Run massive simulations when your client needs it. Pay nothing when they don’t.
Download the required files here and place them in a folder called SimulationFiles. Then, you’ll be ready to send your simulation to the Cloud.
Here is the code required to run a AMR-Wind simulation using the Inductiva API:
"""AMR-Wind example."""
import inductiva
# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c2d-highcpu-4",
spot=True)
# Initialize the Simulator
amr_wind = inductiva.simulators.AmrWind(\
version="3.4.1")
# Run simulation
task = amr_wind.run(input_dir="/Path/to/SimulationFiles",
sim_config_filename="abl_amd_wenoz.inp",
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=Trueenables 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 AMR-Wind simulations, replace input_dir with the path to your AMR-Wind input files
and set the sim_config_filename accordingly. Be sure to specify the AMR-Wind 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 16/04, 14:31:00 0.872 s
In Queue at 16/04, 14:31:01 33.07 s
Preparing to Compute at 16/04, 14:31:34 1.673 s
In Progress at 16/04, 14:31:35 2.954 s
└> 2.832 s /opt/openmpi/4.1.6/bin/mpirun --use-hwthread-cpus amr_wind abl_amd_wenoz.inp
Finalizing at 16/04, 14:31:38 1.547 s
Success at 16/04, 14:31:40
Data:
Size of zipped output: 13.33 MB
Size of unzipped output: 52.24 MB
Number of output files: 92
Total estimated cost (US$): 0.010063 US$
Estimated computation cost (US$): 0.000063 US$
Task orchestration fee (US$): 0.010 US$
Note: A per-run orchestration fee (0.010 US$) applies to tasks run from 01 Dec 2025, in addition to the computation costs.
Learn more about costs at: https://inductiva.ai/guides/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 seconds.
You’ve only just scratched the surface with a very short simulation. Ready for real results? Check out this tutorial to learn how to run longer simulations on high-performance machines.