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https://github.com/ray-project/ray-educational-materials

This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
https://github.com/ray-project/ray-educational-materials

deep-learning distributed-machine-learning generative-ai llm llm-inference llm-serving ray ray-data ray-distributed ray-serve ray-train ray-tune

Last synced: about 1 month ago
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This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

Host: GitHub
URL: https://github.com/ray-project/ray-educational-materials
Owner: ray-project
License: apache-2.0
Archived: true
Created: 2022-09-16T05:26:19.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-02-13T00:29:49.000Z (5 months ago)
Last Synced: 2024-04-14T20:18:42.032Z (3 months ago)
Topics: deep-learning, distributed-machine-learning, generative-ai, llm, llm-inference, llm-serving, ray, ray-data, ray-distributed, ray-serve, ray-train, ray-tune
Language: Jupyter Notebook
Homepage:
Size: 24 MB
Stars: 277
Watchers: 11
Forks: 50
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS

Lists

awesome-ray - ray-educational-materials - on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray. (Models and Projects / Misc)

README

# Ray Educational Materials

[![Introductory notebooks test](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-introductory-modules.yml/badge.svg?branch=main)](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-introductory-modules.yml)
[![Ray core notebooks test](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-ray-core.yml/badge.svg?branch=main)](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-ray-core.yml)
[![Semantic segmentation notebooks test](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-semantic-segmentation.yml/badge.svg?branch=main)](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-semantic-segmentation.yml)
[![Observability notebooks test](https://github.com/ray-project/ray-educational-materials/actions/workflows/scheduled-test-observability-modules.yml/badge.svg)](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.