Launching and connecting to Fluent#

You can use the launch_fluent() function to start Fluent from Python. This code starts Fluent in the background and starts Fluent’s gRPC server so that commands can be sent to it from the Python interpreter:

>>> import ansys.fluent.core as pyfluent
>>> solver = pyfluent.launch_fluent(
>>>     mode=pyfluent.FluentMode.SOLVER
>>> )

You can use the connect_to_fluent() function to connect to a running Fluent session that has already started the gRPC server. There are some options for starting Fluent’s gRPC server:

  1. Start Fluent with <fluent_executable> -sifile=<server_info_file_name>, or

  2. Execute the server/start-server solution mode TUI command in Fluent, or

  3. Execute File -> Applications -> Server -> Start... from the Fluent GUI ribbon menu in solution mode.

On starting the gRPC server, Fluent writes out a server-info file at <server_info_file_name> and prints this information in the console. If you do not specify a particular <server_info_file_name>, it is automatically generated.

This code connects to a running Fluent session where the server-info file is server.txt in the working directory:

>>> import ansys.fluent.core as pyfluent
>>> solver = pyfluent.connect_to_fluent(
>>>     server_info_file_name="server.txt"
>>> )

Launcher options#

The following examples show different ways that you can launch Fluent locally. For more information, see launch_fluent().

Solver mode#

These two examples show equivalent ways to launch Fluent in solution mode:

>>> solver = pyfluent.launch_fluent(
>>>     mode=pyfluent.FluentMode.SOLVER
>>> )

>>> solver = pyfluent.launch_fluent()

Meshing mode#

This example shows how to launch Fluent in meshing mode:

>>> meshing_session = pyfluent.launch_fluent(
>>>      mode=pyfluent.FluentMode.MESHING
>>> )

Precision#

This example shows how to launch Fluent in solution mode and set the floating point precision:

>>> solver = pyfluent.launch_fluent(
>>>      precision=pyfluent.Precision.DOUBLE
>>> )

Dimension#

This example shows how to launch Fluent in solution mode and set the modeling dimension to two:

>>> solver = pyfluent.launch_fluent(
>>>      precision=pyfluent.Precision.DOUBLE,
>>>      dimension=pyfluent.Dimension.TWO
>>> )

Local parallel#

This example shows how to launch Fluent in solution mode and set the number of processors for local parallel execution:

>>> solver = pyfluent.launch_fluent(
>>>      precision=pyfluent.Precision.DOUBLE,
>>>      dimension=pyfluent.Dimension.TWO,
>>>      processor_count=2
>>> )

Distributed parallel#

This example shows how to launch Fluent in solution mode with 16 processors distributed across more than one machine:

>>> solver = pyfluent.launch_fluent(
>>>     precision=pyfluent.Precision.DOUBLE,
>>>     dimension=pyfluent.Dimension.THREE,
>>>     processor_count=16
>>>     additional_arguments="-cnf=m1:8,m2:8",
>>> )

Logging support#

PyFluent has an option to run with logging enabled. This command enables logging:

>>> pyfluent.logging.enable()

For more details, see Logging.

Scheduler support#

When PyFluent is used within a job scheduler environment, the launch_fluent() function automatically determines the list of machines and core counts with which to start Fluent. The supported scheduler environments are Altair Grid Engine (formerly UGE), Sun Grid Engine (SGE), Load Sharing Facility (LSF), Portable Batch System (PBS), and Slurm.

This example shows a bash shell script that can be submitted to a Slurm scheduler using the sbatch command:

#!/bin/bash
#SBATCH --job-name="pyfluent"
#SBATCH --nodes=8
#SBATCH --ntasks=32
#SBATCH --output="%x_%j.log"
#SBATCH --partition=mpi01
#
# Change to the directory where the Slurm job was submitted
#
cd $SLURM_SUBMIT_DIR
#
# Activate your favorite Python environment
#
export AWP_ROOT242=/apps/ansys_inc/v242
. ./venv/bin/activate
#
# Run a PyFluent script
#
python run.py

Here are a few notes about this example:

  • Eight machines with a total of 32 cores are requested. Fluent is started with the appropriate command line arguments passed to -t and -cnf.

  • The variable AWP_ROOT242 is configured so that PyFluent can find the Fluent installation.

  • The code assumes that a Python virtual environment was pre-configured with PyFluent installed before the job script is submitted to Slurm. You could also configure the virtual environment as part of the job script if desired.

  • The run.py file can contain any number of PyFluent commands using any of the supported interfaces.

Within the scheduler environment, the launch_fluent() function can be used in a few different ways. This example shows how to start the three-dimensional, double precision version of Fluent on all the requested machines and cores:

>>> solver = pyfluent.launch_fluent(
>>>      precision=pyfluent.Precision.DOUBLE,
>>>      dimension=pyfluent.Dimension.THREE
>>> )

You can use the processor_count argument to set the number of cores that Fluent uses:

>>> solver = pyfluent.launch_fluent(
>>>     precision=pyfluent.Precision.DOUBLE,
>>>     dimension=pyfluent.Dimension.THREE,
>>>     processor_count=16,
>>>     mode=pyfluent.FluentMode.SOLVER
>>> )

Passing the processor_count parameter like this forces execution of Fluent on 16 cores, despite the fact that the Slurm submission requests 32 total cores from the job scheduler. This behavior may be useful in situations where the scheduler environment allocates all the cores on a machine and you know that Fluent may not scale well on all the allocated cores.

Finally, if you want to ignore the scheduler allocation, you can pass either the -t argument or both the -t and -cnf arguments to the launch_fluent() function using the additional_arguments parameter. For local parallel execution, simply pass the -t argument:

>>> solver = pyfluent.launch_fluent(
>>>     precision=pyfluent.Precision.DOUBLE,
>>>     dimension=pyfluent.Dimension.THREE,
>>>     additional_arguments="-t16"
>>> )

For distributed parallel processing, you usually pass both parameters:

>>> solver = pyfluent.launch_fluent(
>>>     precision=pyfluent.Precision.DOUBLE,
>>>     dimension=pyfluent.Dimension.THREE,
>>>     additional_arguments="-t16 -cnf=m1:8,m2:8",
>>> )

The launch_fluent() function also supports the scheduler_options parameter to submit the Fluent job to a Slurm scheduler without using any bash script:

>>> slurm = pyfluent.launch_fluent(
>>>     scheduler_options={
>>>         "scheduler": "slurm",
>>>         "scheduler_headnode": "<headnode>",
>>>         "scheduler_queue": "<queue>",
>>>         "scheduler_account": "<account>"
>>>     },
>>>     additional_arguments="-t16 -cnf=m1:8,m2:8",
>>> )
>>> solver = slurm.result()

The keys scheduler_headnode, scheduler_queue and scheduler_account are optional and should be specified in a similar manner to Fluent’s scheduler options. Here, the launch_fluent function returns a SlurmFuture instance from which the PyFluent session can be extracted. For a detailed usage, see the documentation of the slurm_launcher module.

The scheduler_options parameter doesn’t support the automatic scheduler allocation, the -t and -cnf arguments must be passed to the launch_fluent() function using the additional_arguments parameter for distributed parallel processing.