##
<pre>
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()
</pre>
##
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:
- [Basic Tutorial](https://huggingface.co/docs/accelerate/usage_guides/tracking)
- [Accelerator API Reference](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.log)
- [Tracking API Reference](https://huggingface.co/docs/accelerate/package_reference/tracking)