Search Results for "compute" - Page 4
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Showing 537 open source projects for "compute"
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The Pope Bot
Autonomous AI agent that you can configure and build
...It’s designed so that every action taken by the agent is logged as a git commit, giving users complete visibility into what the agent did, why it did it, and when, which makes actions auditable and reversible. The framework treats the repository itself as the agent’s “brain,” and GitHub Actions serve as the compute layer, enabling tasks to run securely without exposing sensitive API keys to the underlying AI. The system integrates with messaging platforms like Telegram, where users can interact with the bot, trigger actions, or receive notifications, and supports scheduling and automation through patterns of request handling. -
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Serverless Framework
The easy and open way to build serverless applications
Serverless Framework gives you everything you need to build serverless applications on any cloud. It provides structure, workflow automation and best practices out-of-the-box so you can deploy sophisticated serverless architectures. It uses new, event-driven compute services, such as AWS Lambda, Azure Functions, Google CloudFunctions and more. Serverless Framework lets you build apps made up of microservices that run in response to events. These auto-scale and will only charge you when they run, which means lesser costs for application maintenance. You can also create new or extend existing commands with its great selection of community-written plugins. -
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Cloud Storage FUSE
A user-space file system for interacting with Google Cloud Storage
...It supports performance optimizations like file caching, which stores frequently accessed data on local storage to significantly improve throughput and reduce latency. The system integrates with cloud-native environments such as Kubernetes and can be used in distributed architectures where multiple compute nodes access shared datasets. -
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Google Cloud Platform Go Samples
Sample apps and code written for Google Cloud
Google Cloud Platform Go Samples repository is a comprehensive collection of Go-based code examples that demonstrate how to build applications and services using Google Cloud Platform. It provides developers with practical implementations that cover a wide spectrum of cloud functionalities, including storage, compute, networking, and machine learning services. Each sample is designed to be easily reusable, allowing developers to copy code directly into their own projects as a starting point for development. The repository includes both simple quickstart examples and more advanced application patterns, often accompanied by documentation guides that explain how to deploy and run them in different environments. ... -
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CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will... -
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NATS
Server for NATS.io, the cloud and edge native messaging system
...With the ability to process millions of messages a second per server, you’ll find unparalleled efficiency with NATS. Save money by minimizing cloud costs with reduced compute and network usage for streams, services, and eventing. NATS self-heals and can scale up, down, or handle topology changes anytime with zero downtime to your system. Clients require zero awareness of NATS topology allowing you future proof your system to meet your needs of today and tomorrow. -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
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Attention Residuals (AttnRes)
Drop-in replacement for standard residual connections in Transformers
Attention Residuals is a research-driven architectural innovation for transformer-based models that replaces traditional residual connections with an attention-based mechanism to improve information flow across layers. In standard transformers, residual connections simply sum outputs from previous layers, which can lead to uncontrolled growth of hidden states and dilution of early-layer information in deep networks. Attention Residuals introduces a learnable softmax attention mechanism that... -
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Tarantool
Get your data in RAM, get compute close to data, enjoy the performance
In OLTP scenarios, Tarantool can be used instead of relational databases. Such a solution will work many times faster. With Tarantool, you can replace the traditional bundle of database & cache and reduce operational costs. Tarantool is tolerant of write-heavy loads. It also allows keeping full-featured applications close to the data, thus reducing data access network latency to zero. The open-source Community Edition lets you develop applications and speed up a system in operation. It... -
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NVIDIA AI Cluster Runtime (AICR)
Tooling for optimized and reproducible GPU-accelerated AI runtime
NVIDIA AI Cluster Runtime (AICR) is an emerging project within NVIDIA’s AI infrastructure ecosystem focused on enabling advanced AI compute and runtime workflows, though publicly available documentation remains limited. Based on its positioning within NVIDIA’s repositories, it is designed to support scalable AI runtime environments, potentially addressing challenges related to orchestration, resource management, or reproducible AI execution. The project likely aligns with NVIDIA’s broader strategy of building modular infrastructure layers that integrate with GPU-accelerated workloads and cloud-native systems. ... -
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VoxelMorph
Unsupervised Learning for Image Registration
...VoxelMorph approaches the problem using neural networks that learn to predict deformation fields that transform one image so that it aligns with another. Once the model has been trained, it can rapidly compute the transformation required to register new image pairs, significantly reducing computational time compared to classical registration algorithms. The framework supports both supervised and unsupervised learning approaches and is commonly used in medical imaging applications such as MRI alignment, anatomical analysis, and longitudinal studies. -
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ANE Training
Training neural networks on Apple Neural Engine via APIs
...The repository implements a from-scratch transformer training pipeline capable of running both forward and backward passes on ANE hardware without relying on CoreML, Metal, or GPU acceleration. It explores the internal software stack of the Apple Neural Engine by interfacing with private classes such as _ANEClient and compiling custom compute graphs in the MIL format. The project includes performance benchmarks and kernel breakdowns that show how different components of the training loop are distributed between the ANE and CPU. It is primarily intended as a research and educational proof of concept rather than a production library, highlighting what is technically possible with undocumented hardware access. -
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Agentic Coding Flywheel Setup
System tool for beginners wanting agentic engineering capabilities
Agentic Coding Flywheel Setup (ACFS) is a comprehensive environment bootstrap project that configures a full stack of tools for autonomous AI-assisted coding workflows. With a single shell installer, ACFS transforms a fresh compute environment into a ready-to-use development setup that includes modern shells, language runtimes, AI coding agents (like Claude Code, Codex CLI, and Gemini CLI), and a coordinated toolchain for orchestration and safety. The system is designed for developers who want to run multi-agent coding assistants on personal or VPS hosts with minimal manual configuration. ... -
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NovaSR
A lightning fast audio upsampler
...NovaSR is especially valuable for post-processing tasks in speech enhancement, TTS pipelines, and dataset restoration where low sampling rates degrade perceived audio clarity; the minimal model size also makes it suitable for edge and embedded use cases where memory is at a premium. Its performance can reach thousands of times realtime on modern GPUs, allowing massive audio batches to be processed with negligible compute overhead. -
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Knative Serving
Kubernetes-based, scale-to-zero, request-driven compute
Knative Serving defines a set of objects as Kubernetes Custom Resource Definitions (CRDs). These resources are used to define and control how your serverless workload behaves on the cluster. The service.serving.knative.dev resource automatically manages the whole lifecycle of your workload. It controls the creation of other objects to ensure that your app has a route, a configuration, and a new revision for each update of the service. Service can be defined to always route traffic to the... -
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melonJS
A fresh & lightweight javascript game engine
melonJS is an open-source HTML5 game engine that empowers developers and designers to focus on content. The framework provides a collection of composable entities and support for a number of third-party tools. Giving you a powerful combination that can be used wholesale or piecemeal. melonJS is a lightweight yet powerful HTML5 framework designed from the ground up to provide a true plugin-free 'write-once, run-everywhere' gaming platform. melonJS is an open-source project and supported by a... -
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x402
A payments protocol for the internet. Built on HTTP
...The protocol supports multiple payment networks, including blockchain-based systems and fiat-compatible facilitators, making it flexible and network-agnostic. It is particularly suited for machine-to-machine transactions, such as AI agents paying for APIs, data, or compute resources without human intervention. The system eliminates the need for accounts, sessions, or traditional payment rails by using cryptographic authorization and wallet-based transactions. Developers can integrate payment requirements into endpoints with minimal code using middleware and SDKs. Overall, x402 introduces a new economic layer to the web, enabling micropayments and pay-per-use services natively over HTTP. -
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bitnet.cpp
Official inference framework for 1-bit LLMs
...At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources. -
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PyTorch3D
PyTorch3D is FAIR's library of reusable components for deep learning
...It’s designed to make it easy to build and train neural networks that work directly with 3D data such as meshes, point clouds, and implicit surfaces. The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through full 3D rendering processes. Researchers use it for tasks like shape generation, reconstruction, view synthesis, and visual reasoning. PyTorch3D also includes utilities for loading, transforming, and sampling 3D assets, so models can be trained end-to-end from 2D supervision or partial data. Its modular design allows easy extension—components like differentiable rasterizers, mesh blending, or signed distance field (SDF) modules can be swapped or combined to test new architectures quickly. -
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Taichi
Productive, portable, and performant GPU programming in Python
Taichi is an open-source, embedded DSL within Python designed for high-performance numerical and physical simulations. It uses JIT compilation (via LLVM and its runtime TiRT) to offload compute-heavy code to CPUs, GPUs, mobile devices, and embedded systems. With built-in support for sparse data structures (SNode), automatic differentiation, AOT deployment, and compatibility with CUDA, Vulkan, Metal, and OpenGL ES, it empowers disciplines like simulation, graphics, AI, and robotics -
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TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). TextDistance show benchmarks... -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities. Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks like image classification, object detection, and segmentation. ... -
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StableSwarmUI
Multi-user UI for managing and running Stable Diffusion workflows tool
...It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. StableSwarmUI is built to work alongside backend systems that execute the actual image generation, allowing separation between user interaction and compute workloads. It also emphasizes scalability, making it useful for setups where multiple jobs need to be processed efficiently. Overall, it serves as a coordination layer for Stable Diffusion usage rather than a standalone model implementation. -
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node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama
...By using native bindings and optimized model execution, the framework allows developers to integrate advanced language model capabilities into desktop applications, server software, and command-line tools. The system automatically detects the available hardware on a machine and selects the most appropriate compute backend, including CPU or GPU acceleration. Developers can use the library to perform tasks such as text generation, conversational chat, embedding generation, and structured output generation. Because it runs models locally, the platform is particularly useful for privacy-sensitive environments or offline AI deployments. -
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
...TE also includes a framework-agnostic C++ API that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for many deep learning models.
