Search Results for "cpu monitor"

The MCP System Monitor is a tool that exposes system metrics via the Model Context Protocol (MCP), allowing Large Language Models (LLMs) to retrieve real-time system information through an MCP-compatible interface. ​

Nativ is a native macOS workspace for running AI models locally on Apple silicon. It combines private chat, model management, image generation, voice tools, monitoring, and local inference serving in one SwiftUI application. The app discovers compatible MLX models in the Hugging Face cache and can download additional models with memory-fit warnings. Conversations support streaming responses, images, reasoning output, persistent history, and performance metrics. Nativ exposes OpenAI- and...

Polyaxon is an open-source machine learning operations (MLOps) platform built to help individuals, teams, and organizations develop, train, orchestrate, and monitor machine learning and deep learning workflows at scale with reproducibility and automation as core principles. It provides a unified solution for tracking experiments, managing datasets, scheduling jobs, and comparing results across runs, which greatly improves productivity and collaboration in data science teams. Polyaxon integrates seamlessly with Kubernetes and container orchestration so that workloads can be scheduled efficiently, GPU and CPU resources are shared, and distributed training across multiple nodes is straightforward. ...

GPU Hot is an open-source, lightweight monitoring dashboard designed to provide real-time visibility into NVIDIA GPU performance across single machines or entire clusters. The project offers a self-hosted web interface that streams hardware metrics directly from GPU servers, enabling developers, ML engineers, and system administrators to observe GPU utilization and system behavior in real time through a browser. The dashboard collects and displays a wide range of performance metrics...

flutter_ume is an in-app debug-kit platform for Flutter applications, developed by ByteDance’s Flutter Infra team. It lets developers embed a suite of debugging tools directly into a Flutter app (during development or debug builds), enabling inspection, performance monitoring, UI debugging, network request inspection, widget hierarchy introspection, and more — all from within the running app. UME bundles multiple “plugin kits” (e.g., UI inspector, performance monitor, device info panel,...