Search Results for "optimizer for" - Page 3
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Showing 162 open source projects for "optimizer for"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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artikelschreiber
Frontend and Backend Code for ArtikelSchreiber.com and UNAIQUE.NET
Frontend and Backend Code for ArtikelSchreiber.com and UNAIQUE.NET Text Generator deutsch - Dein KI Text Generator kostenlos mit Künstlicher Intelligenz The Software as a Service can be found here: SEO Optimizer: Ghost Writer - Hausarbeiten schreiben mit KI and KI Text Generator This product includes software developed by Sebastian Enger, M.Sc. Copyright (c) 2023, Sebastian Enger, M.Sc. All rights reserved. Frontend and Backend Source Code for Project: https://github.com/sebastianenger1981/ https://www.artikelschreiber.com/ https://www.artikelschreiben.com/ https://www.unaique.net/ https://www.artikelschreiber.com/opensource/ https://www.unaique.com/ -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. ... -
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V8 Perf
Notes and resources related to v8 and thus Node.js performance
...It explains how V8 represents JavaScript data internally and how those representations can influence execution speed. Compiler documentation explores the Ignition interpreter, TurboFan optimizer, optimization decisions, and deoptimization behavior. Memory sections explain heap organization, garbage collection, object references, allocations, and leak investigation. Profiling material covers inspection tools, runtime flags, performance analysis, and flamegraph generation. Additional notes examine snapshots, code caching, runtime functions, and JavaScript language features from a performance perspective. ... -
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Elephas
Distributed Deep learning with Keras & Spark
...Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. The "master" model on the driver is updated by an optimizer, which takes gradients either synchronously or asynchronously. Hyper-parameter optimization with elephas is based on hyperas, a convenience wrapper for hyperopt and keras. -
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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CSSO
CSS minifier with structural optimizations
CSSO (CSS Optimizer) is a CSS minifier. It performs three sorts of transformations: cleaning (removing redundant), compression (replacement for the shorter forms), and restructuring (merge of declarations, rules, and so on). As a result, an output CSS becomes much smaller in size. One of CDN services like unpkg or jsDelivr can be used. By default (for short path) the ESM version is exposed. -
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YOLOV3 Pytorch
This is a source code for yolo3-pytorch
...It includes utilities for annotation conversion, anchor generation, image prediction, video prediction, batch prediction, FPS measurement, heatmap output, and mAP evaluation. The project added multi-GPU training, target count statistics, learning rate scheduling with step and cosine options, and optimizer selection between Adam and SGD. It also includes adaptive learning rate adjustment based on batch size, image cropping, many configurable parameters, and expanded comments for easier study. It is well suited for learners and developers who want a hands-on YOLOv3 codebase in PyTorch. -
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YOLOV4 Pytorch
This is a source code for YoloV4-pytorch that can be used to train you
...The project added multi-GPU training, seed settings for reproducible results, adaptive learning rate behavior based on batch size, and both step and cosine learning rate schedules. It also supports Adam and SGD optimizer choices, image cropping, adjustable parameters, and extensive code comments. It is a useful educational and applied repository for users who want to understand or customize YOLOv4 in PyTorch. -
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fastPTOptimizer
a fork of the Panorama Tools / libpano13 with a fast PTOptimizer
...fastPTOptimizer decreases the processing time for large panoramas (a speedup factor of 100 or more can be achieved). This is possible by exploiting the sparseness of the mathematical optimization problem. The original PTOptimizer uses the well proven Levenberg Marquardt optimizer lmdif from the minpack software. fastPTOptimizer uses a "sparse" optimizer lmdif_sparse that uses the SuiteSparse library. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. ... -
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Mycat2
MySQL Proxy using Java NIO based on Sharding SQL, Calcite
...Any cross-currant cross-table join query, support cross-currant cross-table non-associated query, support cross-currant cross-table association query, support cross-currant cross-table Window grammar, support global secondary index, limited support storage process, support visualization configuration. Support parallel extraction of result sets, support the automatic transfer of back-end result sets, support multiple routing notes, and optimizer notes. Parameterization of the requested SQL, cache physics execution plan, and request of the same parameterization SQL, will avoid some analysis and optimization processes. -
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Hugin++
a fork of the Hugin project with a fast geometric optimizer
This is a fork of the Hugin project, a Panorama photo stitcher. Hugin++ is linked with fastPTOptimizer (a fork of the libpano library). When you have only a small or medium number of images and control points, the original Hugin does the geometric optimization in a fairly short time. However if you have a large number of images (several hundrets or more than thousand) and many control points, the optimization typically needs much time (up to several days). With Hugin++ the time for... -
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Faster-Rcnn
This is a pytorch implementation library of faster-rcnn
...It supports backbone options through pretrained VGG and ResNet weights, making it useful for comparing feature extractors. The project also includes learning rate scheduling through step and cosine methods, optimizer choices between Adam and SGD, adaptive learning rate behavior based on batch size, image cropping, FPS testing, video prediction, and batch prediction. It is a practical reference for users who want a more classical two-stage detector workflow in PyTorch. -
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Img Optimizer Gradle Plugin
Gradle plugin for optimizing PNGs
Img Optimizer Gradle Plugin is a build-time utility for Android projects that automatically compresses PNG images to reduce APK size. It supports both lossless compression (no quality loss) and near-lossless or more aggressive compression modes, depending on configuration. The plugin runs during the Gradle build cycle, scanning image assets and applying optimizations transparently, which helps minimize the app’s binary footprint without manual image editing. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
...It provides pretrained weight workflows for MobileNetV2 and Xception backbones and notes that the correct backbone should be selected during training and prediction. The project also supports multi-GPU training, multiple backbones, learning rate schedules with step and cosine options, optimizer selection, and adaptive learning rate behavior based on batch size. It is useful for users who want a stronger semantic segmentation baseline than U-Net for scene-level segmentation tasks. -
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ML++
A library created to revitalize C++ as a machine learning front end
...This is especially important in the world of ML, as new algorithms and techniques are being developed day by day. Here are a couple of things currently being developed for ML++. Call the optimizer that you would like to use. For iterative optimizers such as gradient descent, include the learning rate, epoch number, and whether or not to utilize the UI panel. -
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The ASCO project aims to bring circuit optimization capabilities to existing SPICE simulators using a high-performance parallel differential evolution (DE) optimization algorithm. It supports Eldo, HSPICE, LTspice, Spectre, and Qucs.
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Algobot
Cryptocurrency trading bot with a graphical user interface
Cryptocurrency trading bot that allows users to create strategies and then backtest, optimize, simulate, or run live bots using them. Telegram integration has been added to support easier and remote trading. Please note that Algobot requires TA-LIB. You can view instructions on how to download TA-LIB. For Windows users, it's best to download the .whl package for your Python install and pip install it. For Linux and MacOS users, there's excellent documentation available. Create graphs with... -
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scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
...The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an optimizer, run iterations, and inspect the best solution. Genetic algorithm operators can be replaced through user-defined functions or subclassing. Runs may continue from earlier iterations instead of restarting. Vectorization, threading, multiprocessing, caching, examples, and plotting support help users experiment with performance and convergence. -
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SimSiam
PyTorch implementation of SimSiam
SimSiam is a PyTorch implementation of “Exploring Simple Siamese Representation Learning” by Xinlei Chen and Kaiming He. The project introduces a minimalist approach to self-supervised learning that avoids negative pairs, momentum encoders, or large memory banks—key complexities of prior contrastive methods. SimSiam learns image representations by maximizing similarity between two augmented views of the same image through a Siamese neural network with a stop-gradient operation, preventing... -
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ProGuard Java Optimizer and Obfuscator
Java class file shrinker, optimizer, obfuscator, and preverifier
ProGuard has moved to Github now: https://github.com/Guardsquare/proguard ProGuard is a free Java class file shrinker, optimizer, obfuscator, and preverifier. It detects and removes unused classes, fields, methods, and attributes. It optimizes bytecode and removes unused instructions. It renames the remaining classes, fields, and methods using short meaningless names. ProGuard is useful for making code more compact and more efficient, on the desktop, on tablets, on smartphones, and on embedded devices. ... -
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King-Tweaks
Improve the user-experience systemlessly with many tweaks.
A magisk module tweaker / optimizer which have the objective of improving the user-experience. -
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Text Gen
Almost state of art text generation library
...Something sweet built with Tensorflow and Pytorch(coming soon). Load your data, your data must be in a text format. Download the example data from the example folder. Tune your model to know the best optimizer, activation method to use. -
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An open source Solver-compatible optimization engine for Microsoft Excel on Windows and Mac using the Coin-OR CBC linear and integer programming optimizer. You can also use other solvers like Gurobi (if you have it installed), solvers on the NEOS cloud, and various non-linear solvers are supported too.
