An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
-
Updated
Jul 3, 2024 - Python
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Automated Machine Learning with scikit-learn
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Sequential model-based optimization with a `scipy.optimize` interface
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
Automated Machine Learning on Kubernetes
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
EvalML is an AutoML library written in python.
Library for Semi-Automated Data Science
Generalized and Efficient Blackbox Optimization System.
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Tuning hyperparams fast with Hyperband
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps enviro…
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Add a description, image, and links to the hyperparameter-tuning topic page so that developers can more easily learn about it.
To associate your repository with the hyperparameter-tuning topic, visit your repo's landing page and select "manage topics."