Search Results for "system linux"
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Showing 34 open source projects for "system linux"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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1
PyTorch
Open source machine learning framework
PyTorch is a Python package that offers Tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on tape-based autograd system. This project allows for fast, flexible experimentation and efficient production. PyTorch consists of torch (Tensor library), torch.autograd (tape-based automatic differentiation library), torch.jit (a compilation stack [TorchScript]), torch.nn (neural networks library), torch.multiprocessing (Python multiprocessing), and... -
2
Porcupine
On-device wake word detection powered by deep learning
Build always-listening yet private voice applications. Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux... -
3
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... -
4
AutoKeras
AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras only support Python 3. If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. AutoKeras would search for the... -
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
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5
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... -
6
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... -
7
PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
8
Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,... -
9
fastai
Deep learning library
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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10
PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
11
Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,... -
12
dlib
Toolkit for making machine learning and data analysis applications
...Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. -
13
Spektral
Graph Neural Networks with Keras and Tensorflow 2
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. Spektral implements some of the most popular layers for... -
14
irs-elcho
Telemetryczny system monitoringu stanu technicznego urządzeń
Celem projektu jest opracowanie telemetrycznego systemu monitoringu stanu technicznego urządzeń typu chłodnie wentylatorowe, który wykorzystując algorytmy typu deep learning będzie stanowił element strategii predykcyjnego utrzymania ruchu. System wpisuje się w ideę Przemysłu 4.0 poprzez umożliwianie monitoringu i predykcji stanu urządzeń. Projekt zakłada badania przemysłowe i prace rozwojowe nad systemem dokonującym kompleksowej akwizycji i analizy danych dot. stanu działania urządzenia.... -
15
Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential... -
16
Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll... -
17
MTBook
Machine Translation: Foundations and Models
This is a tutorial, the purpose is to introduce the basic knowledge and modeling methods of machine translation systematically, and on this basis, discuss some cutting-edge technologies of machine translation (formerly known as "Machine Translation: Statistical Modeling and Deep Learning") method"). Its content is compiled into a book, which can be used for the study of senior undergraduates and graduate students in computer and artificial intelligence related majors, and can also be used as... -
18
BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
19
Neurolinux
Neurolinux, the Deep Learning OS
Neurolinux, the Deep Learning OS, with all necessary tools and libraries pre-installed. Pytorch, Tensorflow Jupyter notebooks...etc -
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Robust Tube MPC
Example implementation for robust model predictive control using tube
robust-tube-mpc is a MATLAB implementation of robust tube-based Model Predictive Control (MPC). The framework provides tools to design and simulate controllers that maintain stability and constraint satisfaction in the presence of bounded disturbances. Tube-based MPC achieves robustness by combining a nominal trajectory planner with an error feedback controller that keeps the actual system state within a "tube" around the nominal trajectory. This repository includes example scripts and... -
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Geroes is a deep learning-based system dedicated to evaluating gene contribution in defining tissue specificity.
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Consistent Depth
We estimate dense, flicker-free, geometrically consistent depth
Consistent Depth is a research project developed by Facebook Research that presents an algorithm for reconstructing dense and geometrically consistent depth information for all pixels in a monocular video. The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a... -
23
Euler
A distributed graph deep learning framework.
As a general data structure with strong expressive ability, graphs can be used to describe many problems in the real world, such as user networks in social scenarios, user and commodity networks in e-commerce scenarios, communication networks in telecom scenarios, and transaction networks in financial scenarios. and drug molecule networks in medical scenarios, etc. Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable... -
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FastoCloud PRO
IPTV/NVR/CCTV/Video cloud https://fastocloud.com
IPTV/Video cloud Features: Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian) GPU/CPU Encode/Decode/Post Processing Stream statistics CCTV Adaptive hls streams Load balancing Temporary urls HLS push EPG scanning Subtitles to text conversions AD insertion Logo overlay Video effects Relays Timeshifts Catchups Playlists Restream/Transcode from online streaming services like Youtube, Twitch ... -
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RL-Stock
Automated stock trading through a simulated training environment
RL-Stock is a reinforcement learning project that explores automated stock trading through a simulated training environment. It is written as an educational experiment rather than a financial product or investment recommendation system. The project includes scripts for collecting stock data, defining a reinforcement learning environment, training an agent, and visualizing results. It focuses on how an agent can learn trading-like behavior through rewards, states, and actions. The repository...
