Search Results for "process metrics"
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Showing 99 open source projects for "process metrics"
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1
Process Lasso
Process Lasso is Windows process automation and optimization software.
...Advanced features like the CPU Limiter, Instance Balancer, and Watchdog rules offer deep automation for demanding workloads. Process Lasso also includes monitoring tools, responsiveness metrics, and detailed system activity logging. Designed to run efficiently with a lightweight background service, it improves stability without adding overhead. -
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Prom-Client
Prometheus client for node.js
Prom-Client is a Prometheus client for Node.js that allows developers to instrument their applications and expose metrics, facilitating the monitoring of application performance and resource usage. -
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JMX Exporter
A process for exposing JMX Beans via HTTP for Prometheus consumption
JMX to Prometheus exporter: a collector that can configurable scrape and expose mBeans of a JMX target. This exporter is intended to be run as a Java Agent, exposing a HTTP server and serving metrics of the local JVM. It can be also run as a standalone HTTP server and scrape remote JMX targets, but this has various disadvantages, such as being harder to configure and being unable to expose process metrics (e.g., memory and CPU usage). Running the exporter as a Java agent is strongly encouraged. -
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MCP Monitor
A system monitoring tool that exposes system metrics
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. -
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Apache DevLake
Apache DevLake is an open-source dev data platform
Apache DevLake is an open-source dev data platform that ingests, analyzes, and visualizes the fragmented data from DevOps tools to extract insights for engineering excellence, developer experience, and community growth. Apache DevLake is designed for developer teams looking to make better sense of their development process and to bring a more data-driven approach to their own practices. You can ask Apache DevLake many questions regarding your development process. Just connect and query. Your Dev Data lives in many silos and tools. DevLake brings them all together to give you a complete view of your Software Development Life Cycle (SDLC). From DORA to scrum retros, DevLake implements metrics effortlessly with prebuilt dashboards supporting common frameworks and goals. ... -
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django-prometheus
Export Django monitoring metrics for Prometheus.io
...You may want to monitor the creation/deletion/update rate for your model. This can be done by adding a mixin to them. This is safe to do on existing models (it does not require a migration). Note that the exported metrics are counters of creations, modifications and deletions done in the current process. They are not gauges of the number of objects in the model. -
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Middleware
Open-source DORA metrics platform for engineering teams
Bring more visibility to your engineering pipeline, get the right data & actionable insights to unclog bottlenecks, ensuring smooth software delivery. -
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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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GPU Hot
Real-time NVIDIA GPU dashboard
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... -
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10
psutil
Cross-platform lib for process and system monitoring in Python
psutil is a widely adopted cross-platform Python library designed to retrieve detailed information about system utilization and running processes in a consistent and programmatic way. It exposes a rich API that allows developers to inspect CPU usage, memory consumption, disk activity, network statistics, and hardware sensors without relying on platform-specific tools. The library effectively replicates and unifies the capabilities of classic UNIX utilities such as ps, top, netstat, and free,... -
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Zenith
Sort of like top or htop but with zoom-able charts, CPU, GPU
In terminal graphical metrics for your *nix system written in Rust. The make file provides for building fully static versions on Linux against the musl C library. It requires musl-gcc to be installed on the system. Install "musl-tools" package on debian/ubuntu derivatives, "musl-gcc" on fedora and equivalent on other distributions from their standard repos. If one needs to build with NVIDIA support in a virtual environment, then it requires some more setup since typically the VM software is... -
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AIDE ML
AI-Driven Exploration in the Space of Code
...Instead of relying on manual experimentation, the agent autonomously drafts machine learning pipelines, debugs errors, and benchmarks performance against user-defined evaluation metrics. The system repeatedly improves its generated code by exploring different implementation paths and selecting the best-performing solutions. AIDE ML is packaged as a Python toolkit with built-in utilities such as command-line tools, configuration presets, and visualization interfaces that allow researchers to observe how the search process evolves. ... -
13
autoresearch-macos
AI agents running research on single-GPU nanochat training
...By automating experimentation and optimization, it allows continuous improvement without manual intervention, effectively turning research into a self-improving process. -
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VisualDL
Deep Learning Visualization Toolkit
VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time, visualizing the model structure, displaying the data sample, visualizing the relationship between hyperparameters and model metrics, presenting the changes of distributions of tensors, showing the pr curves, projecting high-dimensional data to a lower dimensional space and more. ... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. -
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Project Dashboard
A modern project & task management dashboard
...Its modular design lets you pick and choose which panels or components matter most for your workflow and tailor layouts to match your process — whether agile, waterfall, or hybrid. It also includes theming support so teams can match branding or accessibility preferences without extensive styling work. -
17
FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. ... -
18
OpenTelemetry Collector
OpenTelemetry Collector
The OpenTelemetry Collector offers a vendor-agnostic implementation on how to receive, process, and export telemetry data. In addition, it removes the need to run, operate, and maintain multiple agents/collectors in order to support open-source telemetry data formats (e.g. Jaeger, Prometheus, etc.) to multiple open-source or commercial back-ends. -
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autoresearch for AMD
AI agents running research on single-GPU nanochat training
...The system is built around a minimal structure that includes a data preparation module, a training script that can be modified, and a program specification that guides the agent’s decision-making process. During each iteration, the agent edits the training code, runs an experiment within a fixed time budget, evaluates performance metrics, and decides whether to retain or discard the changes. This loop allows the system to explore a wide range of architectural and hyperparameter configurations without human intervention. The framework emphasizes simplicity and reproducibility, ensuring that experiments are comparable and results are traceable over time. -
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SDGym
Benchmarking synthetic data generation methods
...Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. ... -
21
Node.js Testing Best Practices
Beyond the basics of Node.js testing
...It covers API testing, contract testing, and persistence-layer strategies that avoid coupling tests to fragile implementation details. The material explains how to structure tests around process boundaries—HTTP, queues, cron jobs—while keeping fast feedback cycles through selective integration tests and reliable test doubles. It dives into topics like test containers, ephemeral databases, and idempotent fixtures to ensure deterministic runs in CI. Observability is treated as part of testing: logs, metrics, and tracing make failures diagnosable instead of mysterious. ... -
22
caret
caret (Classification And Regression Training) R package
The caret (Classification And Regression Training) R package streamlines the process of building predictive machine learning models. It provides uniform interfaces for model training, tuning, evaluation, preprocessing, and variable importance. With support for over 200 models, caret is foundational for R workflows in modeling and machine learning. -
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Segmentation Models
Segmentation models with pretrained backbones. PyTorch
...Input channels parameter allows you to create models, which process tensors with an arbitrary number of channels. -
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Broadway
Concurrent and multi-stage data ingestion and data processing
Broadway is a data processing library for Elixir designed to handle high-throughput, concurrent workloads with ease. It provides an abstraction for defining pipelines that consume data from sources like RabbitMQ, Kafka, Amazon SQS, or custom producers. Each pipeline is fault-tolerant and backpressure-aware, ensuring stable throughput even under load. The library integrates seamlessly with GenStage and OTP supervision trees, making it highly resilient in production. Developers can enrich... -
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Unsloth Studio
Unified web UI for training and running open models locally
Unsloth Studio is a web-based interface for running and training AI models locally with a unified and user-friendly experience. It allows users to work with a wide range of models for text, audio, vision, embeddings, and more without relying heavily on cloud infrastructure. Built on top of the Unsloth framework, it focuses on high-performance training with reduced VRAM usage and faster speeds compared to traditional methods. The platform supports fine-tuning, pretraining, and reinforcement...
