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Manage Computational Resources

Manage Computational Resources#

Once you have launched your computational resources, there are a few API methods that help manage them and see their status. Let’s go over them one by one.

Get your active computational resources#

In case computational resources have been launched in another Python session and want to manage or re-use them in another script, users can fetch the respective instance via the get method as follows:

## Obtain a list with instances of all active computational resources
>>> resources_list = inductiva.resources.machine_groups.get()
>>> print(resources_list)
[MPICluster(name="api-23zssj6oq77xxsot3o0nhax3d"),
 ElasticMachineGroup(name="api-45fetsr58okcs0x6j9m0vsi2z"),
 MachineGroup(name="api-4kken08fnoxuu5zjjak6ak2xe")]

List your active computational resources#

When you just want to check the active resources you can quickly list the information of each one either via Python or via the CLI.

Python

inductiva.resources.machine_groups.list()

CLI

$ inductiva resources list

One obtains for example the following information:

Active Resources:

       NAME                                MACHINE TYPE         ELASTIC         TYPE           # MACHINES         DATA SIZE IN GB         SPOT         STARTED AT (UTC)
       api-23zssj6oq77xxsot3o0nhax3d       c2d-highmem-16       False           mpi            3                  70                      False        01 Feb, 12:30:06
       api-45fetsr58okcs0x6j9m0vsi2z       c2-standard-4        True            standard       1/5                70                      False        01 Feb, 12:25:54
       api-4kken08fnoxuu5zjjak6ak2xe       c2-standard-8        False           standard       2                  60                      True         01 Feb, 12:26:37

Terminate the active computational resources#

When you have finished using your computational resources, don’t forget to terminate them. An advantage of Inductiva API is to control your computational resources and avoid them being idle.

Hence, you can either terminate your computational resources via Python or the CLI.

In Python, you will need to instantiate the computational resource object, for example, with the get method and then do machine.terminate() for example.

Via CLI, this process is much easier and you can either terminate a machine on at a time:

inductiva resources terminate api-45fetsr58okcs0x6j9m0vsi2z

Or terminate them all at once. You will need to confirm this action:

inductiva resources terminate --all

Both are a blocking call that will only finish when the machines have terminated, in this way no computational resources are left up.

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