Search Results for "compute"
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Showing 20 open source projects for "compute"
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Paessler - Monitor Your Whole Network in MinutesWaiting weeks for a monitoring rollout isn't an option when infrastructure doesn't stop running. PRTG's auto-discovery scans your network and suggests from over 200 pre-configured sensor types, so you're watching servers, applications and devices within minutes, not after a multi-week deployment. Enterprise-strength monitoring, without the enterprise complexity. Start your free trial today.
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Bacalhau
Community-driven, simple, yet powerful framework
Bacalhau is a decentralized compute platform for running jobs on data stored across distributed networks, like IPFS or Filecoin, without moving the data to centralized cloud environments. It allows developers to run containerized workloads close to where the data lives, reducing latency, cost, and privacy risks. Bacalhau supports various runtime environments and is designed to make decentralized data processing as accessible as traditional cloud computing. -
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Hono
Web framework built on Web Standards
Hono is a small, simple, and ultrafast web framework built on Web Standards. It works on any JavaScript runtime: Cloudflare Workers, Fastly Compute, Deno, Bun, Vercel, AWS Lambda, Lambda@Edge, and Node.js. The router RegExpRouter is really fast. The hono/tiny preset is under 14kB. Using only Web Standard APIs. Works on Cloudflare, Fastly, Deno, Bun, AWS, or Node.js. The same code runs on all platforms. Hono has built-in middleware, custom middleware, third-party middleware, and helpers. ... -
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Argo Workflows
Workflow engine for Kubernetes
...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. Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. Argo Workflows is the most popular workflow execution engine for Kubernetes. It can run 1000s of workflows a day, each with 1000s of concurrent tasks. ... -
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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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Edit Banana
Edit Banana: A framework for converting statistical figures
...The tool focuses on accessibility, giving hobbyists, content creators, and small teams a way to produce polished visuals without downloading heavyweight software or managing local compute resources. Through AI-driven features like content-aware fill and stylistic adjustments, users can modify or replace regions of an image with contextually relevant content that blends seamlessly with the rest of the composition. -
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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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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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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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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. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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future
R package: future: Unified Parallel and Distributed Processing in R
...It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions, managing of packages, RNG, etc. Automatic detection and export of global objects and functions needed by future expressions, so the user doesn’t need to manage that manually. Ability to control how futures are resolved. -
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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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Acorn
A simple application deployment framework built on Kubernetes
...The Acornfile is built and packaged up into a single artifact that contains everything needed to deploy your application and can be shared with a link that will allow it's recipients to have a one-click deployment into a sandbox environment. Acorn Sandboxes are free compute environments in the cloud for anyone with a GitHub account to run, test, and develop Acorns. Sandboxes can be used as often as you like to run Acorns for up to 2 hours at a time. Sandbox environments allow for consuming up to 4GB of ram and up to 10GB of storage space. Acorns launched in the sandbox environment can be accessed via a public URL with SSL. ... -
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Rocket Chip
Rocket Chip Generator
...A diplomacy framework (LazyModules) lets designers wire components with negotiated parameters, enabling reuse and rapid exploration of different cache sizes, port counts, and memory hierarchies. The generator supports custom accelerators through the RoCC interface, allowing domain-specific compute units to be plugged into the pipeline with shared cache and memory semantics. Tooling integrates with FIRRTL, Verilator, and commercial EDA flows, and the ecosystem around Rocket Chip (e.g., Chipyard) adds harnesses, peripherals, and verification infrastructure. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. -
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TensorFlow MacOS
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute
This repository provided a pre-release of TensorFlow and TensorFlow Addons tailored for macOS 11+ with native hardware acceleration via Apple’s ML Compute, supporting both Apple Silicon (M1) and Intel Macs. It shipped ready-made Python 3.8 wheels and install scripts so developers could quickly get an accelerated stack running without building from source. As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. ... -
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"CUDA Templates" is a collection of C++ template classes and functions which provide a consistent interface to NVidia's "Compute Unified Device Architecture" (CUDA), hiding much of the complexity of the underlying CUDA functions from the programmer.
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IPS Framework
A simple Python framework for loosely-coupled multiphysics simulations
...One of the novel features of the IPS framework is its ability to support parallelism at multiple levels: components can launch individual parallel tasks, and also launch multiple tasks concurrently. The framework can execute multiple components concurrently, and even multiple simulations, all within the same pool of compute nodes. -
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aframe-react
Build virtual reality experiences with A-Frame and React
...React wastes a lot of cycles and incurs a lot of memory garbage. aframe-react is often abused where it is too easy to place 3D/real-time logic at the React layer, causing poor performance (e.g., doing React renders on ticks). aframe-react applications frequently ignore the prescribed ECS framework of A-Frame. Internally, React does tons of computation to compute what changed, and flushes it to the entire application. It is apparent React ecosystem does not care much about memory as most examples allocate functions and objects in the render method, and where immutables are popular. -
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GPUBench is a suite of tools useful in evaluting the compute performance of programmable graphics hardware. The tests are designed to provide information valuable to developers utilizing graphics hardware to accelerate compute intensive applications.
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