The difference between ray[air] and ray[default] lies in the set of dependencies they install when you set up the Ray library. These options allow users to customize their installation to suit their specific needs, minimizing unnecessary dependencies.
1. ray[default]
What it includes:
This is the default installation, which contains the core Ray functionality and basic dependencies required to use Ray for distributed computation.
Use case:
Ideal for general-purpose distributed applications, task orchestration, and workflows.
Provides the base components needed for Ray core features like task scheduling, actor management, and Ray's Python API.
Dependencies:
Core Ray libraries only (e.g., task scheduling and execution).
Install Command:
pip install ray[default]
ray[air]
What it includes:
The ray[air] installation includes all dependencies required for Ray AI Runtime (AIR), which is Ray's unified framework for machine learning workflows.
Use case:
Best suited for machine learning and AI-related tasks, such as training, hyperparameter tuning, and serving models.
Adds support for Ray Train, Ray Tune, Ray Serve, and Ray Data, which are specialized components of Ray for ML/AI workflows.
Dependencies:
Includes additional libraries such as:
torch, tensorflow, and other ML frameworks for training models.
scikit-learn for machine learning utilities.
Libraries for handling large datasets and streaming data efficiently.
Ray Serve dependencies for model deployment and serving.
Install Command:
pip install ray[air]
When to Use Which?
Use Case Recommended Installation
Distributed task orchestration ray[default]
General distributed computing ray[default]
Machine learning workflows ray[air]
Model training and hyperparameter tuning ray[air]
Model serving and deployment ray[air]
Handling large-scale data processing ray[air]
Summary
ray[default]: Lightweight, general-purpose distributed computing.
ray[air]: Focused on ML/AI workflows with added dependencies for training, tuning, and serving models.
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