App.py

Compare your experiments with Aim on Spaces
Let's use a quick example of a PyTorch CNN trained on MNIST to demonstrate end-to-end Aim on Spaces deployment. The full example is in the Aim repo examples folder.

from aim import Run
from aim.pytorch import track_gradients_dists, track_params_dists

# Initialize a new Run
aim_run = Run()
...
items = {'accuracy': acc, 'loss': loss}
aim_run.track(items, epoch=epoch, context={'subset': 'train'})

# Track weights and gradients distributions
track_params_dists(model, aim_run)
track_gradients_dists(model, aim_run)