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.get()
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) IDLE TIME MAX COST ($/HOUR)
api-3ejvh64mxuxnfcv3yxdhoyjuj c2-standard-4 False standard 5/5 50 False 10 Jul, 16:23:00 0:04:15/0:30:00 1.4909
api-5014txg0rwx3jbbpf6y0ndzmv c2d-highmem-16 False mpi 3/3 10 False 10 Jul, 16:22:04 0:05:12/0:30:00 3.27774
api-es9sjockjymvkwfmjioibfw8p c2-standard-8 False standard 2/2 60 False 10 Jul, 16:23:25 0:03:50/0:30:00 1.08312
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.