Search Results for "linux performance booster" - Page 5
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Showing 200 open source projects for "linux performance booster"
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory... -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
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UpTrain
Your open-source LLM evaluation toolkit
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison... -
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MLPerf
Reference implementations of MLPerf™ training benchmarks
This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for "real" performance measurements of software frameworks or hardware. Benchmarking the performance of training ML models on a wide variety of use cases, software, and hardware drives AI performance across the tech industry. The MLPerf Training working group draws on... -
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous... -
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PyDenseCRF
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs
PyDenseCRF is a Python library that provides a wrapper around the implementation of fully connected Conditional Random Fields (CRFs) developed by Philipp Krähenbühl and Vladlen Koltun. The project allows developers and researchers to integrate Dense CRF inference into Python-based machine learning pipelines, particularly for computer vision tasks such as image segmentation and labeling. Conditional Random Fields are probabilistic graphical models used to model contextual relationships... -
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Foolbox
Python toolbox to create adversarial examples
Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. Foolbox 3 is built on top of EagerPy and runs natively in PyTorch, TensorFlow, and JAX. Foolbox provides a large collection of state-of-the-art gradient-based and decision-based adversarial attacks. Catch bugs before running your code thanks to extensive type annotations in Foolbox. Foolbox is a Python library that lets you easily run adversarial attacks against machine... -
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AI-Aimbot
CS2, Valorant, Fortnite, APEX, every game
AI-Aimbot is a computer vision project that demonstrates how artificial intelligence can be used to automatically identify and target opponents in video games. The system uses an object detection model based on the YOLOv5 architecture to detect human-shaped characters in gameplay screenshots or video frames. Once a target is identified, the program automatically adjusts the player’s aim toward the detected target, effectively automating the aiming process in first-person shooter games. The... -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
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Alink
Alink is the Machine Learning algorithm platform based on Flink
Alink is Alibaba’s scalable machine learning algorithm platform built on Apache Flink, designed for batch and stream data processing. It provides a wide variety of ready-to-use ML algorithms for tasks like classification, regression, clustering, recommendation, and more. Written in Java and Scala, Alink is suitable for enterprise-grade big data applications where performance and scalability are crucial. It supports model training, evaluation, and deployment in real-time environments and... -
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spaGO
Self-contained Machine Learning and Natural Language Processing lib
A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. Spago is self-contained, in that it uses its own lightweight computational graph both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing. In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace to... -
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FEDML Open Source
The unified and scalable ML library for large-scale training
A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale. TensorOpera AI is the next-gen cloud service for LLMs & Generative AI. It helps developers to launch complex model training, deployment, and federated learning anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, easily, economically, and securely. Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three... -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common... -
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LLM Applications
A comprehensive guide to building RAG-based LLM applications
LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and... -
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fe4ml-zh
Feature Engineering for Machine Learning
fe4ml-zh is an open-source project that provides a Chinese translation and structured documentation of the book Feature Engineering for Machine Learning. The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that... -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that... -
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SIG Rust
Rust language bindings for TensorFlow
SIG Rust provides idiomatic Rust bindings for TensorFlow, making it possible for developers to work with TensorFlow functionality from within the Rust programming language. Rather than replacing TensorFlow itself, it acts as an integration layer that connects Rust applications to the TensorFlow C API. The repository is designed for developers who want Rust’s performance, safety, and systems programming strengths while still accessing TensorFlow’s machine learning capabilities. It includes... -
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MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the OpenMMLab project. The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction. The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. The modular... -
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Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. -
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AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and... -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The... -
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OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets that... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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T81 558
Applications of Deep Neural Networks
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network...
