# Ray Tune Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/ray-tune
> Markdown URL: https://aitinkerers.org/technologies/ray-tune.md
> Technology record last updated: 2026-02-25T10:19:16Z
> Generated: 2026-09-22T07:50:00Z

Ray Tune is the scalable, distributed hyperparameter optimization library built on Ray, enabling seamless transition from a single machine to a large cluster.

Ray Tune efficiently manages hyperparameter search across any scale: from your laptop to a multi-node cluster. It integrates directly with major ML frameworks (PyTorch, TensorFlow, XGBoost) and offers advanced search algorithms like Population-Based Training (PBT) and ASHA (Asynchronous Successive Halving). You define a search space and a target metric (e.g., 'mean_accuracy'), and Ray's distributed actors manage the parallel execution of hundreds of trials. This process minimizes tuning time and maximizes model performance, all without requiring code changes for scaling.

- Official technology site: https://docs.ray.io/en/latest/tune/index.html
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Deep RL for User Experience](https://chicago.aitinkerers.org/talks/rsvp_btt528Sh4Tk)

We'll walk through how ezbot uses the Ray Python libraries to scale reinforcement learning prediction and training for our customers. We leverage the distributed hyperparemeter tuning framework as well as the massively-parallel processing capabilities of Ray to make RL relatively simple.

- Event context: AI Tinkerers Chicago December Meetup — 2024-12-10 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_btt528Sh4Tk

### [AutoRAG](https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s)

Automatic hyperparameter optimization of your retrieval augmented generation pipeline. Stop fiddling around manually with chunk size, top k and so on. Let the machine do the tuning of your retrieval settings. Inspired by hyperparameter optimization in conventional ML.

- Event context: AI Tinkerers Berlin - March 21 — 2024-03-21 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s

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