https://github.com/kamil-kaczmarek/ray-tune-micro-tutorial
Micro tutorial on how to run and scale HPO with LightGBM and Tune
https://github.com/kamil-kaczmarek/ray-tune-micro-tutorial
distributed-computing hyperparameter-optimization hyperparameter-tuning machine-learning ray-distributed ray-tune tutorial
Last synced: 10 months ago
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Micro tutorial on how to run and scale HPO with LightGBM and Tune
- Host: GitHub
- URL: https://github.com/kamil-kaczmarek/ray-tune-micro-tutorial
- Owner: kamil-kaczmarek
- License: mit
- Created: 2022-08-02T21:10:29.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-08T19:39:10.000Z (almost 4 years ago)
- Last Synced: 2025-06-25T01:50:18.844Z (11 months ago)
- Topics: distributed-computing, hyperparameter-optimization, hyperparameter-tuning, machine-learning, ray-distributed, ray-tune, tutorial
- Language: Jupyter Notebook
- Homepage: https://docs.ray.io/en/latest/tune/index.html
- Size: 26.4 KB
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Micro tutorial on how to run and scale hyperparameter optimization with LightGBM and Tune
### It is for you if:
* you model structured data using LightGBM (classification or regression tasks).
* want to run or scale hyperparameter optimization in your project.
* Looking around for quick ways to try hyperparameter optimization in your project.
### What will you do?
* Run hyperparameter tuning with LightGBM on the structured data.
* Configure _scheduler_ for more efficient tuning.
### What will you learn?
* Few bits about Ray and Tune fundamentals.
* How to use Tune to run hyperparameter optimization - quick start.
* Few more bits about _scheduler_ - to better define how tuning should progress.
# Where to start?
* First, make sure that you have an environment ready. Please follow the instructions on [environment setup](environment_setup.md) page (5 minutes read).
* Once your env is ready, go ahead and start [ray_tune_micro_tutorial.ipynb](ray_tune_micro_tutorial.ipynb).
# What to do next?
* Check the [user guides](https://docs.ray.io/en/latest/tune/tutorials/overview.html) for more in-depth introduction to Tune.
* Learn about [Distributed LightGBM on Ray](https://docs.ray.io/en/latest/ray-more-libs/lightgbm-ray.html) that enables multi-node and multi-GPU training.
* Have a closer look at [Tune docs](https://docs.ray.io/en/latest/tune/index.html) to learn more about other [search algorithms](https://docs.ray.io/en/latest/tune/api_docs/suggestion.html) and [schedulers](https://docs.ray.io/en/latest/tune/api_docs/schedulers.html).
* Go to the Ray tutorials page to
# How to connect with community, learn more, join other trainings?
* Feel free to reach out on [Ray-distributed Slack](https://ray-distributed.slack.com/archives/C011ML23W5B). Join `#tutorials` channel, say hello and ask questions.