neural-fortran Example

This example shows that wf is completely independent of the ML framework. It uses neural-fortran to train the same sin(x) approximation and logs identical metrics.

Source: example/neural_fortran_logging/train_with_wandb.f90

Metrics logged

• epoch

• training_loss

• validation_loss

• learning_rate

Running

source tools/setup_env.sh
fpm run --example neural_fortran_logging

Key code

use neural
use wf

! initialise wandb
call wandb_init(project="wandb-fortran-nf", name="sine-neural-fortran")
call wandb_config_set("num_epochs",    num_epochs)
call wandb_config_set("learning_rate", real(learning_rate, kind=8))
call wandb_config_set("framework",     "neural-fortran")

! training loop
do epoch = 1, num_epochs
   ! ... neural-fortran forward / backward / update ...
   if (mod(epoch, 10) == 0) then
      call wandb_log("epoch",           epoch,                     step=epoch)
      call wandb_log("training_loss",   real(train_loss, kind=8),  step=epoch)
      call wandb_log("validation_loss", real(val_loss,   kind=8),  step=epoch)
      call wandb_log("learning_rate",   real(learning_rate,kind=8),step=epoch)
   end if
end do

call wandb_finish()
call wandb_shutdown()

fpm dependency

[[example]]
name = "neural_fortran_logging"
source-dir = "example/neural_fortran_logging"
main = "train_with_wandb.f90"
[example.dependencies]
neural-fortran = { git = "https://github.com/modern-fortran/neural-fortran" }

Framework-agnostic design

The only framework requirement is that you import wf. The wandb_log, wandb_config_set, and wandb_init calls are identical regardless of the ML library used for training — athena, neural-fortran, or any other Fortran framework.