Ray is an AI compute engine. 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.
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
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
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…
Library for Semi-Automated Data Science
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
syftr is an agent optimizer that helps you find the best agentic workflows for your budget.
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
🎯 📈 Sequential and Model-Based Optimization, in Python, featuring SCE-UA, SMBO, and SHGO algorithms. SOTA perf; 0 deps.
Использование MLflow для трекинга экспериментов PyTorch и Sklearn