Search Results for "parallel-sudoku-solver" - Page 13
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Showing 2100 open source projects for "parallel-sudoku-solver"
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Semiotic
A data visualization framework combining React & D3
...It provides three types of frames XYFrame, OrdinalFrame, NetworkFrame, to deploy a wide variety of charts. XY data i.e. line charts and scatterplots. Categorical data i.e. bar charts, violin plots, parallel coordinates. Topological and network data i.e. flow diagrams, network visualization, and hierarchical views. A guide for creating a line chart, timeseries, difference line, and line percents using XYFrame along with hover behavior, responsive dimensions, and styling. XYFrame takes lines as an object or an array of objects. Each object represents a line. ... -
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gluon
A static, type inferred and embeddable language written in Rust
...This keeps each heap small, reducing the overhead of the garbage collector. Gluon is written in Rust, which guarantees thread safety. Gluon keeps the same guarantees, allowing multiple gluon programs to run in parallel. -
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TestCafe
A Node.js tool to automate end-to-end web testing
...There’s no need for WebDrivers or other testing software. Installing TestCafe takes just one minute and one command. TestCafe lets you create smart, stable tests with no manual timeouts. It can run parallel tests, saving you on test execution time, and can even build readable tests with PageObject. TestCafe runs on all popular platforms and environments, from mobile to desktop, as well as remote and cloud browsers. It’s totally free and open source, with plenty of community-made plugins and the freedom to create your own. -
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Stanza
Stanford NLP Python library for many human languages
...It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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BeeAI Framework
Build production-ready AI agents in both Python and Typescript
...BeeAI also provides orchestration tools for designing dynamic workflows, enabling multiple agents to coordinate tasks through structured execution flows, retries, and parallel processing. -
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Fractals
Fractals is a recursive task orchestrator for agent swarm
Fractals is an experimental open-source framework designed to orchestrate complex tasks using swarms of AI agents organized in a recursive structure. The system takes a high-level goal and decomposes it into a hierarchy of smaller subtasks, forming a self-similar tree that resembles a fractal structure. Each leaf node of this tree represents a specific executable task that can be processed independently by an AI agent. The framework runs these subtasks in isolated Git worktrees so agents can... -
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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
...The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive models. The engine supports training models with hundreds of billions of parameters and enables long-context training with sequence lengths reaching tens of thousands of tokens. Its architecture incorporates memory-efficient optimizations that allow researchers to train large models even when computational resources are limited. ... -
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Sandstorm
One API call, pull Claude agent, completely sandboxed
...This approach lowers the friction of building autonomous agents by removing the need to provision servers, orchestrate distributed agents, or manage persistent tooling; agents can be spun up in parallel without manual setup and shut down when complete. The sandbox environment isolates agent execution for security and predictability, and project updates continue to harden observability, fault handling, and configuration validation. -
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WorkAny
Desktop Agent for Any Task
...Powered by a combination of Claude Code as the primary runtime agent and a sandbox execution environment for safety, WorkAny integrates an agent SDK, MCP (Model Context Protocol) support, and custom skills to handle diverse tasks with contextual understanding. Users can connect multiple model providers, including OpenAI, OpenRouter, or custom endpoints, and WorkAny supports parallel task execution with asynchronous result viewing, enhancing productivity. -
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Step3-VL-10B
Multimodal model achieving SOTA performance
Step3-VL-10B is an open-source multimodal foundation model developed by StepFun AI that pushes the boundaries of what compact models can achieve by combining visual and language understanding in a single architecture. Despite having only about 10 billion parameters, it delivers performance that rivals or even surpasses much larger models (10×–20× larger) on a wide range of multimodal benchmarks covering reasoning, perception, and complex tasks, positioning it as one of the most powerful... -
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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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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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Cpp17
Chinese translation of C++17 The Complete Guide
...The content is organized into multiple parts: basic language features (e.g. structured binding, inline variables, enhanced switch, lambdas), template and compile-time features (e.g. fold expressions, class template argument deduction, constexpr improvements), and the additions to the standard library (e.g. std::optional, std::variant, std::string_view, file system, concurrency, and parallel algorithms). It also covers enhancements to existing STL components, new library utilities, and advanced topics like polymorphic memory resources (PMR), alignment, and generic programming improvements. -
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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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frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models
...Utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. Quite fast on one CPU core, and you can run multiple predictions in parallel, thus utilizing as many CPUs as you like to improve the overall prediction throughput of your application/pipeline. -
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gradle-completion
Gradle tab completion for bash and zsh
Bash and Zsh completion support for Gradle. This provides fast tab completion for: Gradle tasks for the current project and sub-projects. Gradle CLI switches (e.g. --parallel). Common Gradle properties (e.g. -Dorg.gradle.debug) It also handles custom default build files, so rootProject.buildFileName = 'build.gradle.kts' is supported. See instructions for bash or for zsh, then consider optional additional configuration. Download and place the plugin and completion script into your oh-my-zsh plugins directory. ... -
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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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DevSpace
The Fastest Developer Tool for Kubernetes
...DevSpace is a very lightweight, client-only CLI tool which uses your current kube-context, just like kubectl or helm. It does not require you to install anything inside your cluster and works out of the box with every Kubernetes cluster. Builds all images in parallel using Docker, kaniko or any custom build commands (e.g. using cloud build). Tags images according to customizable tag schema and updates tags in manifests and Helm chart values. Pushes images to any public or private registry and automatically creates pull secrets if needed. If your project depends on a service which is part of a different git repository, you can add a devspace.yaml config file to each of the repositories and define dependencies between them. -
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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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Bull
Queue package for handling distributed jobs and messages in NodeJS
...Get a complete overview of all your queues. Inspect jobs, search, retry, or promote delayed jobs. Metrics and statistics, and many more features. Queues are robust and can be run in parallel in several threads or processes without any risk of hazards or queue corruption. -
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Scala 2
Scala 2 compiler and standard library
Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries. Scastie is Scala + sbt in your browser! You can use any version of Scala, or even alternate backends such as Dotty, Scala.js, Scala Native, and Typelevel Scala. You can use any published library. You can save and... -
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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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fairseq2
FAIR Sequence Modeling Toolkit 2
...It supports multi-GPU and multi-node distributed training using DDP, FSDP, and tensor parallelism, capable of scaling up to 70B+ parameter models. The framework integrates seamlessly with PyTorch 2.x features such as torch.compile, Fully Sharded Data Parallel (FSDP), and modern configuration management.
