This reference guide assumes that you have already familiarized yourself with the concept of experiment_groups.


Hyperparameters selection is crucial for creating robust models, since they heavily influence the behavior of the learned model. Finding good hyperparameters involves can be very challenging, and requires to efficiently search the space of possible hyperparameters as well as how to manage a large set of experiments for hyperparameter tuning.

The way Polyaxon performs hyperparameters tuning is by providing to the data scientists a selection of search algorithms. Polyaxon supports both simple approaches such as random search and grid search, and provides a simple interface for advanced approaches, such as Hyperband and Bayesian Optimization.

All these search algorithms run in an asynchronous way, and support concurrency to leverage your cluster's resources to the maximum.

Some of these approaches are also iterative and improve based on previous experiments.


  • Easy-to-use: Polyaxon's Optimization Engine is a built-in service and can be used easily by adding a hptuning section to your polyaxonfiles, uou can start the group both using the CLI and the dashboard.
  • Scalability: Tuning hyperparameters or neural architecture requires leveraging a large amount of computation resources, using Polyaxon you can run hundreds of trials in parallel and track their progress in an intuitive way.
  • Flexibility: Besides rich built-in algorithms, Polyaxon allows users to customize various hyperparameter tuning algorithms, neural architecture search algorithms, early stopping algorithms, etc.
  • Efficiency: We are intensively working on more efficient model tuning from both system level and algorithm level. For example, leveraging early feedback to speedup tuning procedure.


  • Define a search space
  • Define a search algorithm
  • Define a model to optimize
  • Optionally define the concurrency and early stopping.


In order to search a hyperparameter space, all search algorithms require a hptuning section, they also share some subsections such as: matrix definition of hyperparameters, early_stopping, and concurrency. Each one of these algorithms has a dedicated subsection to define the required options.