Search Results for "cpu-g" - Page 4
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Showing 1138 open source projects for "cpu-g"
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Chinese-LLaMA-Alpaca 2
Chinese LLaMA-2 & Alpaca-2 Large Model Phase II Project
This project is developed based on the commercially available large model Llama-2 released by Meta. It is the second phase of the Chinese LLaMA&Alpaca large model project. The Chinese LLaMA-2 base model and the Alpaca-2 instruction fine-tuning large model are open-sourced. These models expand and optimize the Chinese vocabulary on the basis of the original Llama-2, use large-scale Chinese data for incremental pre-training, and further improve the basic semantics and command understanding of... -
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POCO
Cross-platform C++ libraries for building network applications
The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++... -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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auto-cpufreq
Automatic CPU speed & power optimizer for Linux
Automatic CPU speed & power optimizer for Linux. Actively monitors laptop battery state, CPU usage, CPU temperature, and system load, ultimately allowing you to improve battery life without making any compromises. -
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Lightweight ring buffer manager
Lightweight generic ring buffer manager library
The library provides generic FIFO ring buffer implementation. -
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mactop
Apple Silicon Monitor Top written in pure Golang
mactop is a terminal-based monitoring tool "top" designed to display real-time metrics for Apple Silicon chips. It provides a simple and efficient way to monitor CPU and GPU usage, E-Cores and P-Cores, power consumption, and other system metrics directly from your terminal. -
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CTranslate2
Fast inference engine for Transformer models
CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced... -
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Text Generation Web UI
A gradio web UI for running Large Language Models like LLaMA
... efficient text streaming. Parameter presets, 8-bit mode. Layers splitting across GPU(s), CPU, and disk. CPU mode, FlexGen, DeepSpeed ZeRO-3, API with streaming and without streaming. LLaMA model, including 4-bit GPTQ. RWKV model, LoRA (loading and training), Softprompts, and extensions. -
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DALI
A GPU-accelerated library containing highly optimized building blocks
..., cropping, resizing, and many other augmentations. These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting the performance and scalability of training and inference. DALI addresses the problem of the CPU bottleneck by offloading data preprocessing to the GPU. Additionally, DALI relies on its own execution engine, built to maximize the throughput of the input pipeline. -
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Tracy Profiler
Frame profiler
A real-time, nanosecond resolution, remote telemetry, hybrid frame, and sampling profiler for games and other applications. Tracy supports profiling CPU (Direct support is provided for C, C++, Lua and Python integration. At the same time, third-party bindings to many other languages exist on the internet, such as Rust, Zig, C#, OCaml, Odin, etc.), GPU (All major graphic APIs: OpenGL, Vulkan, Direct3D 11/12, OpenCL.), memory allocations, locks, context switches, automatically attribute... -
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llama2-webui
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere
Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac). -
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fgprof
fgprof is a sampling Go profiler that allows you to analyze On-CPU
fgprof is a sampling Go profiler that allows you to analyze On-CPU as well as Off-CPU (e.g. I/O) time together. Go's builtin sampling CPU profiler can only show On-CPU time, but it's better than fgprof at that. Go also includes tracing profilers that can analyze I/O, but they can't be combined with the CPU profiler. fgprof is designed for analyzing applications with mixed I/O and CPU workloads. This kind of profiling is also known as wall-clock profiling. If this is the first time you hear... -
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Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which... -
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
... on the NVIDIA developer website. Transform data (ETL) for preprocessing and engineering features. Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code. -
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WeakAuras
World of Warcraft addon that provides a powerful framework
.... Progress bars and textures that show the exact duration of auras. Displays based on auras, health, power (mana, rage, soul shards, holy power, etc.), cooldowns, combat events, runes, totems, items, and many other triggers. Preset and user-defined animations. Custom side-effects such as chat announcements or sounds. Grouping, which allows multiple displays to be positioned and configured at the same time. CPU optimizations such as conditional loading/unloading of displays, modularity, etc. -
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Halide
A language for fast, portable data-parallel computation
Halide is a programming language for fast, portable data-parallel computation. It was designed to make writing high-performance image and array processing code much easier on modern machines. It works on all major operating systems and with several CPU architectures (X86, ARM, MIPS, Hexagon, PowerPC) and GPU Compute APIs (CUDA, OpenCL, OpenGL, among others). It isn't a standalone programming language however; rather it is embedded in C++ which means that you write C++ code, building... -
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ShadowsocksR-native
ShadowsocksR (SSRoT) native implementation for all platforms
ShadowsocksR-native is a lightweight secured SOCKS5 proxy for embedded devices and low-end boxes. It's derived from Shadowsocks-libev. -
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Bento
Packer templates for building minimal Vagrant baseboxes
Bento is a project that encapsulates Packer templates for building Vagrant base boxes. A subset of templates are built and published to the bento org on Vagrant Cloud. These published boxes serve as the default boxes for kitchen-vagrant. -
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Texify
Math OCR model that outputs LaTeX and markdown
Texify is an OCR model that converts images or pdfs containing math into markdown and LaTeX that can be rendered by MathJax ($$ and $ are delimiters). It can run on CPU, GPU, or MPS. -
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FarmBot OS
The operating system and all related software
... with the Farmduino to send G and F commands and receive sensor and encoder data. Take photos with a USB or Raspberry Pi camera, and upload the photos to the web application. You must use a .img writing tool to write FarmBot OS onto the microSD card. We recommend downloading and installing balenaEtcher for this purpose. -
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Robusta KRR
Prometheus-based Kubernetes Resource Recommendations
Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance. -
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Pixie
Instant Kubernetes-Native Application Observability
... locally in the cluster. Pixie uses less than 5% of cluster CPU and in most cases less than 2%. PxL, Pixie’s flexible Pythonic query language, can be used across Pixie’s UI, CLI, and client APIs. -
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LiteNetLib 1.0 indev
Lite reliable UDP library for Mono and .NET
Lite reliable UDP library for .NET Framework 3.5, Mono, .NET Core 2.0, .NET Standard 2.0. Small packet size overhead (1 byte for unreliable, 3 bytes for reliable packets). Helper classes for sending and reading messages. Always use library sources instead of precompiled DLL files (because there are platform-specific #ifdefs and workarounds for unity bugs). Fast packet serializer (Usage manual). Automatic small packets merging. Automatic fragmentation of reliable packets. Automatic MTU... -
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gtop
System monitoring dashboard for terminal
...' for CPU usage, and 'm' for Memory usage. If you see question marks or other different characters, try to run it with the environment variables provided in the documentation. gtop is released under the MIT license. -
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Core ML Tools
Core ML tools contain supporting tools for Core ML model conversion
... performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive.