...                     )
...                 else:
...                     pipeline.save_pretrained(config.output_dir)
```  
Phew, that was quite a bit of code! But you're finally ready to launch the training with 🤗 Accelerate's [`~accelerate.notebook_launcher`] function. Pass the function the training loop, all the training arguments, and the number of processes (you can change this value to the number of GPUs available to you) to use for training:  
```py
>>> from accelerate import notebook_launcher