# Any Black-Box Packages for Bayesian Hyperparameter Optimization?

**URL:** <https://discourse.julialang.org/t/any-black-box-packages-for-bayesian-hyperparameter-optimization/20428>\
**Category:** Machine Learning\
**Created:** [February 4, 2019, 3:03pm UTC](https://discourse.julialang.org/t/any-black-box-packages-for-bayesian-hyperparameter-optimization/20428 "2019-02-04T15:03:13Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![ahmetumutdurmus](https://avatars.discourse-cdn.com/v4/letter/a/ed655f/32.png) [@ahmetumutdurmus](https://discourse.julialang.org/u/ahmetumutdurmus)\
**Post date:** [February 4, 2019, 3:03pm UTC](https://discourse.julialang.org/t/any-black-box-packages-for-bayesian-hyperparameter-optimization/20428/1 "2019-02-04T15:03:13Z")

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Configuring hyperparameters in a machine learning problem can be a dauntingly boring and suboptimal task as each evaluation may be fairly long to conduct and thus performing an exhaustive grid search may be infeasible. Bayesian optimization provides a seemingly nice and intuitive framework for this instead of manual search and there are some packages out there in other languages. For example [HyperOpt](https://github.com/hyperopt/hyperopt) is an open source package developed in Python.

My question is this: How do people perform bayesian optimization for hyperparameter search in Julia? Is there currently a package out there that I was not able to find or do they use PyCall for example and use HyperOpt of Python instead?

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**Author:** ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)\
**Post date:** [February 4, 2019, 3:15pm UTC](https://discourse.julialang.org/t/any-black-box-packages-for-bayesian-hyperparameter-optimization/20428/2 "2019-02-04T15:15:25Z")

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I found

> **[GitHub - jbrea/BayesianOptimization.jl: Bayesian optimization for Julia](https://github.com/jbrea/BayesianOptimization.jl)**
>
> Bayesian optimization for Julia. Contribute to jbrea/BayesianOptimization.jl development by creating an account on GitHub.

very nice (not for ML, but for a difficult optimization problem with a stochastic objective).

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**Author:** ![SimonW](https://avatars.discourse-cdn.com/v4/letter/s/34f0e0/32.png) [@SimonW](https://discourse.julialang.org/u/SimonW)\
**Post date:** [February 5, 2019, 9:42am UTC](https://discourse.julialang.org/t/any-black-box-packages-for-bayesian-hyperparameter-optimization/20428/3 "2019-02-05T09:42:55Z")

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Hi,

only very basic concepts of hyperparameter optimization are implemented in Julia so far (to my knowledge). There are ongoing discussions in [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl/issues) on what could be implemented.

If someone is interested in implementing some sort of BO hyperparameter optimization, then this person should have a look at [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](http://proceedings.mlr.press/v80/falkner18a.html) which seems to outperform standard BO. There exists a python package called [HpBandSter](https://github.com/automl/HpBandSter) already.