Numpy is the de-facto choice for performing array-based operations while PyTorch is largely used as 'a deep learning framework for fast, flexible experimentation'. Even though the two descriptions sound different, both the libraries provide access to a powerful N-dimensional array (or as we say in PyTorch - tensor). PyTorch supports tensor computations (similar to Numpy) with strong GPU acceleration. In some sense, PyTorch can be used as a replacement for Numpy to use the power of GPUs (even if your use-case is not a machine learning use case). The cost of converting a numpy ndarray to torch tensor is quite negligible as they share the same storage. Unfortunately, PyTorch cannot be used like a drop-in replacement for Numpy though PyTorchs is 'expect to get closer and closer to NumPy’s API where appropriate'

Content:

• One slide about 'what is Numpy'
• Few slides about 'what is ndarray' and 'operations supported' by ndarray.
  • These slides would not go into the internals and are presented to keep the presentation self-contained.
• Few slides about 'what is PyTorch'
  • These slides would provide a brief background information about PyTorch
• Few slides about 'what is torch.Tensor'
• Some slides about syntactical similarities and difference between the two apis
  • This would be a very important part of the presentation.
  • Here we would spend time on subtle differences between the two apis - eg Numpy uses axis while PyTorch uses dim and gotchas to look out for.
  • We would talk about operations that are not yet supported in PyTorch.
  • A small pointer about the performance of PyTorch tensors and Numpy ndarrays.
  • I am expecting a few questions from the audience during this part
• Why do we want PyTorch
• One slide about how to convert Numpy ndarray to torch Tensor and vice-versa
• Some slides about moving tensors to GPU
  • How to move tensor
  • Things to take care of - eg do not move data between every operation. If your data lies on GPU, try to do as many operations there as possible.
• How to use other numerical computation libraries with PyTorch
• Where to go from here
• Conclusion

The slides and notebook are available here. I would use colab notebooks for demoing the code. Note that I am specifically avoiding the perspective of how to train neural networks using PyTorch and want to focus on the interplay between PyTorch and Numpy.