swi-ml

Table of Contents:

• What is swi-ml?
• But what is a switchable backend?
  • CuPy comes to rescue!
• How do I use it?
• What dependencies do I need to install?
• Roadmap/Code/Documentation
• Cherry Points

Small Disclaimer: This library is NOT an alternative to amazing libraries like scikit-learn and cuML. Their interfaces are complete on their own!

What is swi-ml?

swi-ml is a small lightweight Python library, which implements a subset of classical machine learning algorithms, with a twist - it has a switchable backend!

from swi_ml import set_backend

# numpy backend (CPU)
set_backend("numpy")

# cupy backend (GPU)
set_backend("cupy")

But what is a switchable backend?

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It is the backbone for many such libraries, like scikit-learn.

But, most of its code is in serial order.

numpy is primarily designed to be as fast as possible on a single core, and to be as parallelizable as possible if you need to do so.

Moreover, vanilla NumPy cannot accelerate computing on a GPU.

CuPy comes to rescue!

CuPy is an open-source array library accelerated with NVIDIA CUDA, which speeds up some operations more than 100X.

“CuPy’s interface is highly compatible with NumPy; in most cases it can be used as a drop-in replacement.” - So why not use this and create a small library which implements a domain-problem (machine learning in this case) and benefit from both libraries at a single interface?

This is what swi-ml does.

It was built in a duration of a month, where I mostly learnt:

• Test-Driven development
• Object-Oriented code structure
• building a pip installable library
• implementing correct Logging (bybye print() statement!)
• and most importantly - implementing a singleton Backend (with correct raises and runtime imports)

How do I use it?

swi-ml is published to PyPI at this URL: https://pypi.org/project/swi-ml/

So installing it is as simple as:

pip install swi-ml

Or, via GitHub:

pip install git+https://github.com/aitikgupta/swi-ml

What dependencies do I need to install?

Except NumPy, there’s no hard-dependency!

Although, to use the GPU-accelerated backend, a working installation of CuPy (install guide) is required.

Other than this, some plot functions might require a working installation of matplotlib, but it will be a runtime-dependency soon (see Roadmap).

(Recommended) Checkout the README.md on GitHub for a detailed guide.

Roadmap/Code/Documentation

Code is open sourced at GitHub, one can find many, many comments within the library.

Cherry Points

There was a time in developing cycles, when I decided to change the name of the library - cherry points if one can figure out the previous name! (HINT: It is close to an amazing library by RapidsAI, and has a commit dedicated to it)

Aitik Gupta

I go by @aitikgupta throughout the web! \o/

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