Numpy To PyTorch

by Shagun Sodhani

data science pytorch numpy best practices

Numpy is the de-facto choice for array-based operations while PyTorch largely used as a deep learning framework. At the core, both provide a powerful N-dimensional tensor. This talk would focus on the similarities and difference between the two and how we can use PyTorch to augment Numpy.

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'


  • 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.

About the Author

Hi, I am Shagun Sodhani. I am a researcher and MSc student at Montreal Institute of Learning Algorithms. I graduated in Computer Science from IIT Roorkee in 2015 and have previously worked as a Machine Learning developer with the Data Science team at Adobe Systems for 2 years. Research takes most of my time though I regularly attend tech-talks and meetups around the city. You may check out my projects, talks and resume.

I love Computer Science in general and Machine Learning in particular. I regularly read and summarise papers from these domains. I am also interested in Maths and Economics. I always look forward to meeting interesting people and learn new stuff, both in-person and remotely. If you want to talk, feel free to drop me a line.

Author website: