built on Unreal Engine with an experimental Unity release in the works. It supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. ... -
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Hello AI World
Guide to deploying deep-learning inference networks
Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
SageMaker MXNet Inference Toolkit is an open-source library for serving MXNet models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep... -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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Catalyst
Accelerated deep learning R&D
...Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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The fastai book
The fastai book, published as Jupyter Notebooks
These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository. The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all... -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and...
