🙌 OpenHands: Code Less, Make More

Model Context Protocol Servers

The official Python SDK for Model Context Protocol servers and clients

🦜🔗 Build context-aware reasoning applications

Examples for https://github.com/optuna/optuna

A hyperparameter optimization framework

Atomistic simulation hands on tutorial on Matlantis

Real-time Web dashboard for Optuna.

Educational Operating System

Repository for hyperparameter tuning

J-Quants-Tutorial

HandyRL is a handy and simple framework for distributed reinforcement learning that is applicable to your own environments.

PFRL: a PyTorch-based deep reinforcement learning library

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

Supplementary components to accelerate research and development in PyTorch

Benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning libr…

Sandbox

Flexible Feature Engineering & Exploration Library using GPUs and Optuna.

NumPy-like API accelerated with CUDA

ChainerCV: a Library for Deep Learning in Computer Vision