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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:
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name: "my_optimize" # name of the step
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type: "optimize" # type of the step (optimize)
3
optimizer: "SOBOL"
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numberOfExperiments: 50 # amount of experiments to execute
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numberOfTrials: 2 # amount of trials for each experiment

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

Preset step

Parameter rendering

Last modified 4mo ago