Search Results for "compute" - Page 2
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Showing 122 open source projects for "compute"
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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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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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BentoML
Unified Model Serving Framework
...Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. Orchestrate distributed inference graph with multiple models via Yatai on Kubernetes. Easily configure CUDA dependencies for running inference with GPU. Automatically generate docker images for production deployment. -
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oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the... -
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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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nivo
A rich set of dataviz components
...In order to use nivo, you have to install the @nivo/core package and then choose some of the scoped @nivo packages according to the charts you wish to use. Given an array of data series having an id and a nested array of points (with x, y properties), it will compute the line for each data series. All datum having null x or y will be treated as holes, thus portions of the corresponding line will be skipped. You can fully customize it using the circleComponent property to define your own, if you wish to do so you should have a look at the default SVG component to get started. Bar chart which can display multiple data series, stacked or side by side. ... -
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rspirv
Rust implementation of SPIR-V module processing functionalities
rspirv is a Rust-based parser, builder, and disassembler for SPIR-V, the intermediate binary format used in Vulkan and OpenCL for shaders and compute kernels. It’s part of the gfx-rs ecosystem, a suite of graphics tools aiming to provide cross-platform rendering capabilities in Rust. rspirv enables manipulation and inspection of SPIR-V modules, which is useful in shader compilers, graphics drivers, and development tools for low-level GPU programming. The library strictly follows the SPIR-V specification and is used in projects that need to generate, analyze, or modify shaders dynamically. -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase mining. The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. ... -
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Flutter Rust Bridge
Rust binding generator, feature-rich, but seamless and simple
...The project supports passing complex types, handling async operations and streams, and integrating with Flutter across mobile and desktop targets. By leaning on Rust’s memory safety and zero-cost abstractions, it enables compute-heavy tasks—parsing, crypto, image/audio processing, and more—without sacrificing Flutter’s developer experience. Build scripts and templates streamline packaging and distribution so the Rust side fits cleanly into CI and multi-platform releases. In practice, teams gain a maintainable way to share one performant Rust core across multiple Flutter apps while keeping the UI reactive and fast. -
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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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OpenNext
Open-source Next.js adapter for AWS
...It translates routing, API handlers, and middleware into deployable artifacts, handling edge cases such as image optimization, ISR, and streaming responses. Static assets are uploaded to object storage and cached at the edge, while dynamic routes are executed in short-lived compute with cold-start-aware build settings. The adapter aims to preserve the Next.js developer experience—file-based routing, app/router features, and incremental builds—while producing infrastructure that scales automatically. Configuration focuses on environment variables, build hooks, and sensible defaults rather than complex bespoke templates. ... -
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Micro Cloud
A distributed OS built for the Cloud
...Micro is the all encompassing end to end platform experience from source to running and beyond built with a developer first focus. Micro’s goal is to abstract away the complexity of building services for the Cloud. The cloud itself has gone through a huge boom through managed Compute and infrastructure services from the likes of AWS and others. It’s taken what was an operational burden and turned it into a suite of fully managed on demand services which can be used via APIs. Micro is built as a microservices architecture and abstracts away the complexity of the underlying infrastructure. -
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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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DCV Color Primitives
DCV Color Primitives Library
...You might want to propagate errors to the caller function or mix with some other error types. So far, buffers were sized taking into account the image pixel format and dimensions; However, you can use a function to compute how many bytes are needed to store an image of a given format and size. If your data is scattered in multiple buffers that are not necessarily contiguous, you can provide image planes. To take into account data which is not tightly packed, you can provide image strides. -
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Kornia
Open Source Differentiable Computer Vision Library
...It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. ... -
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cuDF
GPU DataFrame Library
...The RAPIDS suite of open-source software libraries aims to enable the execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. -
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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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Micronaut Data
Ahead of Time Data Repositories
Micronaut Data is a database access toolkit that uses Ahead of Time (AoT) compilation to pre-compute queries for repository interfaces that are then executed by a thin, lightweight runtime layer. Both GORM and Spring Data maintain a runtime meta-model that uses reflection to model relationships between entities. This model consumes significant memory and memory requirements grow as your application size grows. The problem is worse when combined with Hibernate which maintains its own meta-model as you end up with duplicate meta-models. ... -
19
roaring
Roaring bitmaps in Go (golang)
...For example, we may need to maintain a set of all documents or rows (represented by numerical identifiers) that satisfy some property. Besides adding or removing elements from the set, we need fast functions to compute the intersection, the union, the difference between sets, and so on. To implement a set of integers, a particularly appealing strategy is the bitmap (also called bitset or bit vector). -
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GenAI Processors
GenAI Processors is a lightweight Python library
...The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. -
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ngraph.path
Path finding in a graph
ngraph.path is a JavaScript library that implements efficient pathfinding algorithms for graphs, primarily designed to compute shortest paths in weighted or unweighted networks. It provides a clean API for constructing graph models, assigning weights to edges, and querying for optimal routes between nodes, making it useful for routing, games, maps, and network optimization. The library includes several algorithm implementations such as A*, Dijkstra’s, and breadth-first search, each suited to different types of graph structure and performance needs. ... -
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sebastian/diff
Diff implementation
diff is a standalone PHP library for computing and rendering textual differences, widely used to display “expected vs actual” output in testing tools. It supports line-based and character/word-granular comparisons so you can get both coarse and fine-grained views of changes. Output builders can produce formats like unified or context diffs, or more human-friendly renderings suitable for terminals and HTML reports. Internally, the library splits input into hunks and manages edge cases such as... -
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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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AWS SDK for Go v2
AWS SDK for the Go programming language
Welcome to the AWS SDK for Go. The AWS SDK for Go V2 provides APIs and utilities that developers can use to build Go applications that use AWS services, such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3). The SDK removes the complexity of coding directly against a web service interface. It hides a lot of the lower-level plumbing, such as authentication, request retries, and error handling. The SDK also includes helpful utilities. For example, the Amazon S3 download and upload manager can automatically break up large objects into multiple parts and transfer them in parallel. ...
