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We have Radim Rehurek.
Google's word2vec
There is no manual input
Unsupervised deep learning

It needs a lot of data to do this

This talk is about implementation

The code is written in C, very optimized, but hard to read and optimize

I ported it to Python to understand the code better.
streaming (generator) for inputI put a nicer interface. Connected to that, you can use the whole ecosystem of Python.
My port used NumPy which is the standard in the Python world. I was pretty happy with the result. But it was still 20 times slower than the C implementation.
I had to translate some Python back to C. But it is a HPC application, tiny core which takes a lot of time and the rest doesn't consume much. After some profiling and debugging, I managed to match the speed of the C implementation.
Basic Linear Algebra Subroutines
I translated the core routine into using these BLAS.

Optimized NumPy can still be optimized by about two orders of magnitude.
Diagram explaining implementation
The sentences come from a stream - there is no seeking to middle of sentences.
Worker threads
The port has slightly better accuracy too.
not so good
Cython memoryviews - did not perform as well as plain pointers.
Cython's dynamic compliation doesn't work as expected

BLAS idiosyncracies

Python 3 compatibility

Conclusion slide
QuestionsMemory usage - is this linear growth?It takes about double the amount of memory.
Have you tried numba?
Engineering performant code for deployment scenarios? Tradeoff between speed and maintainability?The tradeoff I make is - depends on how much performance it gets.
You mentioned gensim. Can you tell us more about it?
Gensim is a tool for large scale semantic modeling - I am the author.
Correctness of the code - can you tell us about your test strategy?
It was a bit easier than usual academic papers. We already had a reference implementation. Testing can be done by comparing how it works with word2vec. Mostly standard unit testing stuff.
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