Resource management commands

This page describes all commands that allow Akamas resources to be managed with their options (see also common options available for all commands).

General information

Common options

The following table describes the common options available for all commands:

Akamas aliases

The Akamas CLI allows using a set of aliases or shortcuts for many resources.

Any resource can be specified using either the singular or plural form. Furthermore, the shortcuts listed below are available:

   component            [comp, co, components, cmp]
   system               [systems, sys, sy]
   component-type       [ctype, comp-type, ct, component-types]
   optimization-pack    [optimization-packs, op, opt-pack, opack]
   metric               [metr, metrics, me]
   parameter            [parameters, pa, param, par]
   study                [st, studies, sty]
   workflow             [wf, wkfl, workflows, wo]
   telemetry-instance   [ti, tel-instance, telemetry-instances, tel-inst]
   telemetry-provider   [telemetry-providers, tel-prov, tp, tel-provider]
   kpi                  [kpis]
   workspace            [workspaces, ws]
   trial                [tr, trials]
   user                 [us, users]
   license              [licenses, lic]
   experiment           [experiments, exp]
   log                  [logs]
   step                 [steps]

You can print the list of available aliases with the following command

akamas list alias

Build command

This command builds either a new optimization pack or a new scaffolding hierarchy.

# build optimization pack
akamas build optimization-pack <folder-with-resource-files>

# build scaffolding
akamas build scaffold <folder-with-resource-files>

Build optimization pack

In this case, you supply a folder with a specific hierarchy: the needed folders are metrics, component-types and parameters, and each of them contains a set of yaml resource files listing the supported resources. Then the command akamas build optimization-pack FOLDER_NAME creates a full JSON file with all the optimization pack contents inside it.

Build scaffolding

In this case, you supply two folders: one for variables, named variables and one for templates named templates. The variables folder must contain one yaml file for each desired output set and the templates folder should hold all generic templates that can contain variable parameters specified in the variables file.

For example, the variables folder could contain two files named test.yaml and prod.yaml. The contents of these files are:

study:
  name: my study

k8s:
  namespace: online-boutique
  deployment: adservice
  container: server

k8s_deployment: test

study:
  name: Production study

k8s:
  namespace: online-boutique-prod
  deployment: adservice-prod
  container: server-prod

k8s_deployment: prod

Then suppose that the template folder contains some YAML files and one of them the is following file named template-study.yaml:

kind: study

name: {{ study.name }}
system: {{ k8s.deployment }}
workflow: {{ k8s.deployment }}

goal:
  name: Cost
  objective: minimize
  function:
    formula: (({{ k8s.container }}.container_cpu_limit)/1000)*29 + (((({{ k8s.container }}.container_memory_limit)/1024)/1024)/1024)*3

When launching the command akamas build scaffold SCAFFOLDING_DIR_NAME/, a new folder outputis created inside SCAFFOLDING_DIR_NAME along with two sub-folders test and prod. Each sub-folder now contains the templates rendered with the values set in the variables files. The akamas create entity command makes it easier to create bulk entities.

Create command

Create the Akamas resource described in the provided YAML file.

akamas create <resource-type|-f> <resource-file> [<parent-resource-id|parent-resource-name>]

Create by file/folder

You can also omit the resource type from the create command and use the -f flag instead to create most of the resources with a YAML file in a single command. The supported resources are component, system, optimization-pack, study, workflow, telemetry-instance and telemetry-provider. To use this feature, it's required to add the kind key inside each YAML file. Also, the system key must be added when the resource is required to be attached to a system (applies to telemetry instances and system components).

For example, to create a new telemetry instance, you should add the following to your YAML file:

kind: telemetry-instance
system: SYSTEM_NAME

Then you can use the akamas create -f <filename.yaml> command instead of akamas create telemetry-instance <filename.yaml> SYSTEM_NAME.

Similarly, to create a new telemetry-provider (which does not need the system attribute), you just need to specify the kind to your YAML file:

kind: telemetry-provider

This also works for optimization packs. For standard optimization packs provided by akamas, you need to write a YAML file such as:

kind: optimization-pack
name: OPTIMIZATION_PACK_NAME

If you want to install a custom optimization pack, you can also supply a JSON file. In this case, there is no need to specify the kind attribute.

Finally, if you supply a folder to the command akamas create, it will process all files inside this folder and create all the requested resources. You can, for example, use one of the output folders created by the command akamas build scaffold. Let's assume we have a folder named scaffold that contains the following files:

component.yaml
opt_pack.yaml
provider.yaml
study.yaml
system.yaml
telemetry_instance.yaml
workflow.yaml

the following command will process all of the files above and (if correct) create all resources described inside of them:

akamas create -f scaffold/

Delete command

Delete an Akamas resource, identified by UUID or name.

akamas delete [options] <resource-type|-f> <resource-id|resource-name> [<parent-resource-id|parent-resource-name>]

with the following options:

Delete entity command

Similarly to the create command, you can use the flag -f to delete the supplied resources. See the section Create by file/folder command for instructions on the supported resources and the additional required fields.

All resources created with the command akamas create -f <folder> can also be deleted by using the opposite command akamas delete -f <folder>. The only difference is that the command akamas delete -f has an additional flag --complete. When supplied, it deletes all supported objects including optimization packs and telemetry providers. When the --complete flag is missing, however, optimization packs and telemetry providers are not deleted.

List command

List the resources for the selected type with their id, name, and description. Additional resource-specific fields can be shown.

akamas list [flags] <resource-type> <resource-id|resource-name> [<parent-resource-id|parent-resource-name>]

with the following options:

Describe command

Describe an Akamas resource with all its fields.

akamas describe [flags] <resource-type> <resource-type> <resource-id|resource-name> [<parent-resource-id|parent-resource-name>]

with the following options:

Notice that this command does not support the resource type System.

Update command

Update an Akamas resource, identified by UUID or name.

akamas update <resource-type> <resource-id|resource-name>

with the following options:

Update experiment command

Update an experiment, identified by the ID of the study and the experiment.

akamas update experiment <study-id|study-name> <experiment-id>

with the following options:

Install command

Install a License or an Optimization Pack

akamas install <resource-type> <file>

with the following options:

Uninstall command

Uninstall a License or an Optimization Pack

akamas uninstall <resource-type> <id>

with the following options:

Start command

Start the execution of a Study.

akamas start study <id|name>

Stop command

Stop the execution of a Study. Once stopped, the execution cannot be resumed.

akamas stop study <id|name>

Export command

To export a study, the study name or the study UUID can be used from the command line.

An optional filename can be specified, with a relative or absolute path:

akamas export study <UUID>|"<NAME>" [FILENAME]

The exported information will be saved in tar.gz format.

The following entities are exported:

• The Study

• The Steps of the Study

• The Experiments of the Study

• The Trials of the Study

• The Workflow to which the Study refers

• The Timeseries collected during the study run

• The System to which the Study refers

• The Component related to the Study's System

• The ComponentType of each Component

• The Metrics definitions of each ComponentTypes

• The Parameters definitions of each ComponentTypes

Notice: this operation can require a long time, depending on the quantity of data to be collected. During this time the CLI will wait for Akamas to send the exported package. Do not interrupt the CLI during this phase, as otherwise, the process will need to restart from the beginning.

Import command

Notice: please make sure that you have installed the latest versions of the optimization packs before starting the import: this way, the import procedure will bind the studies to the latest optimization packs version (i.e., the installed ones) instead of importing the (possibly) old ones from the source system.

Use the following command to import a study into an existing Akamas instance:

akamas import study FILENAME

Where FILENAME refers to the file of a previously exported study.

When imported, the following entities will have a new UUID:

• Study

• Workflow

• System

• Component

• ComponentType

• Metrics

• Parameters

In case a resource that is being imported has the same name as an existing one, the existing entity will not be deleted. The existing entity (with its UUID) will be used instead of the imported one.

All steps, experiments, and trials will maintain the same id and, therefore, the same execution order as the original exported study.

Notice: this operation can require a long time. If the CLI shows a timeout error or if the operation is interrupted, the import will continue on the Akamas server.