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
Aug 28, 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.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Automated Machine Learning with scikit-learn
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Sequential model-based optimization with a `scipy.optimize` interface
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
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…
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Library for Semi-Automated Data Science
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.
Distribution transparent Machine Learning experiments on Apache Spark
Black-box optimization library
Hyperparameter search for AllenNLP - powered by Ray TUNE
An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀
AutoML - Hyper parameters search for scikit-learn pipelines using Microsoft NNI
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