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

AutoML library for deep learning

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Differentiable architecture search for convolutional and recurrent networks

Fast and flexible AutoML with learning guarantees.

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Automated Machine Learning on Kubernetes

Automated deep learning algorithms implemented in PyTorch.

A curated list of awesome architecture search resources

An autoML framework & toolkit for machine learning on graphs.

Fast & Simple Resource-Constrained Learning of Deep Network Structure

This is a list of interesting papers and projects about TinyML.

Genetic neural architecture search with Keras

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory

Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

a distributed Hyperband implementation on Steroids

NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers.

A unified interface for optimization algorithms and experiments

[ICLR 2020] "FasterSeg: Searching for Faster Real-time Semantic Segmentation" by Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang

Basic implementation of [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578).

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

Awesome Neural Architecture Search Papers

real-time network architecture for mobile devices and semantic segmentation