Search Results for "network performance" - Page 3
Sort By:
Showing 115 open source projects for "network performance"
View related business solutions-
Build Data Resilience - Take the Assessment TodayIs your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.
-
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.
-
1
DirectXDiagnostic Tool Opener
Open DirectXDiagnostic Tool without any commands
Comprehensive Hardware Analysis, Monitoring and Reporting for Windows. Exhausting information about hardware components displayed in hierarchy unfolding into deep details. Useful for obtaining a detailed hardware inventory report or checking of various hardware-related parameters. Real-time monitoring of a variety of system and hardware parameters covering CPUs, GPUs, mainboards, drives, peripherals, etc. Useful for detection of overheating, overload, performance loss or failure... -
2
picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. -
3
Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
4
DDoS/Dos Attack Simulator
Distributed Denial of Service Attack Simulator
DDoS/DoS Attack Simulator is powerful Python-based software used for attacking servers, hosts, and websites using traffic. It disrupts the normal traffic of a targeted server, service, or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic. A server that does not have protection against it can experience extremely slow performance due to all of the traffic it sends. Extensive features will be added! Mega Feature - > DoS Tool. [ 1 ] Bugs Fixes. [ 2 ] Increase in Optimization... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
5
LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the... -
6
Shennina
Automating Host Exploitation with AI
Shennina is an automated host exploitation framework. The mission of the project is to fully automate the scanning, vulnerability scanning/analysis, and exploitation using Artificial Intelligence. Shennina is integrated with Metasploit and Nmap for performing the attacks, as well as being integrated with an in-house Command-and-Control Server for exfiltrating data from compromised machines automatically. Shennina scans a set of input targets for available network services, uses its AI engine... -
7
Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The... -
8
Text Classification
All kinds of text classification models and more with deep learning
Text Classification is a deep learning repository focused on text classification models for NLP. It provides a broad set of baseline architectures that can be used to study, train, compare, and adapt classification approaches. The project supports both single-label and multi-label classification, making it useful for sentence-level and document-level tasks. It includes classic and advanced models such as fastText, TextCNN, BERT, TextRNN, RCNN, hierarchical attention networks, seq2seq... -
9
ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
10
nlpaug
Data augmentation for NLP
This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together. -
11
Envelop (Risk and Audits software)
Governance, Risk, and Compliance Management Tool
Envelop is a management tool that focuses on governance, risk and compliance processes and documentation. Auditing and managing audit workflows is the main feature of this tool. Envelop is an audit software, that helps with internal and external audits to be managed. The common usage of this tool would be for audits, e.g. financial audits done by internal or external auditors. The overall structure follows this hierarchy : Process > Objective > Risk > Control > Test > Finding You... -
12
PaddleGAN
PaddlePaddle GAN library, including lots of interesting applications
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on. PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and supports developers to quickly build, train and deploy GANs for academic, entertainment, and industrial usage. GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. ... -
13
BPYTOP
Linux/OSX/FreeBSD resource monitor
BPYTOP is a feature-rich, terminal-based resource monitor written in Python 3 that provides a highly visual overview of system performance. It displays real-time usage and statistics for CPU, memory, disks, network, and processes, with colorful graphs and widgets that update at configurable intervals. Users can drill into a process list, sort by various metrics, view tree hierarchies, and quickly spot heavy resource consumers. The tool is highly configurable through both an in-app options menu and a detailed configuration file, allowing customization of themes, update frequency, graph types, temperature sensors, and which “boxes” (CPU, memory, network, processes) are shown. ... -
14
OpenEMM e-mail & marketing automation
software for email automation (newsletters, transaction mails, etc.)
This is an archive for old versions of OpenEMM. You will find the latest version of OpenEMM here: https://wiki.openemm.org OpenEMM is a browser-based enterprise application for email automation like info and marketing newsletters, transaction mails or multi-stage email campaigns. OpenEMM offers tons of features for professional users, among them: a great user interface, template-based HTML mailings, automatic bounce management, mail opening and link tracking, lots of graphical realtime... -
15
YOLOR
implementation of paper - You Only Learn One Representation
...YOLOR includes model configurations, training code, evaluation scripts, inference tools, and pretrained weights. Its central contribution is the use of implicit knowledge to improve network performance without treating every task as fully separate. It is useful for computer vision researchers and developers studying YOLO-style detectors, representation learning, and high-performance detection systems. -
16
Robust Video Matting (RVM)
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX
...Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications. RVM is specifically designed for robust human video matting. -
17
Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
18
YOLOv4-large
Scaled-YOLOv4: Scaling Cross Stage Partial Network
...The project provides a PyTorch implementation of the Scaled-YOLOv4 framework, which extends the original YOLOv4 architecture using Cross Stage Partial (CSP) networks and new scaling techniques. Unlike earlier object detection systems that only scale depth or width, this architecture scales multiple aspects of the neural network including structure, resolution, and channel configuration. This scaling strategy enables the model to adapt to different hardware environments while maintaining a strong balance between speed and detection accuracy. The repository includes multiple model variants such as YOLOv4-tiny, YOLOv4-CSP, and large-scale configurations designed for high-performance detection tasks. -
19
Liferay Portal
The world's leading open source portal
Liferay Portal is the world's leading enterprise open source portal framework, offering integrated Web publishing and content management, an enterprise service bus and service-oriented architecture, and compatibility with all major IT infrastructure. Check GitHub for our latest releases: https://github.com/liferay/liferay-portal/releases https://github.com/liferay/liferay-ide/releases -
20
speedtest-cli
Command line interface for testing internet bandwidth using speedtest
...It is a relative value used for determining the lowest latency server for performing the actual speed test against. Speedtest CLI brings the trusted technology and global server network behind Speedtest to the command line. Measure internet connection performance metrics like download, upload, latency and packet loss natively without relying on a web browser. Test the internet connection of your Linux desktop, a remote server or even lower-powered devices such as the Raspberry Pi with the Speedtest Server Network. ... -
21
CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings. ... -
22
ruia
Async Python framework for fast and flexible web scraping spiders
Ruia is an asynchronous web scraping micro-framework built for Python that focuses on simplicity, speed, and flexibility when creating web crawlers. Ruia is powered by Python’s asyncio library along with aiohttp, enabling developers to perform concurrent network requests efficiently and scrape data from websites with minimal overhead. Ruia follows a “write less, run faster” philosophy, emphasizing concise code and streamlined spider development. It provides a structured approach to building scraping projects through components such as data items, spiders, middleware, and plugins. Developers can define structured fields to extract information from HTML content and process responses asynchronously to improve crawling performance. ... -
23
Beamology
Beamology: Semicom Project Management
Beamology is the leading field of project management for SuperWikia Alpha semicom codesmiths. Its residency programs offer vital resources for SuperWikia vertical environment development and utilities needed for SA-VEC certification. Beamology residents are automatically licensed to access the Transcom system. -
24
EfficientNet Keras
Implementation of EfficientNet model. Keras and TensorFlow Keras
...In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. -
25
Pretty Damn Quick (PDQ) analytically solves queueing network models of computer and manufacturing systems, data networks, etc., written in conventional programming languages. Generic or customized reports of predicted performance measures are output.
