Optimize step

An optimize step generates optimized configurations according to the defined optimization strategy. During this step, Akamas AI is used to generate such optimized configurations.

The optimize step has the following structure:

Field	Type	Value restrictions	Is required	Default value	Description
type	string	optimize	yes		The type of the step, in this case, optimize
name	string		yes		The name of the step
runOnFailure	boolean	true false	no	false	The execution policy of the step: false prevents the step from running in case the previous step failed true allows the step to run even if the previous step failed
numberOfExperiments	integer	numberOfExperiments > 0 and numberOfExperiments >= numberOfInitExperiments	yes		The number of experiments to execute - see below
numberOfTrials	integer	numberOfTrials > 0	no	1	The number of trials to execute for each experiment
numberOfInitExperiments	integer	numberOfInitExperiments < numberOfExperiments	no	10	The number of initialization experiment to execute - see below.
maxFailedExperiments	integer	maxFailedExperiments > 1	no	30	The number of experiment failures (as either workflow errors or constraint violations) to accept before the step is marked as failed
optimizer	string	AKAMAS SOBOL RANDOM	no	AKAMAS	The type of optimizer to use to generate the configuration of the experiments - see below
doNotRenderParameters	string		no		Parameters not to be rendered. - see Parameter rending
renderParameters	string		no		Parameters to be rendered. - see Parameter rending

Optimizer

The optimizer field allows selecting the desired optimizer:

• AKAMAS identifies the standard AI optimizer used by Akamas

• SOBOL identifies an optimizer that generates configurations using Sobol sequences

• RANDOM identifies an optimization that generates configurations using random numbers

Notice that SOBOL and RANDOM optimizers do not perform initialization experiments, hence the field numberOfInitExperiments is ignored.

Refer to page Optimizer Options for more configuration options for the optimizer

Failures

The optimize step is fault-tolerant and tries to relaunch experiments on failure. Nevertheless, the step imposes a limit on the number of failed experiments: if too many experiments fail, then the entire step fails too. By default, at most 30 experiments can fail while Akamas is optimizing systems. An experiment is considered failed when it either failed to run (i.e., there is an error in the workflow) or violated some constraint.

Initializations

The optimize step launches some initialization experiments (by default 10) that do not apply the AI optimizer and are used to find good configurations. By default, the step performs 10 initialization experiments.

Initialization experiments take into account bootstrapped experiments, experiments executed in preset steps, and baseline experiments.

Example

The following snippet shows an optimization step that runs 50 experiments using the SOBOL optimizer:

name: "my_optimize" # name of the step
type: "optimize" # type of the step (optimize)
optimizer: "SOBOL"
numberOfExperiments: 50 # amount of experiments to execute
numberOfTrials: 2 # amount of trials for each experiment