Search Results for "compute" - Page 3
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Showing 646 open source projects for "compute"
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ProbNumDiffEq.jl
Probabilistic Numerical Differential Equation solvers via Bayesian fil
ProbNumDiffEq.jl provides probabilistic numerical ODE solvers to the DifferentialEquations.jl ecosystem. The implemented ODE filters solve differential equations via Bayesian filtering and smoothing. The filters compute not just a single point estimate of the true solution, but a posterior distribution that contains an estimate of its numerical approximation error. -
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OpenMythos
A theoretical reconstruction of the Claude Mythos architecture
...The architecture incorporates advanced techniques such as mixture-of-experts routing, adaptive computation time, and multiple attention mechanisms to dynamically allocate compute where needed. It is highly configurable through a centralized configuration system, allowing experimentation with different architectural parameters such as loop depth, attention type. -
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Neon Database
Neon: Serverless Postgres. We separated storage and compute
The database you love, on a serverless platform designed to help you build reliable and scalable applications faster. Focus on building applications with time and money-saving features like instant provisioning, autoscaling according to load, and scale to zero. Instantly branch your data and schema to access isolated DB copies for development, CI/CD, and schema migrations with copy-on-write storage. Integrate it into your language or framework within minutes and unlock a simpler developer... -
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FlashMLA
FlashMLA: Efficient Multi-head Latent Attention Kernels
...The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to efficiently manage memory during decoding. On very compute-bound settings, it can reach up to ~660 TFLOPS on H800 SXM5 hardware, while in memory-bound configurations it can push memory throughput to ~3000 GB/s. The team regularly updates it with performance improvements; for example, a 2025 update claims 5 % to 15 % gains on compute-bound workloads while maintaining API compatibility. -
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Feynman
The open source AI research agent
Feynman is a command-line AI research agent designed to automate complex research workflows by orchestrating multiple specialized agents that collaborate to gather, analyze, and synthesize information into structured outputs. It operates as a “Claude Code for research,” allowing users to input natural language queries and receive fully developed, source-grounded research briefs, literature reviews, or experimental analyses. The system is built around a multi-agent architecture that includes... -
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Substra
Low-level Python library used to interact with a Substra network
An open-source framework supporting privacy-preserving, traceable federated learning and machine learning orchestration. Offers a Python SDK, high-level FL library (SubstraFL), and web UI to define datasets, models, tasks, and orchestrate secure, auditable collaborations. -
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Retrofit For Dart
Retrofit.dart is an dio client generator using source_gen
retrofit.dart is a type conversion dio client generator using source_gen and inspired by Chopper and Retrofit. If you want to parse models on a separate thread, you can take advantage of the compute function, just like Dio does when converting String data responses into json objects. Avoid using Map values, otherwise, multiple background isolates will be spawned to perform the computation, which is extremely intensive for Dart. -
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Firecracker
Secure and fast microVMs for serverless computing
Firecracker is an open-source virtualization technology developed by AWS for deploying secure micro-VMs (microVMs) that offer strong isolation with minimal overhead. Designed for serverless workloads (e.g., AWS Lambda, Fargate), it combines VM-level security with container-like performance and startup speed. -
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Bittensor
Internet-scale Neural Networks
Bittensor is a decentralized machine learning protocol that allows AI models to collaborate, learn, and earn tokens within a global network. It introduces a blockchain-based economy for neural networks, where participants are incentivized to contribute valuable knowledge and compute power. Bittensor combines peer-to-peer learning with on-chain rewards, creating a self-governing, scalable AI system that evolves without centralized control. It is a novel approach to aligning incentives in AI development, empowering open contributions while preserving model ownership and decentralization. -
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HTTP Shortcuts for Android
Android app to create home screen shortcuts
...Each shortcut, when clicked, triggers an HTTP request, with the possibility to process and display the response in various ways. Run arbitrary JavaScript before & after execution, which allows for lots of customization with features such as compute values such as timestamps, random numbers, UUIDs, hashes, HMACs, base64, etc. Parse JSON or XML and extract data from them. Show toast message or message dialogs. Vibrate or play notification sounds. Trigger other shortcuts to chain multiple HTTP requests. Interact with other devices and services by sending TCP or UDP packets, MQTT messages or using Wake-on-LAN. -
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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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Multica
The open-source managed agents platform
...It introduces a paradigm where agents can be assigned tasks, participate in discussions, and autonomously execute work while reporting progress and blockers in real time. The system integrates with multiple AI coding tools and provides a unified interface for managing tasks, compute environments, and agent execution pipelines. It includes both a web interface and a CLI that connects local or cloud-based runtimes to the platform, enabling flexible deployment and scaling. Multica emphasizes collaboration between humans and AI by allowing agents to operate alongside developers in shared workspaces. It also supports reusable skill accumulation, meaning that solutions generated by agents can be reused across projects to improve efficiency over time. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,... -
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Librosa
Python library for audio and music analysis
Librosa is a powerful Python library for analyzing and processing audio and music signals. Built on top of NumPy, SciPy, and matplotlib, it provides a wide range of tools for feature extraction, time-series manipulation, audio display, and music information retrieval. Whether you're building machine learning models for audio classification or visualizing spectrograms, Librosa is a go-to library for researchers and developers working in audio signal processing. -
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oneAPI.jl
Julia support for the oneAPI programming toolkit.
Julia support for the oneAPI programming toolkit. oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux. This package is still under significant development, so expect bugs and missing features. -
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GLM-4.1V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.1V — often referred to as a smaller / lighter version of the GLM-V family — offers a more resource-efficient option for users who want multimodal capabilities without requiring large compute resources. Though smaller in scale, GLM-4.1V maintains competitive performance, particularly impressive on many benchmarks for models of its size: in fact, on a number of multimodal reasoning and vision-language tasks it outperforms some much larger models from other families. It represents a trade-off: somewhat reduced capacity compared to 4.5V or 4.6V, but with benefits in terms of speed, deployability, and lower hardware requirements — making it especially useful for developers experimenting locally, building lightweight agents, or deploying on limited infrastructure. ... -
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Spice.ai OSS
A self-hostable CDN for databases
Spice is a portable runtime offering developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. Spice connects, fuses, and delivers data to applications, machine-learning models, and AI backends, functioning as an application-specific, tier-optimized Database CDN. The Spice runtime, written in Rust, is built-with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB.... -
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wgpu
Safe and portable GPU abstraction in Rust, implementing WebGPU API
wgpu is a safe and portable graphics library for Rust based on the WebGPU API. It is suitable for general purpose graphics and compute on the GPU. Applications using wgpu run natively on Vulkan, Metal, DirectX 11/12, and OpenGL ES; and browsers via WebAssembly on WebGPU and WebGL2. Angle is a translation layer from GLES to other backends, developed by Google. We support running our GLES3 backend over it in order to reach platforms with GLES2 or DX11 support, which aren't accessible otherwise. ... -
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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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CausalityTools.jl
Algorithms for detecting associations, dynamical influences
CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, independence testing, and causal inference. Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can... -
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Microsoft SEAL
Easy-to-use and powerful homomorphic encryption library
...Developed by the Cryptography and Privacy Research group at Microsoft, it enables software engineers to build end-to-end encrypted data storage and computation services that never have to procure the customer's key. Microsoft SEAL is very easy to use, compile and run in many different environments. Homomorphic encryption is an encryption scheme that allows the cloud to compute directly on the encrypted data, without requiring the data to be decrypted first. This results in encrypted computations remaining encrypted, decrypted only by the data owner using the secret key. -
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TurboQuant PyTorch
From-scratch PyTorch implementation of Google's TurboQuant
TurboQuant PyTorch is a specialized deep learning optimization framework designed to accelerate neural network inference and training through advanced quantization techniques within the PyTorch ecosystem. The project focuses on reducing the computational and memory footprint of models by converting floating-point representations into lower-precision formats while preserving performance. It provides tools for experimenting with different quantization strategies, enabling developers to balance... -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
...It is built on the MiniMax-Text-01 foundation and keeps the same massive parameter budget, but reworks the attention and training setup for better reasoning and test-time compute scaling. Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to support a native context length of 1 million tokens while using far fewer FLOPs than comparable reasoning models for very long generations. The team emphasizes efficient scaling of test-time compute: at 100K-token generation lengths, M1 reportedly uses only about 25 percent of the FLOPs of some competing models, making extended “think step” traces more feasible. ... -
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NVTX (NVIDIA Tools Extension Library)
C-based Application Programming Interface (API)
...It allows developers to insert markers, ranges, and events directly into their applications, providing contextual insight into how code executes on CPUs and GPUs. These annotations are visualized in tools such as NVIDIA Nsight Systems and Nsight Compute, enabling developers to identify performance bottlenecks, track execution flow, and correlate application behavior with hardware activity. The API is written in C and includes wrappers for C++ and Python, making it accessible across different programming environments and workloads. NVTX is particularly valuable in high-performance computing and AI workloads where understanding concurrency, memory usage, and kernel execution is critical for optimization. -
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OpenMeter
Metering and Billing for AI, API and DevOps
OpenMeter is an open-source metering and billing platform designed to collect, aggregate, and analyze usage events from APIs, cloud infrastructure, and software services in real time, enabling flexible usage-based billing for SaaS, AI, and DevOps offerings. It supports high-scale event ingestion and deduplication to accurately record how customers consume billable resources such as API calls, compute time, or storage, and then correlates that usage with payment systems and billing plans to automate invoicing and revenue recognition. The system includes metering, storage, cataloging of products and pricing rules, and tools to enforce limits or quotas, supporting both self-service customer portals and internal dashboards. OpenMeter integrates with payment processors, CRMs, and analytics systems, letting teams launch complex pricing models or subscription tiers without heavy custom engineering work.
