Ray

Ray is an open-source Python framework developed for scaling AI and Python applications from a laptop to a cluster. It enables distributed computing with minimal code changes, allowing developers to build and run parallel, distributed, and production-grade workloads easily.

Features of Ray:

• Provides a simple Python API for parallel and distributed computing.
• Supports scaling of machine learning and AI workloads (e.g., training, tuning, serving).
• Integrates with popular frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Includes libraries for ML tasks like Ray Tune (hyperparameter tuning), Ray Serve (model serving), and Ray RLlib (reinforcement learning).
• Works efficiently on both local machines and large clusters, supporting CPU and GPU environments.

To acces the DashBoard of Ray run the following Commands in your terminal :

$ sudo su
$cd /opt
$source rayenv/bin/activate
$ray --version
$ ray start --head --dashboard-host=0.0.0.0
To hit the browser: http://server-ip:8265

Disclaimer: Ray is developed and maintained by the Ray community and Anyscale. It provides a robust platform for building scalable AI and Python applications, but performance depends on the underlying system configuration and workload design. Always refer to the official Ray documentation for the most accurate and up-to-date information.