Search Results for "optimizer for" - Page 2
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Showing 133 open source projects for "optimizer for"
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Codeflash
Optimize your code automatically with AI
Codeflash is a general-purpose optimizer for Python that uses advanced large language models (LLMs) to automatically generate, test, and benchmark multiple optimization ideas, then creates merge-ready pull requests with the best improvements for your code. Optimize an entire existing codebase by running codeflash --all. Automate optimizing all future code you will write by installing Codeflash as a GitHub action. -
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OpenVINO
OpenVINO™ Toolkit repository
...Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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Binaryen
Compiler infrastructure and toolchain library for WebAssembly
...Binaryen's internal IR uses compact data structures and is designed for completely parallel codegen and optimization, using all available CPU cores. Binaryen's IR also compiles down to WebAssembly extremely easily and quickly because it is essentially a subset of WebAssembly. Binaryen's optimizer has many passes (see an overview later down) that can improve code size and speed. These optimizations aim to make Binaryen powerful enough to be used as a compiler backend by itself. -
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SkillOpt
Text-space optimizer that trains reusable natural-language skills
SkillOpt is a Microsoft research project for improving frozen LLM agents by optimizing reusable natural-language skill documents. Instead of changing model weights, it treats a compact skill file as the trainable state of the agent. The system learns from agent rollouts, reflection, bounded edits, and validation gates to produce better instructions over time. Its output is a deployable best_skill.md artifact that can be reused across agent tasks. The project is focused on making agents more... -
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LLM From Scratch
Build and train a GPT-style language model
...Inspired by Andrej Karpathy’s nanoGPT, the project emphasizes learning through direct implementation and experimentation rather than black-box usage. The workshop documentation explains concepts such as self-attention, embeddings, gradient clipping, optimizer scheduling, and decoding strategies in a practical and approachable way. -
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EAGO.jl
A development environment for robust and global optimization
EAGO is an open-source development environment for robust and global optimization in Julia. EAGO is a deterministic global optimizer designed to address a wide variety of optimization problems, emphasizing nonlinear programs (NLPs), by propagating McCormick relaxations along the factorable structure of each expression in the NLP. Most operators supported by modern automatic differentiation (AD) packages (e.g., +, sin, cosh) are supported by EAGO and a number of utilities for sanitizing native Julia code and generating relaxations on a wide variety of user-defined functions have been included. ... -
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neural-style in TensorFlow
Neural style in TensorFlow
...It creates a new image by combining the content of one image with the artistic style of one or more style images. The project uses TensorFlow automatic differentiation and the Adam optimizer rather than the original L-BFGS approach. Users can run it from the command line by providing a content image, style image inputs, and an output path. It supports checkpoint outputs, iteration control, style blending, and hyperparameter tuning for content weight, style weight, and learning rate. Overall, it is a focused research-style image generation tool for experimenting with artistic transfer and visual optimization. -
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with... -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation /... -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter... -
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Robyn
Experimental, AI/ML-powered and open sourced Marketing Mix Modeling
Robyn is an open-source, AI/ML-powered Marketing Mix Modeling (MMM) toolkit developed by Meta Marketing Science under the “facebookexperimental” GitHub umbrella. Its goal is to democratize rigorous MMM: what traditionally required expert statisticians and expensive consulting becomes accessible to any company with data. Robyn takes in historical data (spends on different marketing channels, conversions, or revenue, and optional context or organic-media variables) and uses a combination of... -
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OptiPNG
Advanced PNG optimization program
OptiPNG is a PNG optimizer that recompresses image files to a smaller size, without losing any information. This program also converts external formats (BMP, GIF, PNM and TIFF) to optimized PNG, and performs PNG integrity checks and corrections. -
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CCleaner
Clean up, speed up, and optimize your PC
CCleaner is a trusted PC optimization tool for Windows or Mac designed to clean, speed up, and secure your computer. It removes junk files, temporary data, and broken settings that slow down system performance over time. The software includes powerful modules like Performance Optimizer, Driver Updater, and Cloud Drive Cleaner, helping you reclaim storage space and improve efficiency. With a simple interface, it provides one-click solutions for beginners while also offering advanced features for power users. CCleaner enhances privacy by deleting browsing traces, cookies, and trackers across major web browsers. ... -
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UglifyJS 3
JavaScript parser, mangler, compressor, beautifier toolkit
...A parser which produces an abstract syntax tree (AST) from JavaScript code. A code generator which outputs JavaScript code from an AST, also providing the option to get a source map. A compressor (optimizer). it uses the transformer API to optimize an AST into a smaller one. A mangler, reduce names of local variables to (usually) single-letters. A scope analyzer, which is a tool that augments the AST with information about where variables are defined/referenced etc. A tree walker, a simple API allowing you to do something on every node in the AST. ... -
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JSoko
The Sokoban game implemented in Java.
JSoko is a Java program for playing the Sokoban game. It features path finding, auto push, auto solving, undo/redo, deadlock detection, and more. -
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OSX-Optimizer
Optimize MacOS, Shell scripts to speed up your mac boot time
OSX-Optimizer is a collection of shell-based tweaks for macOS designed to speed up boot time, improve perceived performance, and reduce unnecessary background activity, especially in virtualized environments. The README emphasizes that macOS can be heavily modified using the defaults command, and many of the optimizations are simply well-chosen combinations of defaults and system utilities. -
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Modcomp XPL Compiler
XPL Compiler for the Modcomp III/IV and Classic
...The first was written in the late 1970's and released to the Modcomp Users Group (MUSE). The second compiler is an extension of the first that includes a Common Sub-Expression optimizer. XPL was used to write the HAL/S compiler for NASA about the same time this XPL compiler was being developed but this is not the compiler used to create HAL/S. I am in need of technical documentation for the Modcomp Classic and the MAX IV operating system. Please let me know in the discussion section is you have documentation from Modular Computer Systems. -
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comfymodeldownloader
Automate ComfyUI model downloads via URL or name into correct folders
Short Description Tired of red error nodes? The ComfyUI Model Downloader automatically analyzes your workflows via AI, tracks down missing models, and manages your heavy files with an integrated Archive Manager. The ultimate SSD-saver and a must-have standalone Java tool for every ComfyUI power user! We’ve all been there... You find an amazing workflow, drag it into ComfyUI, and... RED. Error messages everywhere because you're missing specific models. You spend the next 20 minutes... -
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Bytecoder
Framework to interpret and transpile JVM bytecode to JavaScript
Bytecoder is a Rich Domain Model for Java Bytecode and Framework to interpret and transpile it to other languages such as JavaScript, OpenCL or WebAssembly. The JVM Bytecode is parsed and transformed into an intermediate representation. This intermediate representation is passed thru optimizer stages and sent to a backend implementation for target code generation. The JavaScript backend transforms the intermediate representation into JavaScript. -
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Greenplum Database
Massive parallel data platform for analytics, machine learning and AI
Rapidly create and deploy models for complex applications in cybersecurity, predictive maintenance, risk management, fraud detection, and many other areas. With its unique cost-based query optimizer designed for large-scale data workloads, Greenplum scales interactive and batch-mode analytics to large datasets in the petabytes without degrading query performance and throughput. Based on PostgreSQL, Greenplum provides you with more control over the software you deploy, reducing vendor lock-in, and allowing open influence on product direction. ... -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
...For example, You can add EfficientNet as the backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done! Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD. -
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tyrant_optimize
TUO
Tyrant Unleashed Optimizer (the SIM) -
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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. ...
