> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hyperparameter-tuning.md).

# Hyperparameter Tuning

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Hyperparameter Tuning - Infrastructure and Tooling
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## Summary

* Deep learning models are literally full of hyper-parameters. Finding the best configuration for these variables in a high-dimensional space is not trivial.
* Searching for hyper-parameters is an iterative process constrained by computing power, money, and time. Therefore, it would be really useful to have software that helps you search over hyper-parameter settings.
* **Hyperopt** is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.
* **SigOpt** is an optimization-as-a-service API that allows users to seamlessly tune the configuration parameters in AI and ML models.
* [**Ray Tune**](https://docs.ray.io/en/latest/tune.html) is a Python library for hyperparameter tuning at any scale, integrating seamlessly with optimization libraries such as **Hyperopt** and **SigOpt**.
* **Weights & Biases** has a nice feature called “Hyperparameter Sweeps” — a way to efficiently select the right model for a given dataset using the tool.