Search Results for "metric"
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Showing 261 open source projects for "metric"
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Depth Pro
Sharp Monocular Metric Depth in Less Than a Second
Depth Pro is a foundation model for zero-shot metric monocular depth estimation, producing sharp, high-frequency depth maps with absolute scale from a single image. Unlike many prior approaches, it does not require camera intrinsics or extra metadata, yet still outputs metric depth suitable for downstream 3D tasks. Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. ... -
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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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TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! Automatic accumulation over multiple batches. Automatic synchronization between multiple devices. Metric arithmetic. ... -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
...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, warehouse-specific SQL to compute that metric, handling joins, filters, time grains, offsets, and other complexities under the hood. Because metric definitions live centrally, you avoid duplication across teams and tools, reduce risk of inconsistent numbers, and make it easier to audit and evolve the logic over time. ... -
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Amper
Build tool for the Kotlin and Java languages
Amper is an open-source data collection and metric reporting agent developed by JetBrains as part of their internal analytics and telemetry infrastructure for IntelliJ-based products. Its purpose is to gather usage statistics, performance metrics, error reports, and other diagnostic signals from IDE installations in a privacy-conscious way to help product teams understand real-world usage patterns and improve quality. -
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Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
Map-Anything is a universal, feed-forward transformer for metric 3D reconstruction that predicts a scene’s geometry and camera parameters directly from visual inputs. Instead of stitching together many task-specific models, it uses a single architecture that supports a wide range of 3D tasks—multi-image structure-from-motion, multi-view stereo, monocular metric depth, registration, depth completion, and more. -
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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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wego
Weather app for the terminal
...You can create an account and get a free API key by signing up. You can set the $WEGORC environment variable to override the default config file location. Displayed info (metric or imperial units), temperature range (felt and measured), windspeed and direction, viewing distance, precipitation amount and probability. -
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ML Sharp
Sharp Monocular View Synthesis in Less Than a Second
...The core idea is speed: the 3D representation is produced in under a second on a standard GPU, and then the resulting scene can be rendered in real time to generate new views interactively. 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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Evaluate
A library for easily evaluating machine learning models and datasets
Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. -
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grafana-dashboards-kubernetes
A set of modern Grafana dashboards for Kubernetes
...The project aims to provide clear, practical visualizations that help DevOps teams quickly understand cluster health, workload behavior, and infrastructure performance. Rather than attempting to expose every possible metric, the dashboards are designed to be operationally useful and easy to navigate during day-to-day troubleshooting. They leverage newer Grafana panel capabilities and are commonly used alongside kube-prometheus-stack deployments. The dashboards allow users to drill down from high-level cluster views to detailed pod-level insights, supporting rapid root-cause analysis. ... -
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flagger
Progressive delivery Kubernetes operator (Canary, A/B Testing)
Flagger is a progressive delivery tool that automates the release process for applications running on Kubernetes. It reduces the risk of introducing a new software version in production by gradually shifting traffic to the new version while measuring metrics and running conformance tests. Flagger implements several deployment strategies (Canary releases, A/B testing, Blue/Green mirroring) using a service mesh (App Mesh, Istio, Linkerd, Kuma, Open Service Mesh) or an ingress controller... -
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MLE-Agent
Intelligent companion for seamless AI engineering and research
MLE-Agent is designed as a pairing LLM agent for machine learning engineers and researchers. A library designed for managing machine learning experiments, tracking metrics, and model deployment. -
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NVIDIA GPU Exporter
Nvidia GPU exporter for prometheus using nvidia-smi binary
Nvidia GPU exporter for prometheus, using nvidia-smi binary to gather metrics. There are many Nvidia GPU exporters out there however they have problems such as not being maintained, not providing pre-built binaries, having a dependency to Linux and/or Docker, targeting enterprise setups (DCGM) and so on. -
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Thanos
Highly available Prometheus setup with long term storage capabilities
...Downsample historical data for massive query speedup when querying large time ranges or configure complex retention policies. Thanos leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies. Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly. -
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Gocache
Go cache library that brings you multiple ways to manage caches
...A chain cache: use multiple cache with a priority order (memory then fallback to a redis shared cache for instance) A loadable cache: allow you to call a callback function to put your data back in cache. A metric cache to let you store metrics about your caches usage (hits, miss, set success, set error, ...) A marshaler to automatically marshal/unmarshal your cache values as a struct. Define default values in stores and override them when setting data. Cache invalidation by expiration time and/or using tags. Use of Generics. To begin working with the latest version of gocache, you can import the library in your project. -
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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... -
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SparkyFitness
Track food, fitness, water, and health
...It provides tools for logging daily meals with nutritional breakdowns, tracking workouts with an extensive exercise database, and visualizing long-term progress using interactive charts and reports. The system also supports water intake goals, body metric logging (such as weight and measurements for different muscle groups), and customizable goals to help users stay motivated and accountable. An AI-powered nutrition coach is included, allowing users to log food, exercise, and steps through natural language chat and even upload food images for automatic analysis. SparkyFitness includes secure user authentication and profile management, supports multiple user accounts, and can be deployed via Docker or traditional hosting setups. -
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Argo Rollouts
Progressive Delivery for Kubernetes
Argo Rollouts is a Kubernetes controller and set of CRDs that provide advanced deployment capabilities such as blue-green, canary, canary analysis, experimentation, and progressive delivery features to Kubernetes. Argo Rollouts (optionally) integrates with ingress controllers and service meshes, leveraging their traffic shaping abilities to gradually shift traffic to the new version during an update. Additionally, Rollouts can query and interpret metrics from various providers to verify key... -
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Node exporter
Exporter for machine metrics
Power your metrics and alerting with a leading open-source monitoring solution. Prometheus implements a highly dimensional data model. Time series are identified by a metric name and a set of key-value pairs. PromQL allows slicing and dicing of collected time series data in order to generate ad-hoc graphs, tables, and alerts. Prometheus has multiple modes for visualizing data: a built-in expression browser, Grafana integration, and a console template language. Prometheus stores time series in memory and on local disk in an efficient custom format. ... -
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FitTrackee
Self-hosted outdoor activity tracker
FitTrackee is a self-hostable fitness tracking and workout management platform designed to help individuals and small groups monitor physical activity, set goals, and review performance over time with clarity and flexibility. It provides an organized environment for logging workouts, recording metrics like sets, reps, durations, and weights, and visualizing progress through charts and summaries that detail trends in strength, endurance, and consistency. Instead of locking users into... -
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Fara-7B
An Efficient Agentic Model for Computer Use
Fara-7B is a Microsoft initiative aimed at bringing rigor, transparency, and structured evaluation to AI systems through automated and customizable assessment frameworks. It provides stakeholders with a way to benchmark and evaluate models across dimensions such as fairness, robustness, security, privacy, and ethical considerations. Rather than relying on ad-hoc or manual review processes, FARA enables organizations to profile AI behavior using standardized tests, metrics, and reporting... -
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EvoTrees.jl
Boosted trees in Julia
A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram-based algorithms with support for multiple loss functions, including various regressions, multi-classification and Gaussian max likelihood. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
...Automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
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Auto-Deep-Research
Your Fully-Automated Personal AI Assistant
...Users provide a research topic or multifaceted goal, and the system autonomously breaks the objective down into subtasks like literature collection, critical summarization, cross-comparison, citation extraction, metric evaluation, and structured writing. Auto-Deep-Research integrates retrieval from academic and web sources, processes document corpora for relevance and key insights, and organizes outputs into coherent chapters or sections according to research standards. It also embeds validation loops, where intermediate drafts are self-checked for consistency, coverage, and alignment with sound reasoning practices, reducing reliance on raw generation alone.
