Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
Jun 13, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Sequential model-based optimization with a `scipy.optimize` interface
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Hyperparameter selection on machine learning models using Particle Swarm Optimization
An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀
Library for Semi-Automated Data Science
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.
Sequential model-based optimization with a `scipy.optimize` interface
Distribution transparent Machine Learning experiments on Apache Spark
A lightweight custom automl library.
Automated Machine Learning with scikit-learn
Hyperparameter search wrapper that uses multiple GPUs.
Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.
Genetic algorithm framework for tuning arbitrary functions
A specialized repository for fitting the Vikhlinin model to galaxy cluster density profiles.
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps enviro…
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