Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
-
Updated
May 29, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Hyperparameter selection on machine learning models using Particle Swarm Optimization
An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Library for Semi-Automated Data Science
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.
Tree-of-Parzen-estimators hyperparameter optimization
Sequential model-based optimization with a `scipy.optimize` interface
Sequential model-based optimization with a `scipy.optimize` interface
Distribution transparent Machine Learning experiments on Apache Spark
A lightweight custom automl library.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
Automated Machine Learning with scikit-learn
Hyperparameter search wrapper that uses multiple GPUs.
Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.
Genetic algorithm framework for tuning arbitrary functions
Add a description, image, and links to the hyperparameter-search topic page so that developers can more easily learn about it.
To associate your repository with the hyperparameter-search topic, visit your repo's landing page and select "manage topics."