Search Results for "process metrics"
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Showing 30 open source projects for "process metrics"
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1
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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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. ... -
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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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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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5
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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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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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... -
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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. ... -
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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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10
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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Argilla
The open-source data curation platform for LLMs
Argilla is a production-ready framework for building and improving datasets for NLP projects. Deploy your own Argilla Server on Spaces with a few clicks. Use embeddings to find the most similar records with the UI. This feature uses vector search combined with traditional search (keyword and filter based). Argilla is free, open-source, and 100% compatible with major NLP libraries (Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc.). In fact, you can use and combine your preferred... -
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gopro-dashboard-overlay
Programs to process GoPro MP4 & Generic GPX/FIT files
gopro-dashboard-overlay is a multimedia processing toolkit that generates data-rich overlays on videos using telemetry data from GoPro cameras or external GPS sources. It processes video files alongside GPX or FIT data to render dashboards displaying metrics such as speed, distance, and location directly onto footage. The system supports a wide range of layouts, including maps, gauges, and charts, which can be customized through configuration files. It integrates FFmpeg for rendering and... -
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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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watercrawl
AI-ready web crawler that extracts and structures website content
WaterCrawl is an open source web crawling and data extraction platform designed to transform website content into structured data suitable for machine learning and AI workflows. It enables developers and researchers to crawl web pages, extract meaningful information, and convert it into formats that are easier to process and analyze. It provides a modern crawling system that can automatically navigate links, control crawl depth, and collect content from targeted sections of a website. WaterCrawl supports customizable extraction rules so users can focus only on relevant elements while ignoring unnecessary page components. WaterCrawl also offers real-time monitoring capabilities, allowing users to track crawling progress, performance metrics, and errors during large data collection jobs. ... -
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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. ... -
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PRAXIST
Autonomous research system for measurable computer-executable research
...Runs can be monitored, stopped, resumed, and replayed, with the task project remaining the source of truth for objectives, metrics, baselines, and domain constraints. -
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Weibo Crawler
Python crawler for collecting and downloading Sina Weibo user data
weibo-crawler is a Python-based data collection tool designed to retrieve information from Sina Weibo user accounts. It automates the process of gathering posts, user profile details, and engagement metrics from one or more target accounts. weibo-crawler can extract comprehensive information about users, including profile attributes such as nickname, follower count, following count, and account metadata. It also captures detailed data about each post, including the content, publishing time, topics, mentions, likes, reposts, and comments. ... -
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
Anthropic's Original Performance repository contains the publicly released version of a performance challenge originally used by Anthropic as part of their technical interview process, offering developers the opportunity to optimize and benchmark low-level code against simulated models. The project sets up a baseline performance problem where participants work to reduce simulated “clock cycles” required to run a given workload, effectively challenging them to engineer faster code under... -
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PaSa
An advanced paper search agent powered by large language models
PaSa is an open-source “paper search agent” built around large language models (LLMs), designed to automate the process of academic literature retrieval with human-like decision making. Instead of simply translating a query into keywords and returning a flat list of matching papers, PaSa uses a dual-agent architecture (Crawler + Selector) that can iteratively search, read, analyze, and filter academic publications — simulating how a researcher might dig through citation networks, expand... -
20
Text Length
Real-time word counter and reading time calculator
Text Length is a lightweight desktop utility designed for rapid text analysis. It operates entirely offline on your computer, ensuring complete privacy with zero data transmission to external servers. Featuring a clean dark minimalist interface, the app tracks characters (with/without spaces), words, and paragraphs in real time as you type. To optimize your editing process, it instantly calculates estimated reading times based on industry-standard speeds: 250 words per minute for silent... -
21
DeepSeek VL
Towards Real-World Vision-Language Understanding
DeepSeek-VL is DeepSeek’s initial vision-language model that anchors their multimodal stack. It enables understanding and generation across visual and textual modalities—meaning it can process an image + a prompt, answer questions about images, caption, classify, or reason about visuals in context. The model is likely used internally as the visual encoder backbone for agent use cases, to ground perception in downstream tasks (e.g. answering questions about a screenshot). The repository includes model weights (or pointers to them), evaluation metrics on standard vision + language benchmarks, and configuration or architecture files. ... -
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xTuring
Easily build, customize and control your own LLMs
xTuring is an open-source AI personalization software. xTuring makes it easy to build and control LLMs by providing a simple interface to personalize LLMs to your own data and application. xTuring provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, Galactica, and more. By providing an easy-to-use interface for fine-tuning LLMs to your own data and application, xTuring makes it simple to build, customize and control LLMs. The entire process can be done inside your... -
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four keys
Platform for monitoring the four key software delivery metrics
Through six years of research, the DevOps Research and Assessment (DORA) team has identified four key metrics that indicate the performance of software delivery. Four Keys allows you to collect data from your development environment (such as GitHub or GitLab) and compile it into a dashboard displaying these key metrics. Four Keys works well with projects that have deployments. Projects with releases and no deployments, for example, libraries, do not work well because of how GitHub and GitLab... -
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TensorFlow Ranking
Learning to rank in TensorFlow
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. Commonly used loss functions including pointwise, pairwise, and listwise losses. Commonly used ranking metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library... -
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LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the...
