Ray is an open-source distributed computing framework that simplifies scaling Python applications across CPUs and GPUs. It enables developers and teams to build, train, and deploy machine learning models, run large-scale data processing, and serve applications seamlessly on a unified platform.

Features of Ray:

• General-purpose distributed computing with simple Python APIs.
• Scalable libraries for ML, hyperparameter tuning, reinforcement learning, and model serving.
• Ray Data for distributed data processing and Ray Tune for experiment optimization.
• Ray Serve to deploy ML models and services at production scale.
• Works on laptops, clusters, or cloud environments with minimal setup.

To check Ray version: python -c "import ray; print(ray.__version__)"

Disclaimer: Ray is an independent open-source project maintained by Anyscale and the community. It is not affiliated with, endorsed by, or sponsored by Apache Spark, Dask, or other distributed frameworks. It is provided "as is," without any warranty, express or implied. Users are responsible for understanding and complying with licensing terms, data policies, and regulations while deploying Ray.