Tasks
A Task represents a single execution of a simulation on the Inductiva platform. Tasks are automatically created when you submit a simulation to the API using the run method on any simulator object. Each submission creates a unique task with its own identifier, even when using identical parameters.
Submitting Your First Task
Here's an example of how to create a Task by submitting a SpliSHSPlasH simulation to the API:
# Running the SplishSplash simulator example
import inductiva
# Instantiate machine group
machine_group = inductiva.resources.MachineGroup("c2-standard-4")
machine_group.start()
# Download example input files
input_dir = inductiva.utils.download_from_url(
"https://storage.googleapis.com/inductiva-api-demo-files/"
"splishsplash-input-example.zip", unzip=True)
# Instantiate the simulator
splishsplash_simulator = inductiva.simulators.SplishSplash()
# Run the SPlisHSPlasH simulation with the required .json config file
task = splishsplash_simulator.run(input_dir="splishsplash-input-example",
sim_config_filename="config.json",
on=machine_group)
print(task.id)
# Example output: i4ir3kvv62odsfrhko4y8w2an
Subsequent calls to
splishsplash_simulator.run()with the same input arguments will create a new task.
When a task is submitted, the API provides immediate feedback in the terminal about queue position and monitoring options:
# Number of tasks ahead in the queue: 3.
# · Consider tracking the status of the task via CLI:
# inductiva tasks list --id <task_id>
# · Or, tracking the logs of the task via CLI:
# inductiva logs <task_id>
# · Or, track the task files in real time with:
# inductiva tasks list-files <task_id>
# · You can also get more information about the task via the CLI command:
# inductiva tasks info <task_id>
You can also check task status and queue position programmatically:
# Check queue position
position = task.get_position_in_queue()
if position is not None:
print(f"Tasks ahead in queue: {position}")
# Check current status
print(f"Current status: {task.status}")
Unique Task Identification
Every task receives a unique alphanumeric identifier that allows you to reference it across sessions:
import inductiva
# Create task objects using existing task IDs
task1 = inductiva.tasks.Task("i4ir3kvv62odsfrhko4y8w2an")
task2 = inductiva.tasks.Task("i4ir3kvv62odsfrhko4y8w2an")
# These are different Python objects
print(f"task1 object ID: {id(task1)}") # Output: 4410160112
print(f"task2 object ID: {id(task2)}") # Output: 4389863104
# But they reference the same task
print(f"task1 ID: {task1.id}") # Output: i4ir3kvv62odsfrhko4y8w2an
print(f"task2 ID: {task2.id}") # Output: i4ir3kvv62odsfrhko4y8w2an
Note: Creating multiple
Taskobjects with the same ID doesn't duplicate tasks on the platform. All objects reference the same underlying task.
Task Metadata
Many Inductiva API users generate extensive datasets by running hundreds or thousands of simulation tasks, each with different input parameters. Previously, tracking these parameters required maintaining external mechanisms — either local files or databases — to associate task IDs with their corresponding parameter sets.
Now, it is possible to store these parameters as task metadata directly on the Inductiva platform:
# Run a SpliSHSPlasH simulation
task = splishsplash_simulator.run(input_dir="splishsplash-input-example",
sim_config_filename="config.json",
on=machine_group)
# Store simulation parameters as metadata
task.set_metadata({
"fluid_viscosity": "0.01",
"particle_radius": "0.025",
"simulation_time": "5.0",
"run_number": "2",
})
Note: Both metadata keys and values must be strings. Any numeric values, booleans, or other data types should be converted to strings before being stored as metadata.
And the metadata can later be retrieved by doing:
metadata = task.get_metadata()
You can also manipulate the metadata in the Task's page on the Web Console:
