Introduction to Ray Educational Materials
Ray Educational Materials provide a comprehensive collection of resources focused on Ray, a distributed computing framework designed to scale Python and machine learning workloads from a single laptop to a large cluster. This initiative, managed by Anyscale Inc., aims to facilitate learning and adoption of Ray through a structured set of educational modules.
Learning Path Overview
The learning path is organized into modules, each targeting specific aspects of Ray and related workflows. Here is a detailed breakdown of what learners can expect from these materials:
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Overview of Ray: This module introduces Ray and its ecosystem, providing learners with a solid foundation on the capabilities of the Ray framework.
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Introduction to Ray AI Runtime: Learners will gain insight into the Ray AI Runtime, a component crucial for scaling AI applications.
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Ray Core: Remote Functions as Tasks: This module teaches how to execute arbitrary functions asynchronously on different Python workers, a fundamental aspect of Ray's distributed functionality.
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Ray Core: Remote Objects: This section explores how objects can be stored anywhere within a Ray cluster, enhancing the flexibility of data handling.
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Ray Core: Remote Classes as Actors (Parts 1 & 2): The first part covers working with stateful actors, while the second part explains the "Tree of Actors" pattern, both essential for building scalable applications in Ray.
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Ray Core: Ray API Best Practices: A critical module for learning about patterns, anti-patterns, and best practices when using the Ray API effectively.
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Scaling Batch Inference in Computer Vision: This module provides guidance on scaling batch inference processes in computer vision tasks using Ray.
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Optional: Batch Inference with Ray Datasets: This is a supplementary module that delves into scaling batch inference using Ray Datasets for those seeking advanced knowledge.
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Scaling Model Training: Learners can explore how to scale model training for computer vision with Ray, optimizing performance in large-scale environments.
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Ray Observability Part 1: This module introduces tools like the Ray State API and Ray Dashboard UI to help users observe and manage Ray clusters and applications.
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LLM Model Fine-tuning and Batch Inference: Participants will learn to fine-tune a Hugging Face Transformer on the Alpaca dataset, including distributed hyperparameter tuning and batch inference processes.
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Multilingual Chat with Ray Serve: This practical exercise involves serving a Hugging Face LLM chat model with Ray Serve, integrating language detection and translation services.
Community Engagement
Ray Educational Materials encourage learners to engage with the broader Ray community through various channels:
- Access the official Ray documentation for detailed information.
- Visit the official Ray site to explore the ecosystem and resources.
- Join the Ray community on Slack for discussions.
- Participate in the discussion board to ask questions and view announcements.
- Join meetups to hear talks and connect with other users.
- Contribute to Ray by submitting issues or feature requests on GitHub.
- Learn how to become a Ray contributor to help improve the framework and its documentation.
In summary, Ray Educational Materials equip learners with the knowledge and skills to leverage Ray effectively, promoting active participation and contribution within the vibrant Ray community.