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smartsim-openFOAM

This repository provides instructions and source files for an example of including the SmartRedis client in OpenFOAM so that OpenFOAM can leverage the machine learning, data analysis, and data visualization capabilities of SmartSim.

The example in this repository adapts the TensorFlowFoam work which utilized a deep neural network to predict steady-state turbulent viscosities of the Spalart-Allmaras (SA) model. The example in this repository highlights that a machine learning model can be evaluated using SmartSim from within an OpenFOAM object with minimal external library code. In this particular case, only four SmartRedis client API calls are needed to initialize a client connection, send tensor data for evaluation, execute the TensorFlow model, and retrieve the model inference result. In the following sections, instructions will be given for building and running the aforementioned example.

TFF-SmartSim-inference

Absolute value of the difference between the ML inferred value of eddy viscosity using SmartSim vs the Spalart Allmaras (SA) calculated value from the original TensorFlowFoam project repository. The geometry upstream from the step has been truncated where the error is lowest.

Prerequisites

OpenFOAM-5.x and ThirdParty-5.x

An installation of OpenFOAM-5.x and ThirdParty-5.x is required to run this example. If you do not have an installation of these packages, instructions are provided here for Cray XC (and similar) systems. The instructions in upcoming sections are applicable to both a system install or user install of OpenFOAM-5.x and ThirdParty-5.x.

MiniConda

This demonstration uses MiniConda. Although it is not strictly required, we recommend its use to simplify the tracking of different environments.

MiniConda can be installed via wget of an appropriate installation script, such as one from

https://docs.conda.io/en/latest/miniconda.html#linux-installers

Theta users: on Theta, MiniConda is available through the module system and can be activated via the command

module load miniconda-3

Environment management

We recommend creating a separate environment for this example:

conda create --name=smartsim-openfoam python=3.8.5
conda activate smartsim-openfoam

SmartSim and SmartRedis

An installation of SmartSim is required to run this example. Additionally, the TensorFlow Python package should also be installed. Note that this example is configured to use the CPU versions of the ML backends, but it can be reconfigured to use GPUs. The aforementioned packages can be installed as follows:

conda install git-lfs
pip install smartsim==0.4.0
smart build --device cpu

SmartRedis is also required for this example. SmartRedis can be installed as follows:

git clone https://github.com/CrayLabs/SmartRedis
cd SmartRedis
make lib
pip install .
cd ../

Python dependencies

The training and visualization steps require additional python packages that can be installed inside of the newly created conda environment as follows:

cd /path/to/smartsim-openFOAM
pip install -r requirements.txt

SimpleFoam_ML

As part of the TensorFlowFoam work, the OpenFOAM program simpleFoam_ML was written to call a custom turbulence model that utilizies a TensorFlow model at the beginning of the simulation and then the turbulent eddy-viscosity is held constant for the rest of the simulation. In this example, simpleFoam_ML is also used. To build the simpleFoam_ML executable, execute the following commands:

source /path/to/OpenFOAM-5.x/etc/bashrc
cd /path/to/smartsim-openFOAM/simpleFoam_ML
wclean && wmake

After executing these commands, verify that simpleFoam_ML is in directory referenced by the environment variable FOAM_APPBIN.

SA_Detailed turbulence model

The OpenFOAM cases that are used to generate training data utilize a custom turbulence model for enhanced data output. To build this turbulence model, execute the following commands:

source /path/to/OpenFOAM-5.x/etc/bashrc
cd /path/to/smartsim-openFOAM/turbulence_models/SA_Detailed
wmake .

After executing these commands, verify that SA_Detailed.so is in directory referenced by the environment variable FOAM_USER_LIBBIN.

ML_SA_CG turbulence model with SmartRedis

The TensorFlow machine learning model is incorporated into the OpenFOAM simulation via a custom turbulence model built as a dynamic library. The directory turbulence_models/ML_SA_CG in this repository contains the source files for the custom turbulence model and the files needed to build the associated dynamic library. These files have been adapted from the TensorFlowFoam OF_Model.

To build the dynamic library, execute the following commands:

export SMARTREDIS_PATH=path/to/SmartRedis
source /path/to/OpenFOAM-5.x/etc/bashrc
cd /path/to/smartsim-openFOAM/turbulence_models/ML_SA_CG
wmake libso .

Note that the environment variable SMARTREDIS_PATH is used to locate the SmartRedis include and install directories and should point to the top level SmartRedis directory.

After executing these commands, verify that ML_SA_CG.so is in directory referenced by the environment variable FOAM_USER_LIBBIN.

Running OpenFOAM with SmartSim

A SmartSim script (driver.py) is provided in this repository to execute the OpenFOAM case with the TensorFlow model. The aforementioned SmartSim script will deploy a SmartSim Orchestrator, generate training data, train the machine learning model, and run the OpenFOAM simulation. The results of the simulation will be in the experiment directory openfoam_ml.

On machines with the Slurm workload manager, run.sh can be used to execute the SmartSim script driver.py. Parameters inside of run.sh can be modified to fit compute resource availability. Be sure to set the run.sh script variables OF_PATH and SMARTREDIS_LIB_PATH to the correct directories before executing run.sh. To run run.sh, execute:

sbatch run.sh

On machines with the Cobalt workload manager (e.g. Theta), run-theta.sh can be used to execute the SmartSim script driver.py. Parameters inside of run-theta.sh can be modified to fit compute resource availability. Be sure to set the run-theta.sh script variables OF_PATH and SMARTREDIS_LIB_PATH to the correct directories before executing run-theta.sh. Also, in the run-theta.sh script, set the miniconda path to the smartsim-openfoam environment. To run run-theta.sh, execute:

qsub run-theta.sh

Notes on the inclusion of SmartRedis in OpenFOAM

In this section, a brief description of the SmartRedis code that was added to OpenFOAM is presented. With the notes herein, users should be able to include SmartRedis in any part of the OpenFOAM code.

Aside from the addition of an include statement in ML_SA_CG.H, all of the SmartRedis content is in ML_SA_CG.C.

In line 447 of ML_SA_GC.C, the SmartRedis client is initialized to utilize a SmartSim Orchestrator in cluster configuration. To use to a non-cluster Orchestrator, simply change the boolean input to the constructor to false.

  // Initialize the SmartRedis client
  SmartRedis::Client client(true);

In line 449 through line 458, a key is contructed for the tensor that will be sent to the database. This logic creates the tensor key model_input_rank_ that is suffixed by the MPI rank if the MPI_COMM_WORLD has been initialized. Using the MPI rank as a suffix is a simple and convenient way to prevent key collisions in a parallel application.

    // Get the MPI rank for key creation
    int rank = 0;
    int init_flag = 0;

    MPI_Initialized(&init_flag);
    if(init_flag==1)
        MPI_Comm_rank(MPI_COMM_WORLD, &rank);

    // Create a key for the tensor storage
    std::string input_key = "model_input_rank_" + std::to_string(rank);

In line 460, the tensor is put into the SmartSim Orchestrator using the put_tensor() API call. The input_vals variable is a tensor that contains the velocity, position, and step height for each point in the mesh. The input_dims is a vector containing the input_vals dimensions. Finally, the last two variable inputs to put_tensor() indicate the data type and memory layout associated with the input_vals variable.

    // Put the input tensor into the database
    client.put_tensor(input_key, input_vals, input_dims,
                      SRTensorTypeFloat,
                      SRMemLayoutNested);

In line 466 through 469, a key is constructed for the tensor output of the ML model, and the TensorFlow model that is stored in the database is executed.

    // Run the model
    std::string model_key = "ml_sa_cg_model";
    std::string output_key = "model_output_rank_" + std::to_string(rank);
    client.run_model(model_key, {input_key}, {output_key});

Finally, in line 473, the tensor output of the TensorFlow model is retrieved and stored in a vector named data. The API function unpack_tensor() is used to place the tensor output data into an existing block of memory, whereas the API function get_tensor() can be used to place the results into a block of memory managed by the SmartRedis client. Following the retrieval of the TensorFlow model ouptut tensor, the data can then be used in OpenFOAM simulation.

    // Get the output tensor
    std::vector<float> data(num_cells, 0.0);
    client.unpack_tensor(output_key, data.data(),
                         {num_cells},
                         SRTensorTypeFloat,
                         SRMemLayoutNested);