Main Purpose
Ray is a distributed computing framework designed to make it easy to scale and parallelize Python applications.
Key Features
- Distributed Computing: Ray allows you to parallelize and distribute your Python applications across multiple machines or a cluster of machines.
- Task Parallelism: Ray provides a simple programming model for executing tasks in parallel, allowing you to speed up your computations.
- Actor Model: Ray supports the actor model, which enables you to create and manage stateful objects that can be accessed concurrently by multiple tasks.
- Fault Tolerance: Ray provides fault tolerance mechanisms to handle failures and ensure the reliability of your distributed applications.
- Ray Dashboard: Ray includes a web-based dashboard that allows you to monitor and debug your Ray applications.
Use Case
- Scaling Machine Learning Workloads: Ray can be used to scale machine learning workloads across multiple machines, enabling faster training and inference.
- Distributed Data Processing: Ray can be used to process large datasets in parallel, improving the performance of data-intensive applications.
- High-Performance Computing: Ray can be used to parallelize scientific simulations and other computationally intensive tasks, reducing the time required for execution.
