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.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 16+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
Automated Machine Learning with scikit-learn
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.
Automated Machine Learning on Kubernetes
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
EvalML is an AutoML library written in python.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
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
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…
Tuning hyperparams fast with Hyperband
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.
Library for Semi-Automated Data Science
Library for automatic retraining and continual learning
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."