ray-distributed-computing-anhvth-speedy-utils-c6d6bb10

npx skills add majiayu000/claude-skill-registry --skill ray-distributed-computing-anhvth-speedy-utils-c6d6bb10 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Ray - Distributed Computing for AI and Python Applications ## Overview Ray is an open-source unified framework for scaling AI and Python applications. It provides the compute layer for parallel processing so that you don't need to be a distributed systems expert. Ray minimizes the complexity of running distributed individual workflows and end-to-end machine learning workflows. ### What Ray Provides - **Scalable libraries** for common machine learning tasks (data preprocessing, distributed training, hyperparameter tuning, reinforcement learning, and model serving) - **Pythonic distributed computing primitives** for parallelizing and scaling Python applications - **Integrations and utilities** for deploying on Kubernetes, AWS, GCP, and Azure ## Installation ### Basic Installation ```bash pip install -U ray ``` ### With Specific Libraries ```bash # Ray Data for data processing pip install -U "ray[data]" # Ray Train for distributed training pip install -U "ray[train]" # Ray Tune for hyperparameter tuning pip install -U "ray[tune]" # Ray Serve for model serving pip install -U "ray[serve]" # RLlib for reinforcement learning pip install -U "ray[rllib]" torch # All libraries pip install

pip install -U ray
Ray Data for data processing
pip install -U "ray[data]"
Ray Train for distributed training
pip install -U "ray[train]"
Ray Tune for hyperparameter tuning
pip install -U "ray[tune]"
Ray Serve for model serving
pip install -U "ray[serve]"
RLlib for reinforcement learning