# ray-educational-materials
**Repository Path**: mirrors_ray-project/ray-educational-materials
## Basic Information
- **Project Name**: ray-educational-materials
- **Description**: This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-29
- **Last Updated**: 2026-05-31
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Ray Educational Materials
© 2022, Anyscale Inc. All Rights Reserved
[](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-introductory-modules.yml)
[](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-ray-core.yml)
[](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-semantic-segmentation.yml)
[](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-observability-modules.yml)
Welcome to a collection of education materials focused on [Ray](https://www.ray.io/), a distributed compute framework for scaling your Python and machine learning workloads from a laptop to a cluster.
## Recommended Learning Path
| Module | Description |
|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Overview of Ray](https://github.com/ray-project/ray-educational-materials/blob/main/Introductory_modules/Overview_of_Ray.ipynb) | An Overview of Ray and entire Ray ecosystem. |
| [Introduction to Ray AI Runtime](https://github.com/ray-project/ray-educational-materials/blob/main/Introductory_modules/Introduction_to_Ray_AI_Runtime.ipynb) | An Overview of the Ray AI Runtime. |
| [Ray Core: Remote Functions as Tasks](https://github.com/ray-project/ray-educational-materials/blob/main/Ray_Core/Ray_Core_1_Remote_Functions.ipynb) | Learn how arbitrary functions to be executed asynchronously on separate Python workers. |
| [Ray Core: Remote Objects](https://github.com/ray-project/ray-educational-materials/blob/main/Ray_Core/Ray_Core_2_Remote_Objects.ipynb) | Learn about objects that can be stored anywhere in a Ray cluster. |
| [Ray Core: Remote Classes as Actors, part 1](https://github.com/ray-project/ray-educational-materials/blob/main/Ray_Core/Ray_Core_3_Remote_Classes_part_1.ipynb) | Work with stateful actors. |
| [Ray Core: Remote Classes as Actors, part 2](https://github.com/ray-project/ray-educational-materials/blob/main/Ray_Core/Ray_Core_4_Remote_Classes_part_2.ipynb) | Learn "Tree of Actors" pattern. |
| [Ray Core: Ray API best practices](https://github.com/ray-project/ray-educational-materials/blob/main/Ray_Core/Ray_Core_5_Best_Practices.ipynb) | Learn Ray patterns & anti-patterns and best practices. |
| [Scaling batch inference](https://github.com/ray-project/ray-educational-materials/blob/main/Computer_vision_workloads/Semantic_segmentation/Scaling_batch_inference.ipynb) | Learn about scaling batch inference in computer vision with Ray. |
| [Optional: Batch inference with Ray Datasets](https://github.com/ray-project/ray-educational-materials/blob/main/Computer_vision_workloads/Semantic_segmentation/Batch_inference_with_Ray_Datasets.ipynb) | Bonus content for scaling batch inference using Ray Datasets. |
| [Scaling model training](https://github.com/ray-project/ray-educational-materials/blob/main/Computer_vision_workloads/Semantic_segmentation/Scaling_model_training.ipynb) | Learn about scaling model training in computer vision with Ray. |
| [Ray observability part 1](https://github.com/ray-project/ray-educational-materials/blob/main/Observability/Ray_observability_part_1.ipynb) | Introducing the Ray State API and Ray Dashboard UI as tools for observing the Ray cluster and applications. |
| [LLM model fine-tuning and batch inference](https://github.com/ray-project/ray-educational-materials/blob/main/NLP_workloads/Text_generation/LLM_finetuning_and_batch_inference.ipynb) | Fine-tuning a Hugging Face Transformer (FLAN-T5) on the Alpaca dataset. Also includes distributed hyperparameter tuning and batch inference. |
| [Multilingual chat with Ray Serve](https://github.com/ray-project/ray-educational-materials/blob/main/Ray_Serve/Multilingual_Chat_with_Ray_Serve_GPU.ipynb) | Serving a Hugging Face LLM chat model with Ray Serve. Integrating multiple models and services within Ray Serve (language detection and translation) to implement multilingual chat. |
## Connect with the Ray community
You can learn and get more involved with the Ray community of developers and researchers:
* [**Ray documentation**](https://docs.ray.io/en/latest)
* [**Official Ray site**](https://www.ray.io/)
Browse the ecosystem and use this site as a hub to get the information that you need to get going and building with Ray.
* [**Join the community on Slack**](https://forms.gle/9TSdDYUgxYs8SA9e8)
Find friends to discuss your new learnings in our Slack space.
* [**Use the discussion board**](https://discuss.ray.io/)
Ask questions, follow topics, and view announcements on this community forum.
* [**Join a meetup group**](https://www.meetup.com/Bay-Area-Ray-Meetup/)
Tune in on meet-ups to listen to compelling talks, get to know other users, and meet the team behind Ray.
* [**Open an issue**](https://github.com/ray-project/ray/issues/new/choose)
Ray is constantly evolving to improve developer experience. Submit feature requests, bug-reports, and get help via GitHub issues.
* [**Become a Ray contributor**](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html)
We welcome community contributions to improve our documentation and Ray framework.