Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
Jan 11, 2025 - Python
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A Python implementation of global optimization with gaussian processes.
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
Sequential model-based optimization with a `scipy.optimize` interface
🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
An object-oriented algebraic modeling language in Python for structured optimization problems.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Optimizing inference proxy for LLMs
Optax is a gradient processing and optimization library for JAX.
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
MLBox is a powerful Automated Machine Learning python library.
A library for differentiable robotics.
A research toolkit for particle swarm optimization in Python
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
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