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 30, 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.
Automated Machine Learning with scikit-learn
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
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)
Library for Semi-Automated Data Science
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
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…
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Distribution transparent Machine Learning experiments on Apache Spark
Structure, sample, and savor hyperparameter searches.
Code examples for https://blog.floydhub.com/guide-to-hyperparameters-search-for-deep-learning-models/
Black-box optimization library
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