Search Results for "parallel" - Page 8
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Showing 1344 open source projects for "parallel"
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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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Marl
A hybrid thread / fiber task scheduler written in C++ 11
...The runtime is portable across platforms and integrates cleanly with real-time systems that must minimize latency jitter. Graphics, simulation, and media pipelines use marl to turn long serial phases into parallel stages without rewriting everything around coroutines. -
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Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and... -
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GenStage
Producer and consumer actors with back-pressure for Elixir
...Its clear separation of concerns encourages testable, composable stages that can be rearranged as requirements evolve. In production, this leads to predictable, resilient dataflows for event ingestion, batching, and parallel processing. -
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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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Incredable is the first DLT-secured platform that allows you to save time, eliminate errors, and ensure your organization is compliant all in one place.Incredable streamlines and simplifies the complex process of medical credentialing for hospitals and medical facilities, helping you save valuable time, reduce costs, and minimize risks. With Incredable, you can effortlessly manage all your healthcare providers and their credentials within a single, unified platform. Our state-of-the-art technology ensures top-notch data security, giving you peace of mind.
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. The solution enables ML/AI engineers to run automated experiments based on datasets and hyperparameter conditions within the defined infrastructure budget. ... -
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Trafilatura
Python & command-line tool to gather text on the Web
Trafilatura is a Python package and command-line tool designed to gather text on the Web. It includes discovery, extraction and text-processing components. Its main applications are web crawling, downloads, scraping, and extraction of main texts, metadata and comments. It aims at staying handy and modular: no database is required, the output can be converted to various commonly used formats. Going from raw HTML to essential parts can alleviate many problems related to text quality, first by... -
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LinDB
LinDB is a scalable, high performance, high availability database
LinDB is a scalable, high-performance, high-availability distributed time series database. A single server could easily support more than one million write TPS; With fundamental techniques like efficient compression storage and parallel computing, LinDB delivers highly optimized query performance. The multi-channel replication protocol supports any amount of nodes, and ensures the system's availability. Schema-free multi-dimensional data model with Metric, Tags, and Fields; The LinQL is flexible yet handy for real-time data analytics. Horizontal scalable is made simple by adding more new broker and storage nodes without too much thinking and manual operations. ... -
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AWS Encryption SDK for Dafny
AWS Encryption SDK for Dafny
To build, the AWS Encryption SDK requires the most up-to-date version of dafny on your PATH. In addition, this project uses the parallel verification tasks provided by the dafny.msbuild MSBuild plugin, and thus requires dotnet 3.0. The tests currently require native implementations of cryptographic primitives and other methods, so they can only be run when embedding this library into one of the compilation target languages supported by Dafny. This repo uses Duvet to directly document the specification alongside this implementation. ... -
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Django friendly finite state machine
Django friendly finite state machine support
Django-fsm adds simple declarative state management for Django models. If you need parallel task execution, view, and background task code reuse over different flows - check my new project Django-view flow. Instead of adding a state field to a Django model and managing its values by hand, you use FSMField and mark model methods with the transition decorator. These methods could contain side effects of the state change. -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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Docco
Literate Programming can be Quick and Dirty
Docco is a documentation generator by Jeremy Ashkenas that embraces the literate-programming style: it takes your source code and produces annotated HTML documentation that shows your comments side-by-side with your code. The idea is to read code like a book — commentary on one side, code on the other — which helps reviewers and learners understand intent and implementation simultaneously. It supports many languages (via configuration) and is intentionally quick and dirty, prioritizing... -
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RealWorld
Exemplary fullstack Medium.com clone powered by React, Angular, Node
RealWorld is the “mother of all demo apps”—a full spec and starter backend/frontend that implements a Medium-like blogging platform to showcase best practices across many frameworks. Instead of trivial todo lists, it provides a realistic feature set: authentication, CRUD operations, pagination, comments, profiles, tagging, and favoriting. The same spec is realized dozens of times (React, Vue, Svelte, Angular, Solid, Next, Remix, and many more), alongside multiple server implementations, so... -
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LangExtract
A Python library for extracting structured information
...LangExtract supports a wide range of models, including Google Gemini, OpenAI GPT, and local LLMs via Ollama, making it adaptable to different deployment environments and compliance needs. The system excels at handling long documents using optimized chunking, multi-pass extraction, and parallel processing to ensure both high recall and structured consistency. -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP... -
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mlr3
mlr3: Machine Learning in R - next generation
mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via... -
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous... -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
...However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. It remains a challenge for AI researchers to implement complex distributed training solutions for their models. Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. -
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Reliable PostgreSQL Backup & Restore
Reliable PostgreSQL backup and restore
...Compression is usually the bottleneck during backup operations but, even with now-ubiquitous multi-core servers, most database backup solutions are still single-process. pgBackRest solves the compression bottleneck with parallel processing. Utilizing multiple cores for compression makes it possible to achieve 1TB/hr raw throughput even on a 1Gb/s link. More cores and a larger pipe lead to even higher throughput. A custom protocol allows pgBackRest to backup, restore, and archive locally or remotely via TLS/SSH with minimal configuration. An interface to query PostgreSQL is also provided via the protocol layer so that remote access to PostgreSQL is never required, which enhances security. -
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GoCD
Main repository for GoCD, continuous delivery server
...GoCD streamlines your CD workflow on popular cloud environments such as Kubernetes, Docker, AWS and more. GoCD excels at modeling complex CD workflows for fast feedback with its modeling constructs, parallel execution and dependency management. No plugin required, out of box CD. GoCD helps you troubleshoot a broken pipeline by tracking every change from commit to deploy in real time. Compare content, both files and commit messages, across any two arbitrary builds. GoCD integrates with many popular external tools and services via its extensible plugin architecture. ... -
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Mastra
The TypeScript AI agent framework
...Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. When explicit control is needed, Mastra’s workflow engine uses a graph-style API (.then(), .branch(), .parallel()) to orchestrate multi-step processes. -
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Meta-World
Collections of robotics environments
...The environments adhere to the Gymnasium API, which makes them easy to plug into existing RL pipelines, and they support both synchronous and asynchronous vectorized execution for running many environments in parallel. Installation is done via pip, with official support for Python versions 3.8 through 3.11 on Linux and macOS, and the project is licensed under MIT to encourage broad academic and industry use. -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in... -
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C-ARES
A C library for asynchronous DNS requests
This is C-ARES, an asynchronous resolver library. It is intended for applications that need to perform DNS queries without blocking, or need to perform multiple DNS queries in parallel. The primary examples of such applications are servers that communicate with multiple clients and programs with graphical user interfaces. The full source code is available in the 'C-ARES' release archives. -
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Geodesic
Geodesic is a DevOps Linux Toolbox in Docker
...It's designed to bring consistency and boost efficiency across development environments. It achieves this without the need for installing additional software on your workstation. Think of Geodesic as a containerized parallel to Vagrant, offering similar functionality within a Docker container context. -
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Helmsman
Helm Charts as Code
Helmsman is a Helm Charts (k8s applications) as Code tool that allows you to automate the deployment/management of your Helm charts from version-controlled code. Helmsman uses a simple declarative TOML file to allow you to describe a desired state for your k8s applications as in the example toml file. Alternatively YAML declaration is also an acceptable example YAML file. Helmsman sees what you desire, validates that your desire makes sense (e.g. that the charts you desire are available in... -
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Argo Workflows
Workflow engine for Kubernetes
Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). Define workflows where each step in the workflow is a container. Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. ...
