inductiva 0.3.0

Python client for the Inductiva API

Inductiva API Python client

Inductiva is a Python package designed for executing large-scale simulations of physical systems directly in the cloud.

This offers several distinct advantages:

• 🔄 It consolidates various simulation domains, including fluid dynamics, molecular dynamics, plasmas, and structural mechanics, under a single unified entry point.
• 📦 Eliminates the need to install and manage complex simulation software and corresponding dependencies.
• 🚀 Allows running hundreds or even thousands of simulations concurrently, with no additional effort.
• 💽 Automatically optimizes hardware configurations for each type of simulation (e.g., CPU vs. GPU, appropriate number of CPU cores, RAM, etc.).
• 🐍 You're not limited to a graphical interface or intricate configuration scripts. Instead, you write small Python programs that seamlessly integrate with your existing codebase.

Installation

On your terminal:

pip install inductiva

API access tokens

Please request API token and add the following line to your code:

import inductiva

inductiva.api_key = "YOUR_API_KEY"

And you are good to go! You can start exploring our tutorial notebooks.

Scenarios

Inductiva API contains pre-built scenarios that define physical systems of interest ready to simulate. Users can choose some parameters and configure the system according to their needs, run the simulation using the most adequate resources and visualize the results.

Let's go through the currently available scenarios in Inductiva API.

Protein solvation

The protein solvation scenario models the dynamics of a protein whose structure is described by a PDB file. The protein is placed in a cubic box filled with water. If the protein has a non-zero electric charge, charged ions are added to the solution to neutralize the system. First, the system undergoes an energy minimization process to eliminate any steric clashes or structural issues within the protein-water system. After this, the position of the atoms in this system is updated according to Newton's equation in discrete time steps. The force that acts upon the particles is computed using standard molecular force fields.

Example

Initialize the scenario:

from inductiva import molecules

scenario = molecules.scenarios.ProteinSolvation("protein.pdb", temperature=300)

The user must provide the path for the PDB file corresponding to the protein to be simulated. Additionally, the temperature (in Kelvin) can be specified, which defaults to 300 K.

Run the simulation:

task = scenario.simulate(simulation_time_ns=10, n_steps_min=5000)

output = task.get_output()

Users can set the simulation duration (in ns) and the number of steps for the energy minimization.

Visualize the results:

view = ouptut.render_interactive(representation="ball+stick", add_backbone=True)

This yields an interactive visualization of the protein's trajectory that can be visualized and manipulated in a standard jupyter notebook. The user can specify the representation used for the protein and choose to add the backbone to the visualization.

Available scenarios

These are currently available scenarios:

• Coastal Dynamics
• Wind Tunnel
• Fluid Tank
• Protein Solvation

Simulators

Inductiva API has available several open-source simulators ready to use. Users familiar with the simulators can easily start running simulations with their previously prepared simulation configuration files. In this way, they can take advantage of performant hardware to speed up their simulation and exploration.

The simulators we provide are all open-source and have their own dedicated documentation.

Currently, we have available the following simulators:

• SPlisHSPlasH
• DualSPHysics
• OpenFOAM
• SWASH
• XBeach
• GROMACS

If you would like other simulators to be added, contact us at [e-mail].

Example

Example of how to use the simulators:

simulator = inductiva.simulators.DualSPHysics()

output_dir = simulator.run(input_dir="FlowCylinder",
                           sim_config_filename="CaseFlowCylinder_Re200_Def.xml",
                           output_dir="Flow",
                           device="gpu")

The user must specify the input directory, the simulation configuration file, the output directory and the device to run the simulation on.

Find more examples of simulations in the tutorials section.

Async API

Up until now, all examples have run synchronously, which allows users to get feedback while the simulation is running. However, this is not always the best option. For example, if the user wants to run a large number of simulations, it is better to run them asynchronously. This way, the user can launch all the simulations and then check the results when they are ready.

Let's look at an example using the wind tunnel scenario:

from inductiva import fluids

# Initialize scenario with defaults
scenario = fluids.scenarios.WindTunnel()

# Path to a set of objects
object_path = "path/to/vehicle.obj"

# Run simulation
task = scenario.simulate(object_path=object_path,
                         run_async=True)

# Blocking call to obtain the results
output = task.get_output()

In this way, the simulation is launched asynchronously and the user can continue with other tasks. When the user wants to retrieve the results, they can do so by calling the get_output() method. This method will block until the results are ready.

Running simulations asynchronously allows users to launch multiple simulations in parallel. Let's look at an example:

from inductiva import fluids

# Initialize scenario with defaults
scenario = fluids.scenarios.WindTunnel()

# Path to a set of vehicles
vehicle_path_list = ["vehicle_1.obj", "vehicle_2.obj", ..., "vehicle_1000.obj"]

tasks_list = []

for vehicle in vehicle_path_list:
    task = scenario.simulate(object_path=vehicle,
                             run_async=True)
    tasks_list.append(task)

All of the simulations will be launched in one go. The user can check the status of the simulations and retrieve the results when they are ready. Check the FAQ section for more information on how to do this.

FAQ:

• Getting Started
• Task Management
• Machine Group