An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
a delightful machine learning tool that allows you to train, test, and use models without writing code
Gradient based Hyperparameter Tuning library in PyTorch
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing
A fast and lightweight AutoML library.
A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.
Library for Semi-Automated Data Science
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.
Distributed DAG (Directed acyclic graph) framework for machine learning with UI
Naszilla is a Python library for neural architecture search (NAS)
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
A specially designed light version of Fast AutoAugment
An automatic ML model optimization tool.
A simple, extensible library for developing AutoML systems
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Time Series Forecasting Best Practices & Examples
EvalML is an AutoML library written in python.
mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search
Provide an input CSV and a target field to predict, generate a model + code to run it.
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas
State-of-the art Automated Machine Learning python library for Tabular Data
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.
[UNMAINTAINED] Automated machine learning for analytics & production
AutoML frAmework for Neural Networks
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Lightwood is Legos for Machine Learning.
Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search
Open Toolkit for Painless Object Detection
TensorFlow implementation of PNASNet-5 on ImageNet
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Open-source implementation of Google Vizier for hyper parameters tuning
AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
Automated Data Science and Machine Learning library to optimize workflow.
[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
MLBox is a powerful Automated Machine Learning python library.
Once For All
[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
The implementation of "Shape Adaptor: A Learnable Resizing Module" [ECCV 2020].
[CVPR 2020] MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Primitives for machine learning and data science.