This example demonstrates how to set up a data-driven model for a 2D
Darcy flow using the Fourier Neural Operator (FNO) architecture inside
of Modulus. Training progress can be tracked through
MLFlow. This example
runs on a single GPU, go to the darcy_nested_fno example for
exploring a multi-GPU training.
To train the model, run
python train_fno_darcy.py
training data will be generated on the fly.
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.