Create App.py

App.py

@@ -0,0 +1,15 @@
+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)