PyTorch is designed to be intuitive, linear in thought, and easy to use.When you execute a line of code, it gets executed. There isn't an asynchronous view of the world.When you drop into a debugger or receive error messages and stack traces, understanding them is straightforward.The stack trace points to exactly where your code was defined.We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.
Note: The ideal batch size you use will depend on the specific GPU and dataset and model you're working with. The code below is specifically targeted for the A100 GPU available on Google Colab Pro. However, you may to adjust it for your own GPU. As if you set the batch size too high, you may run into CUDA out of memory errors.
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