The RAY framework is an open-source project that provides a simple, universal API for building distributed applications. It is particularly well-suited for applications that require parallel and distributed computing, making it a popular choice for machine learning, deep learning, and big data processing tasks. Here are some reasons why and scenarios when the RAY framework is used:
Ray shines when you need to supercharge your Python applications (especially AI workloads) from single-machine development to large-scale clusters. It simplifies distributed computing, letting you write seamless code that scales from your laptop to hundreds of GPUs without rewriting. Use Ray for tasks like parallel processing, machine learning training and serving, hyperparameter tuning, and reinforcement learning - all in one unified framework.
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