![]() Now we know how PyTorch 2.0 works, let's get started. If you want to learn more about PyTorch 2.0, check out the official “GET STARTED”. This allows PyTorch 2.0 to achieve a 1.3x-2x training time speedups supporting today's 46 model architectures from HuggingFace Transformers Those new features include top-level support for TorchDynamo, AOTAutograd, PrimTorch, and TorchInductor. If you ask yourself, why is there a new major version and no breaking changes? The PyTorch team answered this question in their FAQ: “We were releasing substantial new features that we believe change how you meaningfully use PyTorch, so we are calling it 2.0 instead.” Pytorch 2.0 will not require any modification to existing PyTorch code but can optimize your code by adding a single line of code with model = pile(model). PyTorch 2.0 or, better, 1.14 is entirely backward compatible. Fine-tune & evaluate BERT model with the Hugging Face Trainerīefore we can start, make sure you have a Hugging Face Account to save artifacts and experiments.Setup environment & install Pytorch 2.0.It will cover how to fine-tune a BERT model for Text Classification using the newest PyTorch 2.0 features. This blog post explains how to get started with PyTorch 2.0 and Hugging Face Transformers today. ![]() On December 2, 2022, the PyTorch Team announced PyTorch 2.0 at the PyTorch Conference, focused on better performance, being faster, more pythonic, and staying as dynamic as before. ![]()
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