#大语言模型#Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
#计算机科学#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
#计算机科学#Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
#计算机科学#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
#自然语言处理#OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
#计算机科学#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 spa...
#计算机科学#Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
#计算机科学#Library for Semi-Automated Data Science
syftr is an agent optimizer that helps you find the best agentic workflows for your budget.
#计算机科学#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
#计算机科学#Black-box optimization library
Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems
#计算机科学#An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀