自动机器学习综述 - 知乎
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ClimbsRocks/auto_ml |
automl/Auto-PyTorch
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL).
Auto-PyTorch is mainly developed to support tabular data (classification, regression), but can also be applied to image data (classification). The newest features in Auto-PyTorch for tabular data are described in the paper "Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL" (see below for bibtex ref). |
D-X-Y/AutoDL-Projects
Automated Deep Learning Projects (AutoDL-Projects) is an open source, lightweight, but useful project for everyone. This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms. 中文介绍见README_CN.md |
EpistasisLab/tpot
TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. |
Keras Tuner
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.
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NNI
NNI (Neural Network Intelligence) 是一个轻量但强大的工具包，帮助用户自动的进行特征工程，神经网络架构搜索，超参调优以及模型压缩。
NNI 管理自动机器学习 (AutoML) 的 Experiment，调度运行由调优算法生成的 Trial 任务来找到最好的神经网络架构和/或超参，支持各种训练环境，如本机，远程服务器，OpenPAI，Kubeflow，基于 K8S 的 FrameworkController（如，AKS 等）， DLWorkspace (又称 DLTS), AML (Azure Machine Learning), AdaptDL（又称 ADL） ，和其他的云平台甚至 混合模式 。
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D-X-Y/Awesome-AutoDL
A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search. |
AutoGluon
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning text, image, and tabular data. |
Determined
Determined is an open-source deep learning training platform that makes building models fast and easy. Determined enables you to:
Train models faster using state-of-the-art distributed training, without changing your model code
Automatically find high-quality models with advanced hyperparameter tuning from the creators of Hyperband
Get more from your GPUs with smart scheduling and cut cloud GPU costs by seamlessly using preemptible instances
Track and reproduce your work with experiment tracking that works out-of-the-box, covering code versions, metrics, checkpoints, and hyperparameters
Determined integrates these features into an easy-to-use, high-performance deep learning environment — which means you can spend your time building models instead of managing infrastructure.
To use Determined, you can continue using popular DL frameworks such as TensorFlow and PyTorch; you just need to update your model code to integrate with the Determined API. |
walkerning/aw_nas
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner. aw_nas is a NAS framework with various NAS algorithms implemented in a modularized manner. Currently, aw_nas can be used to reproduce the results of many mainstream NAS algorithms, e.g., ENAS, DARTS, SNAS, FBNet, OFA, predictor-based NAS, etc. And we have applied NAS algorithms for various applications & scenarios with aw_nas, including NAS for classification, detection, text modeling, hardware fault tolerance, adversarial robustness, hardware inference efficiency, and so on.
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1. MLBox
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2. Auto Sklearn
自动机器学习之auto-sklearn入门
auto-sklearn 0.12.6
automl/auto-sklearn
auto-sklearn详解 |
3. TPOT
TPOT(自动机器学习方法)学习笔记 - 知乎
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