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  "Wish it scaled better"

  by TonyTiger on Aug 9, 2024 2:55 PM

  Summary:
  While I appreciate PyTorch's ease of use and flexibility, I've noticed some challenges. The dynamic computation graph, though great for debugging, can be less efficient when scaling up for production. I've also found that PyTorch's lack of built-in support for distributed training makes it harder to work with large models. Transitioning from research to production isn't always smooth, especially with its sometimes tricky integration with deployment tools and platforms.

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