Nested Fourier Neural Operater for Darcy Flow
This example demonstrates how to set up a data-driven model for a 2D Darcy flow using the Nested Fourier Neural Operator (FNO) architecture inside of Modulus. Training progress can be tracked through MLFlow. This case is parallelised to run in multi-GPU settings.
Prerequisites
Install the required dependencies by running below:
pip install -r requirements.txt
Start with generating the dataset for training:
python generate_nested_darcy.py
which will create the folder ./data with out_of_sample.npy,
training_data.npy, validation_data.npy.
To train the model on a single GPU, run
python train_nested_darcy.py +model=ref0
python train_nested_darcy.py +model=ref1
For training a model on two GPUs, run
mpirun -n 2 python train_nested_darcy.py +model=ref0
mpirun -n 2 python train_nested_darcy.py +model=ref1
To evaluate the model use:
python evaluate_nested_darcy.py
Progress can be monitored using MLFlow. Open a new terminal and navigate to the training directory, then run:
mlflow ui -p 2458
View progress in a browser at http://127.0.0.1:2458
If training on a remote machine, set up a ssh tunnel to the server with
LocalForward 8080 your_remote_machine_addr:8080. ssh to the server
via the specified port, in this case 8080, navigate to the training
directory and launch mlflow server
mlflow server --host 0.0.0.0 --port 8080
On your local machine, open a browser and connect to localhost:8080.