PC-53293.yaml

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speaker_name: "Shagun Sodhani"

talk_title: "Numpy To PyTorch"

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talk_tags:
- "data science"
- "pytorch"
- "numpy"
- "best practices"


talk_abstract: "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."

talk_details: |
  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'](https://pytorch.org/). 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'](https://pytorch.org/2018/01/19/a-year-in.html)
  
  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](https://github.com/shagunsodhani/PyCon2018). I would use [colab notebooks](https://colab.research.google.com/) 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. 




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about_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.



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author_website: 'https://shagunsodhani.in/'