from accelerate import Accelerator
-accelerator = Accelerator()
+accelerator = Accelerator(log_with="wandb")
train_dataloader, model, optimizer scheduler = accelerator.prepare(
        dataloader, model, optimizer, scheduler
)
+accelerator.init_trackers()
model.train()
for batch in train_dataloader:
    optimizer.zero_grad()
    inputs, targets = batch
    outputs = model(inputs)
    loss = loss_function(outputs, targets)
+    accelerator.log({"loss":loss})
    accelerator.backward(loss)
    optimizer.step()
    scheduler.step()
+accelerator.end_training()

## To use experiment trackers with `accelerate`, simply pass the desired tracker to the `log_with` parameter when building the `Accelerator` object. Then initialize the tracker(s) by running `Accelerator.init_trackers()` passing in any configurations they may need. Afterwards call `Accelerator.log` to log a particular value to your tracker. At the end of training call `accelerator.end_training()` to call any finalization functions a tracking library may need automatically. ## To learn more checkout the related documentation: - Using experiment trackers - Accelerator API Reference - Tracking API Reference