ray-tune

What problem does it solve?

This Skill provides a solution for distributed hyperparameter tuning, addressing the inefficiency of manual hyperparameter search and the need for parallelization in hyperparameter tuning processes.

Core Features & Use Cases

• Distributed Hyperparameter Tuning: Utilizes Ray's distributed computing capabilities to perform hyperparameter tuning across multiple nodes or GPUs.
• Early Stopping and Search Algorithms: Offers features like early stopping and integration with search algorithms like Optuna and HyperOpt.
• Integration with Ray Train: Facilitates seamless integration with Ray Train for training machine learning models.
• Use Case: When you need to fine-tune a machine learning model with many hyperparameters and require efficient and distributed training.

Quick Start

Use the ray-tune skill to tune the learning rate of a machine learning model with the following command: ray-tune tune --trainable my_trainable --param-space '{"lr": tune.loguniform(1e-5, 1e-1)}' --num-samples 50