Ray is 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.
- Tune: Scalable Hyperparameter Tuning
- RLlib: Scalable Reinforcement Learning
- RaySGD: Distributed Training Wrappers
- Ray Serve: Scalable and Programmable Serving
Key-Features of Ray
- Scale anywhere
- uses scalable machine learning libraries
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Tpot is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant.
TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. TPOT is built on top of scikit-learn.
Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.
AutoKeras is an AutoML library for deep learning. The goal of AutoKeras is to make machine learning accessible for everyone.
AutoKeras supports several tasks with an extremely simple interface.
You can click the links below to see the detailed tutorial for each task.
Structured Data Classification
Structured Data Regression
FeatureTools is an open-source python library for automated feature engineering.
Featuretools automatically creates features from temporal and relational datasets.
Featuretools uses DFS(Deep Feature Synthesis) for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modelling.
Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row.
Featuretools comes with a library of low-level functions that can be stacked to create features. You can build and share your own custom primitives to be reused on any dataset.
Featuretools improves your existing workflow and is easily accessible through its Python API.
NNI is an open-source AutoML toolkit for automating the machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.
The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments’ trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.), DLWorkspace (aka. DLTS), AML (Azure Machine Learning), AdaptDL (aka. ADL), other cloud options and even Hybrid mode.
Who should consider using NNI
- Those who want to try different AutoML algorithms in their training code/model.
- Those who want to run AutoML trial jobs in different environments to speed up the search.
- Researchers and data scientists who want to easily implement and experiment with new AutoML algorithms, may it be hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
- ML Platform owners who want to support AutoML in their platform