What is Ray Framework?

Ray is an open-source framework designed to enable the development of scalable and distributed applications in Python. It provides a simple and flexible programming model for building distributed systems, making it easier to leverage the power of parallel and distributed computing. Some key features and capabilities of the Ray framework include:

Ray allows you to easily parallelize your Python code by executing tasks concurrently across multiple CPU cores or even across a cluster of machines. This enables faster execution and improved performance for computationally intensive tasks.

Ray provides a distributed execution model, allowing you to scale your applications beyond a single machine. It offers tools for distributed scheduling, fault tolerance, and resource management, making it easier to handle large-scale computations

With Ray, you can define Python functions that can be executed remotely. This enables you to offload computation to different nodes in a cluster, distributing the workload and improving overall efficiency.

Ray provides high-level abstractions for distributed data processing, such as distributed data frames and distributed object stores. These features make it easier to work with large datasets and perform operations like filtering, aggregation, and transformation in a distributed manner.

Ray includes built-in support for reinforcement learning algorithms and distributed training. It provides a scalable execution environment for training and evaluating machine learning models, enabling efficient experimentation and faster training times.

1. Ray AI Runtime (AIR)

This open-source collection of Python libraries is designed specifically for ML engineers, data scientists, and researchers. It equips them with a unified and scalable toolkit for developing ML applications. The Ray AI Runtime consists of 5 core libraries:

Ray Data

Achieve scalability and flexibility in data loading and transformation across various stages, such as training, tuning, and prediction, regardless of the underlying framework.

Ray Train

Enables distributed model training across multiple nodes and cores, incorporating fault tolerance mechanisms that seamlessly integrate with widely used training libraries.

Ray Tune

Scale your hyperparameter tuning process to enhance model performance, ensuring optimal configurations are discovered.

Ray Serve

Effortlessly deploy models for online inference with Ray's scalable and programmable serving capabilities. Optionally, leverage micro batching to further enhance performance.

Ray RLlib

Seamlessly integrate scalable distributed reinforcement learning workloads with other Ray AIR libraries, enabling efficient execution of reinforcement learning tasks.

references:

https://www.datacamp.com/tutorial/distributed-processing-using-ray-framework-in-python