Search Results for "tuning"
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Showing 35 open source projects for "tuning"
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BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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JavaCore
JavaCore is a summary of experience with Java core technologies
...Its concurrency coverage includes threads, locks, atomics, thread pools, concurrent containers, synchronization, and the Java Memory Model. JVM chapters address memory, garbage collection, bytecode, class loading, diagnostics, monitoring, and performance tuning. -
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OpenAI Cookbook
Examples and guides for using the OpenAI API
openai-cookbook is a repository containing example code, tutorials, and guidance for how to build real applications on top of the OpenAI API. It covers a wide range of use cases: prompt engineering, embeddings and semantic search, fine-tuning, agent architectures, function calling, working with images, chat workflows, and more. The content is primarily in Python (notebooks, scripts), but the conceptual guidance is applicable across languages. The repository is kept up to date and often expanded, and its examples are intended to serve both beginners and intermediate users of the API. ... -
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LLM Datasets
Curated list of datasets and tools for post-training
...The repository aims to make datasets easy to inspect and transform, with scripts for downloading, deduping, cleaning, and converting to formats like JSONL that slot into training pipelines. It highlights instruction-tuning and conversation-style corpora while also pointing to code, math, or domain-specific sets for targeted capabilities. Quality is a recurring theme: examples and utilities help filter low-value samples, enforce length limits, and split train/validation consistently so results are comparable. Licensing and provenance are surfaced to encourage compliant usage and to guide dataset selection in commercial settings. ... -
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5
Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation, letting practitioners swap components without rewriting the whole stack. ... -
6
ants
ants is a high-performance and low-cost goroutine pool in Go
...Managing and recycling a massive number of goroutines automatically. Purging overdue goroutines periodically. Abundant APIs: submitting tasks, getting the number of running goroutines, tuning capacity of pool dynamically, releasing pool, rebooting pool. Handle panic gracefully to prevent programs from crash. Efficient in memory usage and it even achieves higher performance than unlimited goroutines in Golang. Nonblocking mechanism. Just take a imagination that your program starts a massive number of goroutines, resulting in a huge consumption of memory. ... -
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WebP Codec
Library to encode and decode images in WebP format
libwebp is the reference codec library for Google’s WebP image format, providing both encoding and decoding along with command-line tools. It supplies cwebp to compress images into WebP and dwebp to decompress them back, making it easy to test quality/size trade-offs across presets and tuning parameters. The GitHub repository is a mirror; the canonical source of truth lives on Chromium’s git, and developer docs are hosted on WebP’s portal. The project underpins WebP support across browsers, imaging libraries, and many native apps thanks to its stable C API. Additional companion repos host test data and demos, including JavaScript builds and timing tests for various platforms. ... -
8
Windows 11 Guide
Windows Security tools, Encryption, Nextcloud, Graphics, Gaming
Windows 11 Guide is a comprehensive, single-repository handbook for setting up and tuning a Windows 11 (and often Windows 10) desktop for real day-to-day use, covering everything from basic configuration to advanced development and gaming scenarios. It walks users through installing essential applications, configuring Windows security tools, setting up encryption and backups, and integrating productivity services like Nextcloud. -
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Index Advisor
PostgreSQL Index Advisor
index_advisor is a PostgreSQL extension developed by Supabase that suggests index optimizations based on query performance. It passively monitors query patterns and recommends indexes to improve performance without requiring deep database expertise. This tool helps developers and DBAs reduce query latency and improve efficiency in production environments. -
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10
glMatrix
Javascript Matrix and Vector library for High Performance WebGL apps
...These types of applications demand high performance vector and matrix math, which is something that Javascript doesn't provide by default. glMatrix to the rescue! glMatrix is designed to perform vector and matrix operations stupidly fast! By hand-tuning each function for maximum performance and encouraging efficient usage patterns through API conventions, glMatrix will help you get the most out of your browsers Javascript engine. -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers,... -
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HumanSystemOptimization
Healthy Learning to 150 - An Incomplete Guide to Human System Tuning
HumanSystemOptimization is a long-form personal guide about health, learning, productivity, and daily habits rather than a software application. It summarizes ideas the author gathered from neuroscience podcasts, research discussions, and personal experimentation. Major sections discuss sleep, circadian rhythms, diet, exercise, focus, mood, learning, and other aspects of everyday performance. The repository mixes background explanations with practical routines and links to outside material... -
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FasterLIO
Faster-LIO: Lightweight Tightly Coupled Lidar-inertial Odometry
...Users can compile it with CMake or catkin, run it through Docker, visualize output in RViz, and save trajectories and point clouds. Configuration files support multiple sensor datasets, while selectable voxel implementations allow performance and stability tuning. -
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Recursive Language Models
General plug-and-play inference library for Recursive Language Models
...It provides a consistent API that abstracts away many of the repetitive engineering patterns in RL research and application work, letting developers focus on modeling, experimentation, and fine-tuning rather than infrastructure plumbing. Within the framework, you can define custom agents, environments, policy networks, and reward structures while leveraging built-in dataset utilities, logging, and checkpointing for reproducible experiments. RLM also includes integration with popular simulation environments and benchmark suites, giving researchers a ready-made playground for algorithm comparison and performance tracking. -
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doc.rustdesk.com
Document of RustDesk
...The project treats documentation as code, enabling version control, community contributions, and swift iteration alongside product releases. It also provides best practices around performance tuning, authentication choices, and network topology so organizations can run RustDesk confidently at scale. -
16
Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. Nevergrad supports parallelization, budget scheduling, and multiple cost/resource constraints, allowing it to scale to nontrivial optimization problems. ... -
17
Faiss
Library for efficient similarity search and clustering dense vectors
...It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. ... -
18
LangExtract
A Python library for extracting structured information
LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and... -
19
torchkeras
Python library that simplifies PyTorch model training
...It includes progress visualization, training logs, early stopping, and integration with monitoring tools such as TensorBoard and Weights & Biases. Its modular implementation makes training behavior easier to understand and customize. The project also provides examples of distributed training, hyperparameter tuning, and integration with other machine learning frameworks. -
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AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides... -
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Neural Network Intelligence
AutoML toolkit for automate machine learning lifecycle
Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.) ... -
22
iJEPA
Official codebase for I-JEPA
...This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. The design scales naturally with Vision Transformer backbones and flexible masking strategies, and it trains stably at large batch sizes. i-JEPA’s predictions are made in embedding space, which is computationally efficient and better aligned with downstream discrimination tasks. The repository provides training recipes, data pipelines, and evaluation code that clarify which masking patterns and architectural choices matter most. -
23
Learning Prompt
Free prompt engineering online course
...It collects examples of effective prompt formats, chains of thought, strategy scaffolds, and comparative methods that help yield consistent, high-quality results. The project also includes meta-guidance: reflections on prompt failures, tips for prompt tuning, ways to mitigate model bias or misinterpretation, and how to adapt prompts for different AI capabilities. Because prompt design is a skill under continuous iteration, the repo encourages contributions of new prompt recipes, evaluations, and domain-specific adaptations. It’s intended for educators, developers, and prompt engineers who want a living toolkit rather than static advice. ... -
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albert_zh
Implementation of A Lite Bert For Self-Supervised Learning Language
...The project includes several model variants, such as tiny, small, base, large, and xlarge-style releases, giving users options for speed, size, and accuracy tradeoffs. It also provides guidance for fine-tuning downstream tasks such as sentence-pair semantic similarity and Chinese classification benchmarks. The repository includes support paths for TensorFlow, PyTorch conversion, Keras loading, TensorFlow 2.0 loading, and TensorFlow Lite deployment for mobile scenarios. Overall, it is useful for Chinese NLP developers who need compact pretrained language models for classification, similarity, and other language understanding tasks. -
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Angular Performance Checklist
Cheatsheet for developing lightning fast progressive Angular apps
Angular Performance Checklist is a practical resource and “cheatsheet” aimed at helping Angular (or AngularJS/Angular-related) developers optimize web application performance, covering both network/load-time optimizations and runtime performance improvements. It outlines actionable recommendations — from bundling and minification, tree-shaking, lazy loading, ahead-of-time (AoT) compilation, resource prefetching, caching and compression, to runtime strategies like change-detection...
