Search Results for "network performance" - Page 3
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Showing 92 open source projects for "network performance"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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.
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Angular Performance Checklist
Cheatsheet for developing lightning fast progressive Angular apps
Angular Performance Checklist is a practical resource and “cheatsheet” aimed at helping Angular (or AngularJS/Angular-related) developers optimize web application performance, covering both network/load-time optimizations and runtime performance improvements. It outlines actionable recommendations — from bundling and minification, tree-shaking, lazy loading, ahead-of-time (AoT) compilation, resource prefetching, caching and compression, to runtime strategies like change-detection optimization (OnPush, detaching change detectors), minimizing DOM operations, optimizing templates, using pure pipes, minimizing watchers, and more. ... -
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Darknet
Convolutional Neural Networks
...With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. Its simplicity, combined with powerful capabilities, has made Darknet one of the most influential projects in the computer vision community. -
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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. -
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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. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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TNN
Uniform deep learning inference framework for mobile
TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. -
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NATS HTTP Round Tripper
This is a Golang http.RoundTripper that uses NATS as a transport
...It can be an effective bridge for teams migrating from synchronous REST calls to a message-driven fabric without rewriting every call site. Because the transport is swappable, it promotes clean separation between business logic and the underlying network. The result is an incremental pathway to evented architectures with the ergonomics of conventional request/response programming. -
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Android Tech Frontier
Translates high-quality Android technology, open source libraries
Android Tech Frontier is a curated, comprehensive digest of cutting-edge Android engineering knowledge, architecture patterns, system internals, performance practices, and technical discussions — aimed at intermediate to advanced Android developers who want to deepen their understanding of the platform. The repository aggregates articles, analysis, and mini-tutorials that explore topics such as ART and Dalvik internals, memory management, rendering pipelines, custom view performance optimization, threading and concurrency on Android, JNI interactions, and network stack behavior. ... -
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sharppcap
ully managed, cross platform
Fully managed, cross-platform (Windows, Mac, Linux) .NET library for capturing packets from live and file-based devices -
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The Neural Process Family
This repository contains notebook implementations
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from... -
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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VivaGraph
Graph drawing library for JavaScript
VivaGraphJS is a powerful, high-performance graph drawing library for JavaScript that enables developers to visualize complex networks directly in the browser or in Node.js environments. It is designed for speed and scalability, handling large graph datasets with smooth rendering and interactive capabilities such as dragging nodes and zooming. The library supports multiple rendering backends including SVG, WebGL, and Canvas, allowing developers to choose the best balance of performance and visual fidelity for their use case. ... -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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pytorch-examples
Simple examples to introduce PyTorch
...It also serves as a quick reference for common patterns and techniques used in deep learning workflows. The project aligns with PyTorch’s philosophy of combining usability with performance and flexibility. Overall, pytorch-examples is an essential learning resource for anyone working with PyTorch. -
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printThis
jQuery printing plugin; print specific elements on a page
...Accepts custom CSS/jQuery selectors. The amount of time to wait before calling print() in the printThis iframe. Defaults to 1000 milliseconds. Appropriate values depend heavily on the content and network performance. Graphics heavy, slow, or uncached content may need extra time to load. Use a custom page title on the iframe. This may be reflected on the printed page, depending on the settings. Blank by default. Copies style attributes from the body and html tags into the printThis iframe. Added to provide support for CSS Variables. ... -
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Mars
A cross-platform network component developed by WeChat
...It consists of four main parts: Comm, which contains a basic library, including basic tools like sockets, threads, alarm, message queues, and coroutines; Xlog, which provides high-performance, high-availability, security, and fault-tolerant log functions; SDT which is the network diagnosis module; and STN or signaling transmission network module, the major component of Mars responsible for the small data signaling channel between the terminal and the server. Mars has been proven effective by billions of WeChat users. ... -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
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Simd
High performance image processing library in C++
The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. -
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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EventMachine
EventMachine, fast, simple event-processing library for Ruby programs
...It provides event-driven I/O using the Reactor pattern, much like JBoss Netty, Apache MINA, Python's Twisted, Node.js, libevent and libev. Extremely high scalability, performance and stability for the most demanding production environments. An API that eliminates the complexities of high-performance threaded network programming, allowing engineers to concentrate on their application logic. This unique combination makes EventMachine a premier choice for designers of critical networked applications, including Web servers and proxies, email and IM production systems, authentication/authorization processors, and many more. ... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. ... -
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swarmprom
Docker Swarm instrumentation with Prometheus, Grafana, cAdvisor
...It collects host, container, and Docker daemon metrics through Node Exporter, cAdvisor, and a dedicated daemon exporter. Grafana dashboards display cluster capacity, node health, service tasks, CPU, memory, storage, network traffic, I/O, and Prometheus performance. Alertmanager distributes alerts, Unsee provides an alert dashboard, and optional Slack settings deliver notifications to a channel. Global services and Docker DNS discovery automatically cover exporter instances added with new nodes. The project is intended as a foundation that teams can extend and harden for their own production environments. -
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Synaptic
Architecture-free neural network library for node.js and the browser
Synaptic is a javascript neural network library for node.js and the browser, its generalized algorithm is architecture-free, so you can build and train basically any type of first order or even second order neural network architectures. This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an Embedded Reber Grammar test, so you can easily test and compare the performance of different architectures. ... -
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OracleClientDAC for Delphi
A feature-rich and high-performance data access components for Oracle
OracleClient Data Access Components (OracleClientDAC) is a feature-rich and high-performance library of components that provides native connectivity to Oracle from Delphi. OracleClientDAC-based applications connect to Oracle directly through the ADO.Net OracleClient, which is an Oracle Data Provider for .NET (ODP.Net). OracleClientDAC aims to assist programmers in developing of fast and native Oracle database applications. OracleClientDAC allows developers to take advantage of advanced... -
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SqlClientDAC for Delphi
A feature-rich and high-performance data access components for MS SQL
SqlClient Data Access Components (SqlClientDAC) is a feature-rich and high-performance library of components that provides native connectivity to SQL Server from Delphi. SqlClientDAC-based applications connect to SQL Server directly through the ADO.Net SqlClient, which is a .NET Framework Data Provider for SQL Server. SqlClientDAC is designed to help programmers develop faster and cleaner SQL Server database applications. SqlClientDAC uses its own protocol to communicate with SQL Server.... -
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OLEDBDAC for Delphi
Provides data access for data sources exposed through OLE DB.
OLE DB Data Access Components (OLEDBDAC) is a feature-rich and high-performance library of components that provides data access for data sources exposed through OLE DB from Delphi for both 32-bit and 64-bit Windows platforms. OLEDBDAC-based applications connects to any data source exposed through OLE DB using the .NET Framework Data Provider for OLE DB. OLEDBDAC aims to assist programmers in developing of fast and native database applications whose data source is exposed through OLE DB....
