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This week you'll learn about What's New in PyTorch 2.0? torch.compile.

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Since its conception, PyTorch has essentially been one of the pillars of the deep learning community (along with TensorFlow) when it came to deep learning frameworks. The flexibility, ease of use,  and strong user support have made PyTorch a staple in the deep learning community, and it's likely to remain an important tool for researchers and developers in the future. 

So how do you ensure that the continued innovation of the PyTorch framework is at par with the ever-so-fast evolution of the computation capabilities of GPUs?

We take you through a little tour of the next generation PyTorch series, PyTorch 2.0, which aims to revolutionize an already strong framework by taking it to the next level.  

The big picture: The creators of PyTorch recently announced the release of PyTorch 2.0, the long-anticipated successor of PyTorch 1.x. According to the creators: 

PyTorch's biggest strength beyond our amazing community is that we continue as a first-class Python integration, imperative style, simplicity of the API and options. PyTorch 2.0 offers the same eager-mode development and user experience, while fundamentally changing and supercharging how PyTorch operates at compiler level under the hood. We are able to provide faster performance and support for Dynamic Shapes and Distributed. —PyTorch 2.0

How it works: While retaining its essence, PyTorch 2.0 brings in new technologies like TorchDynamo, TorchInductor, AOT Autograd, etc., which aims to significantly improve execution time for all kinds of operations. Due to its Pythonic nature, the PyTorch interface has always been intuitive and easy to use. PyTorch 2.0 aims to make it even more Pythonic with the introduction of torch.compile, where all the technologies mentioned above work in unison for better abstraction and optimization. They also provide backend support for various architectures. 

Our thoughts: PyTorch 2.0 shows how much the developers of PyTorch truly care about it and the community. Introducing these diabolical changes to make experimenting with deep learning architectures faster, all while keeping the essence of PyTorch 1.x alive, is indeed a direction that PyTorch's ever-growing community will deeply appreciate. 

Yes, but: PyTorch 2.0 is still in its initial stages. However, as PyTorch has always had a strong community, feedback from the community will play a key role in the direction toward which PyTorch 2.0 moves. 

Stay smart: Don't miss out on the next big thing in deep learning! Instead, closely follow the releases of PyTorch 2.0 and their quest to cater this powerful new tool to the community's needs.

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