Search Results for "metrics" - Page 8
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Showing 240 open source projects for "metrics"
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BPYTOP
Linux/OSX/FreeBSD resource monitor
...It displays real-time usage and statistics for CPU, memory, disks, network, and processes, with colorful graphs and widgets that update at configurable intervals. Users can drill into a process list, sort by various metrics, view tree hierarchies, and quickly spot heavy resource consumers. The tool is highly configurable through both an in-app options menu and a detailed configuration file, allowing customization of themes, update frequency, graph types, temperature sensors, and which “boxes” (CPU, memory, network, processes) are shown. It supports temperature monitoring, per-core stats, I/O graphs, swap, battery information, and network auto-scaling, making it suitable for serious monitoring on laptops and servers alike. -
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instagram-profilecrawl
Instagram profile crawler that extracts posts, tags, and stats
...It operates by automating a web browser using Selenium and performing requests to gather structured information from the platform. instagram-profilecrawl can analyze multiple usernames in a single run and store the extracted information locally in structured formats such as JSON. The collected data can include profile metadata, post details, engagement metrics, and commenter activity, allowing users to analyze account behavior or monitor profile growth over time. It also provides scripts for downloading images from crawled profiles and logging statistics into CSV files for tracking metrics like followers, likes, and comments. Authentication is optional, meaning the crawler can access public profile data without logging in. -
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igel
Machine learning tool that allows you to train and test models
A delightful machine learning tool that allows you to train/fit, test, and use models without writing code. The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I sometimes needed a tool sometimes, which I could use to fast create a machine learning prototype. Whether to build some proof of concept, create a fast draft model to prove a point or use auto ML. I find myself often stuck writing boilerplate code and thinking too much about... -
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PowerfulSeal
A powerful testing tool for Kubernetes clusters.
PowerfulSeal injects failure into your Kubernetes clusters so that you can detect problems as early as possible. It allows for writing scenarios describing complete chaos experiments. -
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Perceptual Similarity Metric and Dataset
LPIPS metric. pip install lpips
...To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. -
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speedtest-cli
Command line interface for testing internet bandwidth using speedtest
...It is a relative value used for determining the lowest latency server for performing the actual speed test against. Speedtest CLI brings the trusted technology and global server network behind Speedtest to the command line. Measure internet connection performance metrics like download, upload, latency and packet loss natively without relying on a web browser. Test the internet connection of your Linux desktop, a remote server or even lower-powered devices such as the Raspberry Pi with the Speedtest Server Network. Set up automated scripts to collect connection performance data, including trends over time. -
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Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. -
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Objectron
A dataset of short, object-centric video clips
The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists... -
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MachineLearningStocks
Using python and scikit-learn to make stock predictions
MachineLearningStocks is a Python-based template project that demonstrates how machine learning can be applied to predicting stock market performance. The project provides a structured workflow that collects financial data, processes features, trains predictive models, and evaluates trading strategies. Using libraries such as pandas and scikit-learn, the repository shows how historical financial indicators can be transformed into machine learning features. The model attempts to predict... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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pycoQC
pycoQC computes metrics and generates Interactive QC plots
PycoQC computes metrics and generates interactive QC plots for Oxford Nanopore technologies sequencing data. PycoQC relies on the sequencing_summary.txt file generated by Albacore and Guppy, but if needed it can also generate a summary file from basecalled fast5 files. The package supports 1D and 1D2 runs generated with Minion, Gridion and Promethion devices and basecalled with Albacore 1.2.1+ or Guppy 2.1.3+. -
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ngxtop
Real-time metrics for nginx server
...It automatically detects the access log location and format in many cases, enabling quick deployment with minimal configuration. Users can also customize queries, grouping logic, and filters to analyze specific traffic patterns or metrics. By combining the familiarity of the UNIX top paradigm with flexible log parsing, ngxtop offers a lightweight yet powerful approach to real-time web server observability. -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. ... -
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Metrix++
Management of source code quality is possible.
The project has been moved to https://github.com/metrixplusplus/metrixplusplus ______________________ Metrix++ is an extendable tool to collect and analyse code metrics. - Multiple languages supported - Multiple metrics available - Configurable. Every metric has got 'turn-on' and other configuration options. There are no predefined thresholds for metrics or rules. You can choose and configure any limit you want. - High-performance. Processes thousands of files per minutes. - Seamless application to legacy code due to embedded capability to differentiate new code, modified and legacy. -
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Datadog Agent
Datadog Agent Version 5
Datadog Agent is an open-source agent for collecting metrics, logs, and traces from servers, applications, and services, sending data to Datadog for analysis. -
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SentEval
A python tool for evaluating the quality of sentence embeddings
...The suite also contains linguistic probing tasks that illuminate what properties embeddings capture, such as tense, word order, or syntactic structure. Datasets are wrapped with unified preprocessing and metrics so results are comparable across papers and implementations. Because the interface is minimal, researchers can plug in encoders from any framework or language model and obtain a broad evaluation with little glue code. SentEval helped establish common baselines and reporting conventions in the sentence-representation community, reducing friction when comparing new methods. -
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Codeface
Typefaces for source code beautification
Codeface is a gallery and repository of monospaced fonts for developers. Fonts displayed in the main gallery, bitmap gallery, and CJK gallery are distributed through this repository. The font name link will open the respective font directory where you can download fonts from each typeface. This directory also includes the typeface license. The font archive links on the main gallery page include all fonts in the main gallery and bitmap font gallery collections. The font archive links on the... -
17
Reliable Metrics for Generative Models
Code base for the precision, recall, density, and coverage metrics
Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those properties separately. ... -
18
PerfKit Benchmarker
PerfKit Benchmarker (PKB) contains a set of benchmarks
PerfKitBenchmarker is an open-source benchmarking framework designed to measure and compare the performance of cloud infrastructure across multiple providers in a consistent and reproducible way. It allows users to evaluate metrics such as latency, throughput, provisioning time, and system performance using a standardized set of benchmarks. The tool supports a wide range of environments, including major cloud platforms, Kubernetes clusters, and even local hardware, making it highly versatile for performance analysis. It simplifies the process of running complex benchmarks by providing unified command-line workflows that handle resource provisioning, execution, and result collection. ... -
19
Albedo
A recommender system for discovering GitHub repos
...It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible setup and Makefile-driven workflow streamline tasks like spinning up services, loading datasets, training models, and generating candidate lists. Because it’s built around Spark’s scalable primitives, Albedo can experiment on substantial snapshots of GitHub metadata rather than toy corpora. ... -
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PyTorch GAN Zoo
A mix of GAN implementations including progressive growing
...It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD, and CIFAR-10. In addition to core GAN training, the repository includes tools for model evaluation, such as Inception Score and SWD metrics, as well as advanced features like GDPP for diverse generation and AC-GAN conditioning for class-specific synthesis. The framework also supports “inspirational generation,” enabling style or content transfer from reference images through pre-trained models. -
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sysadmin-util
Tools for Linux/Unix sysadmins
...The repository contains a variety of single-purpose scripts that solve common problems such as determining file ages, testing SSH connectivity, checking MySQL slave status, expanding IPv6 addresses, sending metrics to Graphite, and working with IP ranges. The project intentionally keeps the set small and focused, emphasising quality and personal usefulness over trying to be a large toolbox everyone adopts unchanged. Each tool is simple to inspect, easy to install, and written to be useful in scripting and cron workflows, with many commands designed to behave well in automation and monitoring contexts. ... -
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TensorWatch
Debugging, monitoring and visualization for Python Machine Learning
...It enables developers to observe training behavior in real time through interactive visualizations, primarily within Jupyter Notebook environments. The tool treats most data interactions as streams, allowing flexible routing, storage, and visualization of metrics generated during model training. A distinctive capability is its “lazy logging” mode, which lets users query live training processes without pre-instrumenting all metrics ahead of time. TensorWatch supports multiple chart types and can be extended with custom visualizers and dashboards, making it highly adaptable for research workflows. ... -
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Ouroboros
Automatically update running docker containers
...Push your image to your registry and simply wait your defined interval for ouroboros to find the new image and redeploy your container autonomously. Notify you via many platforms courtesy of Apprise. Serve metrics for trend monitoring (Currently: Prometheus/Influxdb) Per-command and scenario examples can be found in the wiki. -
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Fantasque Sans Mono
A font family with a great monospaced variant for programmers
...It appeared that similar names were already in use for other fonts, and that people tended to extend their instinctive hatred of Comic Sans to this very font of mine (which of course can only be loved). The font includes a bold version, with the same metrics as the regular one. Both versions include the same ranges of characters, latin letters, some accented glyphs (quite a lot), some greek letters, some arrows. It also features a good italic version, which I designed in a fashion similar to Consolas' italic version, with new glyph designs, not just an added slant. -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
...The repository provides complete tooling for preprocessing mesh datasets (e.g., ShapeNet), training DeepSDF models, reconstructing meshes from learned latent codes, and quantitatively evaluating results with metrics such as Chamfer Distance and Earth Mover’s Distance.
