Turn days of simulation babysitting into minutes of automated, parallel computing.
This tutorial will show you how to run SFINCS simulations using the Inductiva API.
The use case covered in this tutorial is featured in the following publication: Camila Gaido Lasserre, Kees Nederhoff, Curt D. Storlazzi, Borja G. Reguero, Michael W. Beck (2024). "Improved efficient physics-based computational modeling of regional wave-driven coastal flooding for reef-lined coastlines." Ocean Modelling.
Download the use case here and copy
the sfincs_netbndbzsbzifile.nc file from Models/HighReliefArea/SFINCS/BoundaryConditions/rp_000_SLR000 to the Models/HighReliefArea/SFINCS/InputFiles folder.
The InputFiles folder should contain the following files:
sfincs.dep sfincs.man
sfincs.gms sfincs.msk
sfincs.ind sfincs.obs
sfincs.inp sfincs.obs.bak
sfincs.inp.bak sfincs_netbndbzsbzifile.nc
Here is the code required to run a SFINCS simulation using the Inductiva API:
"""SFINCS example"""
import inductiva
# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c2d-highcpu-8",
spot=True)
# Initialize the Simulator
sfincs = inductiva.simulators.SFINCS(\
version="2.2.1")
# Run simulation
task = sfincs.run(\
input_dir="/Path/to/Models/HighReliefArea/SFINCS/InputFiles",
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-8) equipped with 8 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 SFINCS simulations, replace input_dir with the
path to your SFINCS 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 04/06, 14:53:34 3.135 s
In Queue at 04/06, 14:53:37 38.267 s
Preparing to Compute at 04/06, 14:54:16 2.278 s
In Progress at 04/06, 14:54:18 1823.975 s
└> 1823.822 s sfincs
Finalizing at 04/06, 15:24:42 2.335 s
Success at 04/06, 15:24:44
Data:
Size of zipped output: 74.44 MB
Size of unzipped output: 88.29 MB
Number of output files: 7
Total estimated cost (US$): 0.034 US$
Estimated computation cost (US$): 0.024 US$
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 1824 seconds (30 minutes and 24 seconds).
💻 Want to run SFINCS directly from Deltares' repository? Check out this tutorial.
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