Search Results for "code metric"
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Showing 48 open source projects for "code metric"
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
MetricFlow is an open-source semantic layer engine designed to help organizations define, manage, and query business metrics in a consistent, governed way. It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized,... -
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Metrics
Capturing JVM and application-level metrics
...The central library for Metrics is metrics-core, which provides some basic functionality. The starting point for Metrics is the MetricRegistry class, which is a collection of all the metrics for your application (or a subset of your application). Each metric is associated with a MetricRegistry, and has a unique name within that registry. A gauge is the simplest metric type. It just returns a value. If, for example, your application has a value which is maintained by a third-party library, you can easily expose it by registering a Gauge instance which returns that value. -
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ML Sharp
Sharp Monocular View Synthesis in Less Than a Second
...The representation is metric, meaning it supports camera movements with an absolute scale rather than only relative depth cues, which is useful for consistent viewpoint changes and downstream spatial tasks. The project is structured for reproducibility, with code and assets aimed at demonstrating view synthesis quality, sharp details, and fine structures when rendering high-resolution images. -
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Image Fusion
Deep Learning-based Image Fusion: A Survey
This repository is a survey / code collection centered on deep learning–based image fusion (e.g. fusing infrared + visible light images, multi-modal fusion) methods. It catalogs many fusion algorithms (e.g. DenseFuse, FusionGAN, NestFuse, etc.), links to code implementations, and describes evaluation metrics. The repository includes a “General Evaluation Metric” subfolder containing objective fusion metrics. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
...The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. By the end, participants can build end-to-end ML experiments, interpret outputs, and iterate with confidence rather than just copying code. -
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DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning,...
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DVC
Data Version Control | Git for Data & Models
...DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. Harness the full power of Git branches to try different ideas instead of sloppy file suffixes and comments in code. Use automatic metric tracking to navigate instead of paper and pencil. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. -
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Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation,... -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ... -
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Metarank
A low code Machine Learning service that personalizes articles
...Run Metarank API service, feed it with real-time events and receive a personalized ranking for your items that will boost conversion, click-through rate or any other business-critical metric you define. -
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Micrometer
App observability facade for the most popular observability tools
Micrometer provides a simple facade over the instrumentation clients for the most popular observability systems, allowing you to instrument your JVM-based application code without vendor lock-in. Think SLF4J, but for observability. Micrometer provides vendor-neutral interfaces for timers, gauges, counters, distribution summaries, and long task timers with a dimensional data model that, when paired with a dimensional monitoring system, allows for efficient access to a particular named metric with the ability to drill down across its dimensions. ... -
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AWS IoT Device Defender Library
Client library for using AWS IoT Defender service on embedded devices
...This library also supports custom metrics, a feature that helps you monitor operational health metrics that are unique to your fleet or use case. For example, you can define a new metric to monitor the memory usage or CPU usage on your devices. This library has no dependencies on any additional libraries other than the standard C library, and therefore, can be used with any MQTT client library. This library is distributed under the MIT Open Source License. This library has gone through code quality checks including verification that no function has a GNU Complexity score over 8, and checks against deviations from mandatory rules in the MISRA coding standard. ... -
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FLAML
A fast library for AutoML and tuning
...Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space, and metric), or full customization (arbitrary training and evaluation code). It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research. -
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you... -
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phantomas
Headless Chromium-based web performance metrics collector
Headless Chromium-based modular web performance metrics collector. Modular approach, each metric is generated by a separate "module". phantomas "core" acts as an events emitter that each module can hook into. In-depth metrics such as: number of events bound via jQuery, calls to window.writeor complex and duplicated CSS selectors (via analyze-css). JSON as an output format. Easy integration with other nodejs projects via CommonJS module (see API docs). -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before... -
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SLOC
simple tool to count SLOC (source lines of code)
sloc is a simple, fast tool for counting lines of code that’s designed to be used from both the command line and Node.js scripts. It tallies physical lines, source lines, comment lines, and blank lines, giving you a quick snapshot of code size across files or entire directories. The utility is language-aware through lightweight detectors and patterns, so it can ignore comments correctly and avoid counting generated or minified files if you configure it to. -
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prometheus-net
.NET library to instrument your code with Prometheus metrics
This is a .NET library for instrumenting your applications and exporting metrics to Prometheus. -
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Locale en_RU
English language locale for Russia
... • Date format is changed from confusing mm/dd/yyyy to unambiguous ISO yyyy-mm-dd. • Weeks start on Monday, not Sunday. • Thousand separator is not comma but narrow non-breaking space. • Imperial units are replaced with metric ones. • Default paper sheet size is A4. • Territory is changed to Russia: country name, country phone code, currency name, etc. See detailed description at https://en-ru.sourceforge.io in English and Russian languages. -
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NLG-Eval
Evaluation code for various unsupervised automated metrics
NLG-Eval is a toolkit for evaluating the quality of natural language generation (NLG) outputs using multiple automated metrics such as BLEU, METEOR, and ROUGE. -
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PyCls
Codebase for Image Classification Research, written in PyTorch
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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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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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...
