| 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) |