Search Results for "multi-objective optimization"
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Showing 21 open source projects for "multi-objective optimization"
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Discover and manage administrator, service, and web app passwords, keys, and identities. Automate management with approval workflows. Centrally control, audit, monitor, and record all access to critical IT assets.
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
...), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
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BISHENG
BISHENG is an open LLM devops platform for next generation apps
BISHENG is an open LLM application DevOps platform, focusing on enterprise scenarios. It has been used by a large number of industry-leading organizations and Fortune 500 companies. "Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. Everyone is welcome to participate. -
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Open Source Vizier
Python-based research interface for blackbox
Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale. Allows a user to setup an OSS Vizier Server, which can host black-box optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions. Defines abstractions and utilities for implementing new optimization algorithms... -
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Aphantasia
CLIP + FFT/DWT/RGB = text to image/video
... (including multi-language from SBERT), continuous mode to process phrase lists (e.g. illustrating lyrics), pan/zoom motion with smooth interpolation. Direct RGB pixels optimization (very stable) depth-based 3D look (courtesy of deKxi, based on AdaBins), complex queries: text and/or image as main prompts, separate text prompts for style and to subtract (avoid) topics. Starting/resuming process from saved parameters or from an image. -
Yooz provides the smartest, most powerful, and easiest-to-use cloud-based E-invoicing and Purchase-to-Pay automation solution. It delivers unmatched savings, speed, and security with affordable zero-risk subscriptions to more than 5,000 customers and 300,000 users worldwide.
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload... -
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Ray
A unified framework for scalable computing
... model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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DeepXDE
A library for scientific machine learning & physics-informed learning
... stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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Jupyter Enterprise Gateway
Enables Jupyter Notebooks to share resources across clusters
Jupyter Enterprise Gateway is a headless web server with a pluggable framework for anyone supporting multiple notebook users in a managed-cluster environment. Some of the core functionality it provides is better optimization of compute resources, improved multi-user support, and more granular security for your Jupyter notebook environment - making it suitable for enterprise, scientific, and academic implementations. From a technical perspective, Jupyter Enterprise Gateway is a web server... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
PaddleX is a deep learning full-process development tool based on the core framework, development kit, and tool components of Paddle. It has three characteristics opening up the whole process, integrating industrial practice, and being easy to use and integrate. Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the... -
BackBox seamlessly integrates with network monitoring and NetOps platforms and automates configuration backups, restores, and change detection. BackBox also provides before and after config diffs for change management, and automated remediation of discovered network security issues.
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TensorFlow Ranking
Learning to rank in TensorFlow
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. Commonly used loss functions including pointwise, pairwise, and listwise losses. Commonly used ranking metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library... -
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Auto-PyTorch
Automatic architecture search and hyperparameter optimization
... (forecasting). The newest features in Auto-PyTorch for tabular data are described in the paper "Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL" (see below for bibtex ref). Details about Auto-PyTorch for multi-horizontal time series forecasting tasks can be found in the paper "Efficient Automated Deep Learning for Time Series Forecasting" (also see below for bibtex ref). -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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CachyOS
Blazingly Fast & Customizable Linux distribution
... packages are compiled with Full LTO and other optimization flags. Depending on the march your cpu have it is compiled against x86-64, x86-64-v3 or x86-64-v4. For the Desktop Environment we provide basic theming. In CachyOS-Hello can be also security related packages enabled, like: DNScrypt-proxy Firejail apparmor Security and Performance! https://discord.gg/qJqj94uFwE https://t.me/+zCzPX4cAFjk1MTY -
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ImageRevise
ImageRevise - Effortless Image Resizing & Optimization Tool
ImageRevise is a streamlined Tkinter-based application that makes image resizing and optimization effortless. With its intuitive interface, you can easily resize images while maintaining the aspect ratio, add custom watermarks with customizable positions, and optimize images for web use by adjusting quality levels. The application also lets you view and remove EXIF metadata, ensuring privacy and reducing file size. Features like multi-threaded image processing keep the app responsive during... -
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Hyperfit
Software for calibration of hyperelastic constitutive models
This software allows to fit various hyperelastic constitutive models. Optimal set of parameters of a selected constitutive model can be identified. Many advanced setting, methods and corrections are available to get optimal results according to the user's preference. -
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Snap7
32/64 bit multi-platform Ethernet S7 PLC communication suite
Snap7, through three specialized components: Client and the inedited Server and Partner, allows you to definitively integrate your PC based systems into a PLC automation chain. Designed to transfer large amounts of hi-speed data in industrial facilities, it scales easily, down to small Linux Arm boards such as Raspberry PI. Hi level object oriented wrappers are provided, currently C/C++, .NET/Mono, Pascal, LabVIEW, Python with many source code examples. Very easy to use, a full... -
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Coach
Enables easy experimentation with state of the art algorithms
... to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning. -
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Metaboflux is a generic approach for predicting flux distribution in metabolic networks under multiple and various constraints deducted from the experiments, to increase the biological relevance of the model.
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This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
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A script(ing/ed/able) optimizing compiler for the C and Objective-C languages (and in the future other languages such as C++, D and Java). Written in a scripting language to allow for experimentation with code generation and optimization techniques.
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PyOptFrame-LEGACY
PyOptFrame-LEGACY is Python OptFrame v2. Newest version v5 on github.
PyOptFrame-LEGACY is a Python version of OptFrame v2, proposed in 2011, now superseeded in 2021 by v5 on GitHub and PIP. The main objective is to provide the same interface to OptFrame C++ optimization framework, including classic metaheuristics such as genetic algorithms, simulated annealing, variable neighborhood search, first/best/multi-improvement, hill climbing, and multi-objective methods such as nsga-ii. See NEWEST version v5 on GitHub and PIP. Please try Official pyoptframe on https...
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