Run Your First Simulation

This tutorial will show you how to run Quantum ESPRESSO simulations using the Inductiva API.

To help you get started with simulations, we will explore a use case from this Atomsk tutorial, which guides us in creating a unit cell for fcc aluminium.

Prerequisites

Copy and create your Al.pw file, placing it in a designated folder. Then, make the following adjustments:

• Remove the line of code that specifies the pseudo directory path: pseudo_dir = '/home/user/espresso/pseudo/'.

• Under the section labeled ATOMIC_SPECIES, update the pseudopotential file: Change Al 26.982 Al.fixme.upf to Al 26.982 Al.pbe-nl-rrkjus_psl.1.0.0.UPF.

You’re ready to send your simulation to the Cloud!

Running a Quantum ESPRESSO Simulation

Here is the code required to run a Quantum ESPRESSO simulation using the Inductiva API:

"""Quantum ESPRESSO example."""
import inductiva
from inductiva.commands import MPIConfig, Command

# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
    provider="GCP",
    machine_type="c2d-highcpu-4",
    spot=True)

# List of commands to run
commands = [
    "pw.x -i Al.pw"
]

# Initialize the Simulator
qe = inductiva.simulators.QuantumEspresso(\
    version="7.4.1")

# Run simulation
task = qe.run( \
    input_dir="/Path/to/SimulationFiles",
    commands=commands,
    on=cloud_machine)

# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()

task.download_outputs()

task.print_summary()

In this basic example, we’re using a cloud machine (c2d-highcpu-4) equipped with 4 virtual CPUs. For larger or more compute-intensive simulations, consider adjusting the machine_type parameter to select a machine with more virtual CPUs and increased memory capacity. You can explore the full range of available machines here.

Note: Setting spot=True enables the use of spot machines, which are available at substantial discounts. However, your simulation may be interrupted if the cloud provider reclaims the machine.

To adapt this script for other Quantum ESPRESSO simulations, replace input_dir with the path to your Quantum ESPRESSO input files. Be sure to specify the QUANTUM ESPRESSO version compatible with your input files.

When the simulation is complete, we terminate the machine, download the results and print a summary of the simulation as shown below.

Task status: Success

Timeline:
	Waiting for Input         at 21/04, 16:15:34      0.834 s
	In Queue                  at 21/04, 16:15:34      30.538 s
	Preparing to Compute      at 21/04, 16:16:05      7.544 s
	In Progress               at 21/04, 16:16:13      3.174 s
		└> 3.061 s         /opt/openmpi/4.1.6/bin/mpirun --np 4 --use-hwthread-cpus pw.x -i Al.pw
	Finalizing                at 21/04, 16:16:16      0.413 s
	Success                   at 21/04, 16:16:16      

Data:
	Size of zipped output:    603.96 KB
	Size of unzipped output:  1.48 MB
	Number of output files:   10

Estimated Task Compute Cost = 0.00011 US$
Task Orchestration Fee = 0.01 US$
Total Estimated Cost = 0.01011 US$
Learn more about costs at: https://inductiva.ai/guides/how-it-works/basics/how-much-does-it-cost

As you can see in the “In Progress” line, the part of the timeline that represents the actual execution of the simulation, the core computation time of this simulation was approximately 3.2 seconds.

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