Search Results for "memory" - Page 32
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Showing 2497 open source projects for "memory"
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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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TensorFlow Model Optimization Toolkit
A toolkit to optimize ML models for deployment for Keras & TensorFlow
...Among many uses, the toolkit supports techniques used to reduce latency and inference costs for cloud and edge devices (e.g. mobile, IoT). Deploy models to edge devices with restrictions on processing, memory, power consumption, network usage, and model storage space. Enable execution on and optimize for existing hardware or new special purpose accelerators. Choose the model and optimization tool depending on your task. In many cases, pre-optimized models can improve the efficiency of your application. Try the post-training tools to optimize an already-trained TensorFlow model. ... -
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dqlite
Embeddable, replicated and fault tolerant SQL engine
Dqlite is a fast, embedded, persistent SQL database with Raft consensus that is perfect for fault-tolerant IoT and Edge devices. Dqlite (distributed SQLite) extends SQLite across a cluster of machines, with automatic failover and high-availability to keep your application running. It uses C-Raft, an optimised Raft implementation in C, to gain high-performance transactional consensus and fault tolerance while preserving SQlite’s outstanding efficiency and tiny footprint. Stable Golang client... -
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Fluent Bit Amazon Kinesis Firehose
A Fluent Bit output plugin for Amazon Kinesis Data Firehose
...However, we are pausing development on it and will focus on the high-performance version instead. If the features of the higher performance plugin are sufficient for your use cases, please use it. It can achieve higher throughput and will consume less CPU and memory. This plugin has been tested with Fluent Bit 1.2.0+. It may not work with older Fluent Bit versions. We recommend using the latest version of Fluent Bit as it will contain the newest features and bug fixes. We distribute a container image with Fluent Bit and these plugins. -
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Beef Programming Language
Beef Programming Language
...Beef allows for safely mixing different optimization levels on a per-type or per-method level, allowing for performance-critical code to be executed at maximum speed without affecting the debuggability of the rest of the application. Beef can detect memory leaks in real-time. As with most safety features in Beef, this can be turned off in Release builds for performance-critical applications. The Beef IDE supports productivity features such as autocomplete, fixits, reformatting, refactoring tools, type inspection, runtime code compilation (hot code swapping). -
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ArrayFire
ArrayFire, a general purpose GPU library
ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if you're interested and able to write top performing tensor functions. Together we can fulfill The ArrayFire Mission under an excellent Code of Conduct that promotes a respectful and friendly building experience. ... -
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fswatch
A cross-platform file change monitor with multiple backends
A cross-platform file change monitor with multiple backends: Apple OS X File System Events API, BSD kqueue, Solaris/Illumos File Events Notification, Linux inotify and a stat()-based backend. fswatch is a file change monitor that receives notifications when the contents of the specified files or directories are modified. fswatch implements four kinds of monitors. A monitor based on the File System Events API of Apple OS X. A monitor based on kqueue, an event notification interface introduced... -
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formidable
The most used, flexible, fast and streaming parser
The most used, flexible, fast and streaming parser for multipart form data. Supports uploading to serverless environments, AWS S3, Azure, GCP or the filesystem. Used in production. This project is semantically versioned and available as part of the Tidelift Subscription for professional-grade assurances, enhanced support and security. The maintainers of formidable and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the Open Source... -
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GRR
GRR Rapid Response, remote live forensics for incident response
GRR Rapid Response is an incident response framework focused on remote live forensics. It consists of a python client (agent) that is installed on target systems, and python server infrastructure that can manage and talk to clients. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR client is deployed on systems that one might want to investigate. On every such system, once... -
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AlaSQL
JavaScript SQL database for browser and Node.js for relational tables
AlaSQL.js - JavaScript SQL database for browser and Node.js. Handles both traditional relational tables and nested JSON data (NoSQL). Export, store, and import data from localStorage, IndexedDB, or Excel. We focus on speed by taking advantage of the dynamic nature of JavaScript when building up queries. Real-world solutions demand flexibility regarding where data comes from and where it is to be stored. We focus on flexibility by making sure you can import/export and query directly on data... -
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Typecho Blogging Platform
A PHP blogging platform, simple and powerful
With only 7 data tables and less than 400KB of code, a complete plug-in and template mechanism is complete. Ultra-low CPU and memory usage is enough to give full play to the maximum performance of the host. Native support for Markdown typesetting syntax, easy to read and write. Support various cloud hosts such as BAE/GAE/SAE, even in the face of sudden high traffic, it can easily cope with it. The meticulously polished operation interface is still a familiar feature, but more mature and with more options. ... -
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CXX
Safe interop between Rust and C++
...This is done together with any necessary static assertions needed for later in the build process to verify correctness. The result is an FFI bridge that operates at zero or negligible overhead, with no copying, serialization or memory allocation needed. -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write... -
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VoltAgent
Open Source TypeScript AI Agent Framework
An AI Agent Framework provides the foundational structure and tools needed to build applications powered by autonomous agents. These agents, often driven by Large Language Models (LLMs), can perceive their environment, make decisions, and take actions to achieve specific goals. Building such agents from scratch involves managing complex interactions with LLMs, handling state, connecting to external tools and data, and orchestrating workflows. VoltAgent is an open source TypeScript framework... -
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Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
...We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. It will likely only work on an RTX 3090, an RTX 2080 Ti, or high-end enterprise GPUs. Lower-end cards must reduce the n_neurons parameter or use the CutlassMLP (better compatibility but slower) instead. tiny-cuda-nn comes with a PyTorch extension that allows using the fast MLPs and input encodings from within a Python context. ... -
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AIMET
AIMET is a library that provides advanced quantization and compression
...For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. -
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ffsend
Easily and securely share files from the command line
...History tracking your files for easy management, ability to use your own Send hosts. Inspect or delete shared files, accurate error reporting. Streaming encryption and uploading/downloading, very low memory footprint. Intended for use in scripts without interaction. -
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RISC-V sandboxing library
The fastest RISC-V sandbox
RISC-V sandboxing library is a high-performance, ultra-low-latency RISC-V userspace emulator library written in modern C++, designed for embedding and sandboxing applications. Unlike full-system emulators, it focuses specifically on executing user-space programs, making it ideal for scenarios such as sandboxed execution, scripting engines, and high-performance server environments. The library is engineered to achieve extremely fast startup and execution times, with the ability to run large... -
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BeeAI Framework
Build production-ready AI agents in both Python and Typescript
BeeAI Framework is an open-source, production-grade toolkit designed for building intelligent AI agents and complex multi-agent systems that can reason, act, and collaborate to solve real-world problems at scale. It goes beyond simple prompt-based interactions by introducing rule-based governance and constraint enforcement, enabling developers to create agents with predictable and controllable behavior while still preserving advanced reasoning capabilities. The framework supports both Python... -
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rust-bert
Rust native ready-to-use NLP pipelines and transformer-based models
...The project ports many capabilities of the Hugging Face Transformers ecosystem into the Rust programming language. It allows developers to run state-of-the-art NLP models like BERT, GPT-2, and DistilBERT directly within Rust applications while maintaining high performance and memory efficiency. The library integrates with Rust machine learning infrastructure using crates such as tch-rs and ONNX Runtime for model execution. It also includes tokenization utilities, model architectures, and task-specific pipelines that simplify the development of NLP applications. Because Rust is known for its safety and performance, this project enables developers to deploy modern NLP models in production systems written in Rust. -
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multi-agent-shogun
Samurai-inspired multi-agent system for Claude Code
multi-agent-shogun is a multi-agent orchestration system designed to coordinate multiple AI coding agents working in parallel. Inspired by the hierarchy of a feudal Japanese military structure, the system organizes agents into roles such as Shogun, Karo, and Ashigaru, which correspond to strategist, coordinator, and worker agents. A user interacts primarily with the Shogun agent by issuing natural language instructions that describe the desired tasks. The system then automatically... -
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RTP-LLM
Alibaba's high-performance LLM inference engine for diverse apps
...The system focuses on improving throughput, latency, and resource utilization when running large models in production environments. It achieves this by implementing optimized GPU kernels, batching strategies, and memory management techniques tailored for transformer inference workloads. The framework is designed for large-scale AI services and is already used internally across several Alibaba platforms such as Taobao, Amap, and other business systems that rely on conversational or search-related AI services. RTP-LLM supports a wide variety of modern model architectures, including Qwen, DeepSeek, and Llama-based models, making it a flexible engine for deploying many different open-source LLMs. -
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LLM-Pruner
On the Structural Pruning of Large Language Models
LLM-Pruner is an open-source framework designed to compress large language models through structured pruning techniques while maintaining their general capabilities. Large language models often require enormous computational resources, making them expensive to deploy and inefficient for many practical applications. LLM-Pruner addresses this issue by identifying and removing non-essential components within transformer architectures, such as redundant attention heads or feed-forward... -
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nndeploy
An Easy-to-Use and High-Performance AI Deployment Framework
...The system supports multiple inference engines and hardware accelerators, allowing the same AI workflow to run on different platforms without significant modifications. nndeploy also includes performance optimization techniques such as parallel execution, memory reuse, and hardware-accelerated operations to improve inference speed. -
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Agentic Context Engine
Make your agents learn from experience
...In this workflow, one component generates solutions, another reflects on outcomes, and a third curates useful knowledge so it can be reused in future interactions. This architecture allows agents to gradually build persistent operational memory without requiring additional training datasets or model retraining. -
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MatMul-Free LM
Implementation for MatMul-free LM
...The architecture relies on quantization-aware training and lightweight operations to replace conventional dense matrix multiplications with more efficient alternatives. These optimizations can significantly reduce memory consumption and potentially improve computational efficiency during both training and inference. The repository provides implementations of models at several parameter scales and includes tools for experimenting with the architecture using modern machine learning frameworks.
