MachineGroup Class

MachineGroup Class#

A Machine group is a pool of homogeneous machines that work individually and which do not communicate with each other in any way. Launching a machine group allows the creation of a private queue that only receives the tasks you specifically send to them. Then, the machines can pick simulations from the queue, which allows to run multiple simulations in parallel and speeds up the exploration of a design space.

To instantiate a MachineGroup object the following parameters can be configured:

  • the machine_type defines the type of CPU used for each machine. This parameter follows the naming convention set by Google Cloud, e.g., c2-standard-16. This convention is composed of a prefix that defines the CPU series, a suffix that sets the number of virtual CPUs (vCPU) per machine and the middle word refers to the level of RAM per vCPU. In the example, c2 refers to an Intel Xeon Scalable processor of 2nd generation, standard means 4 GB of RAM per vCPU and will contain 16 vCPUs. Currently, this is the list of available machine types available via the API.

  • the num_machines sets the number of machines available in the computational resource. While the computational resource is active, these machines will be reserved for the user.

  • the data_disk_gb allows the selection of the size of the disk attached to each machine that is reserved for the simulation data in GB.

  • the spot argument determines if the machines will be preemptible or standard. Preemptible machines can be stopped at any time and for that reason are only advised for fault-tolerant workloads. If simulations are running when they are stopped, the simulation is resubmitted to the queue of the machine group again.

For example, the following code creates a MachineGroup with 2 machines of type c2-standard-16 with 100 GB of disk space each:

import inductiva

machine_group = inductiva.resources.MachineGroup(

Creating an instance of MachineGroup does not start the machines. This only registers the configuration on the API, which can now be used to manage it further.

Managing the MachineGroup#

With your machine_group object ready, starting all of the machines at the same time is as simple as calling machine_group.start().

Within a few minutes, the machines will be set up and ready to pick several simulations simultaneously. At any moment, you can check an estimate of the price per hour of the group with machine_group.estimate_cloud_cost() and when you have finished you can terminate it with machine_group.terminate(). Running simulations will be killed and from this point, the machine_group object cannot be re-used.

To simplify the workflow, the last two functions can also be performed via the CLI.

First, you can check the cost of the group by selecting the machine type and the number of machines you wish to use:

$ inductiva resources cost c2-standard-4 -n 4
Estimated total cost (per machine): 0.919 (0.230) $/h.

When you don’t need the Machine group anymore, you can easily kill it with the name:

$ inductiva resources terminate api-agn23rtnv0qnfn03nv93nc

Machine Group on demand without any hassle.