Search Results for "deep network monitoring"
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Showing 3100 open source projects for "deep network monitoring"
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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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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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Nezha Monitoring
Self-hosted, lightweight server and website monitoring and O&M tool
Nezha is a self-hosted monitoring and alerting tool for servers, providing real-time tracking and notifications on server health and performance. -
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Network UPS Tools
The Network UPS Tools repository
Network UPS Tools (NUT) is an open-source solution for monitoring and managing uninterruptible power supplies (UPS) and other power devices across networks. -
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Monitoring Plugins
A suite of Monitoring Plugins (formerly known as nagios-plugins)
Monitoring Plugins is a collection of open-source plugins designed to work with Nagios and other monitoring systems. These plugins perform various checks on hosts and services, providing administrators with essential information about system health and performance. The suite includes a wide range of plugins written in C, Perl, and other languages. -
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Network Flight Simulator
A utility to safely generate malicious network traffic patterns
flightsim is a lightweight utility used to generate malicious network traffic and help security teams to evaluate security controls and network visibility. The tool performs tests to simulate DNS tunneling, DGA traffic, requests to known active C2 destinations, and other suspicious traffic patterns. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. ... -
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imgclsmob Deep learning networks
Sandbox for training deep learning networks
imgclsmob is a deep learning research repository focused on implementing and experimenting with convolutional neural networks for computer vision tasks. The project serves as a sandbox for training and evaluating a wide variety of neural network architectures used in image analysis. It includes implementations of models used for tasks such as image classification, object detection, semantic segmentation, and pose estimation. -
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TrafficMonitor
Floating window used to display current network speed, CPU & memory
TrafficMonitor is a network monitoring software with floating window feature for Windows. It displays the current internet speed and CPU and RAM usage. There are also other capabilities like an embedded display in the taskbar, changeable display skins, and historical traffic statistics. There are two versions of TrafficMonitor, the standard version and the Lite version. -
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TaskExplorer
Powerful system task manager
TaskExplorer is a powerful system task-manager / process-inspector tool designed for Windows that goes well beyond the capabilities of the built-in Task Manager. It provides real-time insights into running processes, threads, open handles, network sockets, memory usage, modules loaded, and more — all in a unified, fast, and efficient GUI. Rather than scattering information across tabs or dialogs, TaskExplorer displays detailed data in a panel layout: when you select a process, the lower panel updates dynamically to show relevant info (threads, handles, sockets, modules, etc.), making deep inspection quick and intuitive. ... -
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SmokePing
The Active Monitoring System
SmokePing is a latency monitoring tool that measures, visualizes, and alerts on network latency using configurable probes such as ping, fping, or HTTP checks. Developed by Tobi Oetiker, it provides detailed, historical graphs of network performance, making it ideal for diagnosing intermittent issues or verifying SLAs. It uses RRDtool for data storage and graphing. -
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. ... -
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Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
...The materials include Jupyter notebooks, explanations of core deep learning concepts, and step-by-step demonstrations of building and training neural networks. Throughout the lessons, users learn how to work with tensors, create neural network architectures, manage training workflows, and evaluate model performance. -
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Datadog Client Libraries for Go
Datadog Go Library including APM tracing, profiling, and security
Datadog Application Performance Monitoring (APM) gives deep visibility into your applications with out-of-the-box performance dashboards for web services, queues, and databases to monitor requests, errors, and latency. Distributed traces seamlessly correlate to browser sessions, logs, profiles, synthetic checks, network, processes, and infrastructure metrics across hosts, containers, proxies, and serverless functions. -
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WatchYourLAN
Lightweight network IP scanner
WatchYourLAN is a local network monitoring tool that displays connected devices, providing insight into network activity and device status. -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
...The fluctuations in stock prices are driven by the forces of supply and demand, which can be unpredictable at times. To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. -
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Netron
Visualizer for neural network, deep learning, machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, Keras, TensorFlow Lite, Caffe, Darknet, Core ML, MNN, MXNet, ncnn, PaddlePaddle, Caffe2, Barracuda, Tengine, TNN, RKNN, MindSpore Lite, and UFF. Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. -
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LibreHardwareMonitor
Monitor temperature sensors, fan speed, voltage, load & clock speeds
...LibreHardwareMonitor supports modern Intel and AMD CPUs, major GPU vendors, storage devices, and network adapters. Built on modern .NET versions, it continues to evolve with frequent updates and broad community contributions. Licensed under MPL 2.0, it offers a transparent and extensible alternative to proprietary hardware monitoring tools. -
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Hubble
Network, Service & Security Observability for Kubernetes using eBPF
Hubble is a fully distributed networking and security observability platform for cloud native workloads. It is built on top of Cilium and eBPF to enable deep visibility into the communication and behavior of services as well as the networking infrastructure in a completely transparent manner. The Linux kernel technology eBPF is enabling visibility into systems and applications at a granularity and efficiency that was not possible before. It does so in a completely transparent way, without... -
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Sniffnet
Application to comfortably monitor your Internet traffic
Application to comfortably monitor your Internet traffic. Multithreaded, cross-platform, and reliable. Sniffnet is completely free, open-source software which needs lots of effort and time to develop and maintain. Save complete textual reports with detailed information for each network connections. Get details about domain names and network providers of the hosts you are exchanging traffic with. -
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Nvitop
An interactive NVIDIA-GPU process viewer and beyond
nvitop is an interactive NVIDIA device and process monitoring tool. It has a colorful and informative interface that continuously updates the status of the devices and processes. As a resource monitor, it includes many features and options, such as tree-view, environment variable viewing, process filtering, process metrics monitoring, etc. Beyond that, the package also ships a CUDA device selection tool nvisel for deep learning researchers. -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms. ... -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
...Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
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Zapret
Advanced open source tool for bypassing DPI-based censorship
Zapret is an open source, cross-platform tool designed to help users bypass and evade Deep Packet Inspection (DPI)-based traffic blocking and censorship systems. Rather than acting as a traditional proxy or VPN, it works by manipulating network packets and traffic streams locally to confuse or disrupt DPI mechanisms used by ISPs and network filters, making it possible to access restricted websites and services without relaying traffic through third-party servers. ... -
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LibreNMS
Community-based GPL-licensed network monitoring system
Welcome to LibreNMS, a fully featured network monitoring system that provides a wealth of features and device support. LibreNMS is an auto-discovering PHP/MySQL/SNMP-based network monitoring that includes support for a wide range of network hardware and operating systems including Cisco, Linux, FreeBSD, Juniper, Brocade, Foundry, HP, and many more. -
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based...
