Ray is an open source library for parallel and distributed Python that can be paired with PyTorch to rapidly scale machine learning applications, offering simplicity, robustness, and performance advantages. It has a general-purpose framework that has built many libraries and frameworks on top of it to accomplish different tasks, with the vast majority supporting PyTorch, requiring minimal modifications to code, and integrating seamlessly with each other. RaySGD is a library that provides distributed training wrappers for data parallel training, offering ease of use, scalability, accelerated training, fault tolerance, and compatibility with other libraries like Ray Tune and Ray Serve. Ray Tune is a Python library for experiment execution and hyperparameter tuning at any scale, allowing launch of multi-node distributed hyperparameter sweeps in fewer than 10 lines of code, with first-class support for GPUs and automatic management of checkpoints and logging to TensorBoard. Ray Serve is a library for easy-to-use scalable model serving, offering compatibility with many other libraries like Ray Tune and FastAPI. RLlib is a library for reinforcement learning that offers both high scalability and a unified API for a variety of applications, including native support for PyTorch, TensorFlow Eager, and TensorFlow, as well as support for complex model types and multi-agent algorithms. The Ray Cluster Launcher allows users to launch and scale machines across any cluster or cloud provider with ease, automating tasks such as autoscaling, syncing files, submitting scripts, port forwarding, and more.