Run Your First Simulation

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

We will cover the closed_box use case from the examples available in the official CaNS documentation, to help you get started with simulations.

Prerequisites

1. Download the required files here and save them to a folder named SimulationFiles.
2. In the SimulationFiles folder, create a new folder named data. The simulator writes files to this folder and will encounter an error if it is not present.

Then, you’ll be ready to send your simulation to the Cloud.

Running a CaNS Simulation

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

"""CaNS example."""
import inductiva

# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
    provider="GCP",
    machine_type="c2d-highcpu-16",
    spot=True)

# Initialize the Simulator
cans = inductiva.simulators.CaNS(\
    version="2.4.0")

# Run simulation
task = cans.run(input_dir="/Path/to/SimulationFiles",
    sim_config_filename="input.nml",
    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-16) equipped with 16 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 the code for this or any other use case, simply replace input_dir with the path to your CaNS input files and set the sim_config_filename accordingly. Be sure to specify the CaNS 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 17/04, 15:25:52      0.797 s
    In Queue                  at 17/04, 15:25:52      73.225 s
    Preparing to Compute      at 17/04, 15:27:06      1.518 s
    In Progress               at 17/04, 15:27:07      126.298 s
        └> 126.177 s       /opt/openmpi/4.1.6/bin/mpirun --use-hwthread-cpus cans input.nml
    Finalizing                at 17/04, 15:29:13      1.236 s
    Success                   at 17/04, 15:29:15

Data:
    Size of zipped output:    3.50 MB
    Size of unzipped output:  203.68 MB
    Number of output files:   3260
Total estimated cost (US$): 0.0142 US$
    Estimated computation cost (US$): 0.0042 US$
    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/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 126.3 seconds (around 2 minutes).