Neural Architecture Search Python Packages

nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

hpbandster

a distributed Hyperband implementation on Steroids

kubeflow-katib-api

Automated Machine Learning on Kubernetes

autokeras

AutoML library for deep learning

deephyper

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning

kubeflow-katib

Automated Machine Learning on Kubernetes

hyperactive

A unified interface for optimization algorithms and experiments

syne-tune

Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

e2eaiok

Intel® End-to-End AI Optimization Kit

hypernets

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

neural-pipeline-search

Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.

nni-daily

Neural Network Intelligence package

e2eaiok-denas

Intel® End-to-End AI Optimization Kit

rfa-toolbox

A toolbox for receptive field analysis and visualizing neural network architectures

e2eaiok-sda

Intel® End-to-End AI Optimization Kit

myylearn

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

adanet

Fast and flexible AutoML with learning guarantees.

evoxbench

Transforming Neural Architecture Search (NAS) into multi-objective optimization problems. A benchmark suite for testing evolutionary algorithms in deep learning.

e2eaiok-modeladapter

Intel® End-to-End AI Optimization Kit

neural-dsl

A domain-specific language and debugger for neural networks

pyrecdp

Intel® End-to-End AI Optimization Kit

betty-ml

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

xautodl

Automated deep learning algorithms implemented in PyTorch.

neuropt

LLM-guided ML optimization — point it at a training script, it reads the curves and designs better models