PyTorch is an open source machine learning framework used for both research prototypes and production implementation. According to him source code repositoryPyTorch provides two high-level features:

  • Tensor calculation (such as NumPy) with high GPU acceleration.
  • Deep neural networks built on a tape-based autograde system.

Originally developed at Idiap Research Institute, NYU, NEC Laboratories America, Facebook and Deepmind Technologies, with contributions from the Torch and Caffe2 projects, PyTorch now has a thriving open source community. PyTorch 1.10, launched in October 2021, has 426 contributors, and the repository currently has 54,000 stars.

This article is an overview of PyTorch, including new features in PyTorch 1.10 and a quick guide to getting started with PyTorch. I previously reviewed PyTorch 1.0.1 and compared TensorFlow and PyTorch. I suggest you read the review for an in-depth discussion of the PyTorch architecture and how the library works.

The evolution of PyTorch

In the beginning, scientists and researchers were attracted to PyTorch because it was easier to use than TensorFlow for development of models with graphic processors (GPU). PyTorch is the default impatient execution mode, which means that its API calls are executed on call, instead of being added to the schedule to be launched later. TensorFlow has since improved its support for impatient performance, but PyTorch is still popular in academia and research.

At this point, PyTorch is ready for production, which allows you to easily switch between impatient modes and graphics with TorchScriptand speeding up the road to production with TorchServe. IN torch.distributed back end allows scalable distributed training and optimization of productivity in research and production, and the rich ecosystem of tools and libraries expands PyTorch and supports the development of computer vision, natural language processing and more. Finally, PyTorch is well supported on major cloud platforms, including Alibaba, Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure. Cloud support ensures seamless development and easy scaling.

What’s new in PyTorch 1.10

According to the PyTorch blog, PyTorch 1.10 updates are focused on improving learning and productivity, as well as developer usability. look Notes on PyTorch version 1.10 for details. Here are some highlights from this issue:

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