Search Results for "memory usage" - Page 9
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Showing 656 open source projects for "memory usage"
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WGCLOUD
Visibility into servers, applications, and infrastructure
...Built on a Spring Boot microservices foundation with an agent-server model, the system emphasizes rapid deployment, minimal configuration overhead, and automated operation for large-scale environments. It collects extensive host metrics such as CPU usage, temperature, memory utilization, disk performance, network throughput, and hardware health while also supporting monitoring of processes, containers, ports, and databases. The platform includes advanced operational capabilities such as web-based SSH access, batch command execution, and visual topology generation, making it more than a passive monitoring tool. wgcloud is intended for private, self-hosted deployments where organizations want full control over monitoring data without relying on external cloud services. -
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Checkmate
Checkmate is an open-source, self-hosted tool
...The application continuously checks whether websites and services are accessible and performing optimally, generating alerts and reports when availability or performance degrades. It supports detailed infrastructure monitoring through an optional agent called Capture, which collects hardware metrics such as CPU, memory, disk usage, and temperature from remote machines. The system is designed to scale, having been stress-tested with more than a thousand active monitors without major performance issues. Checkmate also includes incident tracking and notification capabilities so teams can quickly respond to outages or anomalies. Overall, the platform serves as a comprehensive, developer-friendly alternative to commercial uptime and infrastructure monitoring tools. -
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observer_cli
Visualize Erlang/Elixir Nodes On The Command Line
...Increments are values that are mostly useful when compared to a previous one to have an idea of what they're doing because otherwise, they'd never stop increasing: bytes in and out of the node, number of garbage collector runs, words of memory that were garbage collected, and the global reductions count for the node. Total scheduler utilization will equal 1.0 when all schedulers have been active all the time between the two refresh intervals. The result being that there is a decent chunk of CPU usage that would be mostly free for scheduling actual Erlang work (assuming the schedulers are busy waiting more than trying to select tasks to run). -
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Dozzle
Realtime log viewer for containers. Supports Docker, Swarm and K8s
...The interface includes practical quality-of-life features like fuzzy searching for containers, regex log search, split-screen viewing for multiple logs, and live stats such as CPU and memory usage. It supports more advanced analysis through an in-browser SQL query engine for querying logs, which helps when you need structured filtering without exporting data elsewhere. Dozzle also supports multi-user authentication and can integrate with proxy-forward authorization setups for deployments behind gateways. For larger environments, it can monitor multiple hosts via an agent mode and supports orchestrators like Docker Swarm and Kubernetes. -
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RISC-V sandboxing library
The fastest RISC-V sandbox
RISC-V sandboxing library is a high-performance, ultra-low-latency RISC-V userspace emulator library written in modern C++, designed for embedding and sandboxing applications. Unlike full-system emulators, it focuses specifically on executing user-space programs, making it ideal for scenarios such as sandboxed execution, scripting engines, and high-performance server environments. The library is engineered to achieve extremely fast startup and execution times, with the ability to run large... -
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LLM-Pruner
On the Structural Pruning of Large Language Models
LLM-Pruner is an open-source framework designed to compress large language models through structured pruning techniques while maintaining their general capabilities. Large language models often require enormous computational resources, making them expensive to deploy and inefficient for many practical applications. LLM-Pruner addresses this issue by identifying and removing non-essential components within transformer architectures, such as redundant attention heads or feed-forward... -
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Torch Pruning
DepGraph: Towards Any Structural Pruning
Torch-Pruning is an open-source toolkit designed to optimize deep neural networks by performing structural pruning directly within PyTorch models. The library focuses on reducing the size and computational cost of neural networks by removing redundant parameters and channels while maintaining model performance. It introduces a graph-based algorithm called DepGraph that automatically identifies dependencies between layers, allowing parameters to be pruned safely across complex architectures.... -
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Autocxx
Tool for safe ergonomic Rust/C++ interop driven from existing C++
autocxx is an open source Rust tool and procedural macro designed to enable automatic, safe interoperability between C++ and Rust. It integrates the functionality of bindgen (for parsing and generating Rust bindings from C++ headers) with cxx (for safe and idiomatic Rust–C++ communication), automating the creation of bridges between the two languages. With autocxx, developers can include C++ headers directly in Rust source code and automatically generate bindings for the corresponding C++... -
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tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai
...If you require any of the dependencies that is not installed, tsai will ask you to install it when necessary) We've also added a new PredictionDynamics callback that will display the predictions during training. This is the type of output you would get in a classification task. New tutorial notebook on how to train your model with larger-than-memory datasets in less time achieving up to 100% GPU usage! See our new tutorial notebook on how to track your experiments with Weights & Biases -
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HTTP Kit
Clojure HTTP server/client library with WebSocket support
...It comfortably handles tens of thousands of requests/sec on even midrange hardware. Here is another test about how it stacks up with others. It's not only fast, but efficient! Each connection costs nothing but a few kB of memory. RAM usage grows O(n) with connections. Written from the ground-up to be lean, the entire client/server is available as a single ~90kB JAR with zero dependencies and ~3k lines of (mostly Java) code. Synchronous is simple. Asynchronous is fast & flexible. -
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Alternative JSON parser for Go
One of the fastest alternative JSON parser for Go
It does not require you to know the structure of the payload (eg. create structs), and allows accessing fields by providing the path to them. It is up to 10 times faster than standard encoding/json package (depending on payload size and usage), allocates no memory. Originally made for a project that relies on a lot of 3rd party APIs that can be unpredictable and complex. Encoding/json requires you to know exactly your data structures, or if you prefer to use map[string]interface{} instead, it will be very slow and hard to manage. I investigated what's on the market and found that most libraries are just wrappers around encoding/json, there is few options with own parsers (ffjson, easyjson), but they still require you to create data structures. ... -
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CPUAlert
High / Low CPU & MEMORY usage alerts by Email / Teams / Telegram
Simple and powerful CPU & MEMORY monitoring tool for Windows and Linux. Detect both HIGH and LOW usage, avoid repeated alert spam, and identify the TOP1-TOP5 process causing the issue. Alert only when usage continuously exceeds the threshold and back to normal. It monitors CPU and RAM usage and sends Email, Microsoft Teams, and Telegram alert notifications when usage remains above or below configured thresholds. -
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nanobot
🐈 nanobot: The Ultra-Lightweight Clawdbot / OpenClaw
nanobot is an ultra-lightweight personal AI assistant designed to deliver powerful agent capabilities without unnecessary complexity. Built in just ~4,000 lines of clean, readable code, it offers a minimalist alternative to heavyweight agent frameworks while retaining core intelligence and extensibility. nanobot is optimized for speed and efficiency, enabling fast startup times and low resource usage across environments. Its research-ready architecture makes it easy for developers to... -
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picocli
Framework for building GraalVM-enabled command line apps
Picocli is a one-file framework for creating Java command-line applications with almost zero code. It supports a variety of command-line syntax styles including POSIX, GNU, MS-DOS and more. It generates highly customizable usage help messages that use ANSI colors and styles to contrast important elements and reduce the cognitive load on the user. Picocli-based applications can have command line TAB completion showing available options, option parameters, and subcommands, for any level of nested subcommands. Picocli-based applications can be ahead-of-time compiled to a GraalVM native image, with extremely fast startup time and lower memory requirements, which can be distributed as a single executable file. ... -
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Smile
Statistical machine intelligence and learning engine
...Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM languages. Data scientists and developers can speak the same language now! Smile provides hundreds advanced algorithms with clean interface. Scala API also offers high-level operators that make it easy to build machine learning apps. ... -
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Resque
Ruby library for creating background jobs and processing them
Resque is a Redis-backed Ruby library for creating background jobs, placing them on multiple queues, and processing them later. Resque (pronounced like "rescue") is a Redis-backed library for creating background jobs, placing those jobs on multiple queues, and processing them later. Background jobs can be any Ruby class or module that responds to perform. Your existing classes can easily be converted to background jobs or you can create new classes specifically to do work. Or, you can do... -
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LiteRT
LiteRT, successor to TensorFlow Lite
LiteRT is Google's next-generation on-device machine learning framework and the successor to TensorFlow Lite, designed for high-performance AI and generative AI deployment across edge devices. It provides efficient model conversion, optimization, and runtime execution while leveraging hardware acceleration from CPUs, GPUs, and NPUs. LiteRT supports a wide range of platforms, including Android, iOS, Linux, macOS, Windows, web environments, and IoT devices. The framework simplifies on-device... -
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quicktemplate
Fast, powerful, yet easy to use template engine for Go
quicktemplate is a fast and efficient templating engine for Go that generates code at compile time to render templates with minimal runtime overhead. Unlike traditional templating systems that interpret templates dynamically, Quicktemplate compiles templates into Go code, significantly improving performance. This approach eliminates the need for reflection and reduces memory allocations, making it suitable for high-performance applications. The library is designed to be simple and... -
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Mini Agent
A minimal yet professional single agent demo project
Mini-Agent is a minimal yet production-minded demo project that shows how to build a serious command-line AI agent around the MiniMax-M2 model. It is designed both as a reference implementation and as a usable agent, demonstrating a full execution loop that includes planning, tool calls, and iterative refinement. The project exposes an Anthropic-compatible API interface and fully supports interleaved thinking, letting the agent alternate between reasoning steps and tool invocations during... -
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MemoryCleaner
Windows memory cleaner tool for optimizing system performance
MemoryCleaner is a powerful Windows memory cleaning tool designed to optimize system memory usage and enhance performance. It cleans various memory areas including working sets and standby lists, helping users free up occupied memory resources. Key features include cleaning six memory types: working set, system working set, standby list, low priority standby list, modified page list, and combined page list. -
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Qwen2.5-Omni
Capable of understanding text, audio, vision, video
Qwen2.5-Omni is an end-to-end multimodal flagship model in the Qwen series by Alibaba Cloud, designed to process multiple modalities (text, images, audio, video) and generate responses both as text and natural speech in streaming real-time. It supports “Thinker-Talker” architecture, and introduces innovations for aligning modalities over time (for example synchronizing video/audio), robust speech generation, and low-VRAM/quantized versions to make usage more accessible. It holds... -
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GrandPerspective
Graphically shows disk usage for Macs
GrandPerspective is a utility application for macOS that graphically displays the disk usage of your file system. It can help you to manage your disk, as you can easily spot which files and folders take up the most space. -
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
...The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory usage than traditional 16-bit or 32-bit neural networks. The architecture introduces specialized layers such as BitLinear, which replace standard linear projections in transformer networks with quantized operations. By limiting weight precision while maintaining efficient scaling and normalization strategies, the architecture aims to retain competitive performance while significantly reducing hardware requirements. -
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Bolt NLP
Bolt is a deep learning library with high performance
Bolt is a high-performance deep learning inference framework developed by Huawei Noah's Ark Lab. It is designed to optimize and accelerate the deployment of deep learning models across various hardware platforms. Bolt is a light-weight library for deep learning. Bolt, as a universal deployment tool for all kinds of neural networks, aims to automate the deployment pipeline and achieve extreme acceleration. Bolt has been widely deployed and used in many departments of HUAWEI company, such as... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
...After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. ...
