2025-09-12 –, Room 5
Building your own minimalist NumPy like, ArryPy, it reveals the secret sauce behind high-performance array computing.
In this hands-on workshop, you’ll start with a naïve Python loop, then progressively supercharge it: first pure-Python ufuncs, next Cython-typed memoryviews, and finally native C with SIMD via pybind11.
Along the way, you’ll benchmark each stage in a live leaderboard, turning optimization into a competition. You’ll leave equipped with concrete skills in profiling, vectorization, and extension modules—and a clear roadmap for writing faster Python code in any project.
The main aim of this workshop is to build a numpy like library that will extract as much performance from Python as possible.
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First implement a basic class based library which people can start building, testing bed will be provided to then to easily verify whatever they are building. Basically using a lot of dunder methods.
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Broadcasting Logic in Python and Zero-Copy Views, Build slicing that returns views, not copies. Add stride-trick logic to handle mismatched shapes
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Start replacing working codes with Cython
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Replace Cython with Native C & SIMD via pybind11
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Benchmarking & Profiling Deep Dive
- Use pytest-benchmark for reproducible results.
- Comfortable with Python
- Have an understanding of Object Oriented Programming and Modules
- Understanding basic compilation in C.
- Writing Basic C code.
- Have any of the Linux, MacOS or Windows machine
- Have following installed in your machine:
- Python3
- PIP
- GNU C compiler
- Cython
Intermediate
Abhik Sarkar is an experienced Python Machine Learning Engineer with over eight years of expertise in scalable, intelligent solutions. He currently leads the Machine Learning department at a high-growth startup specializing in computer vision technologies for safety and security.
Developer working at the intersection of DevOps, machine reliability, and system observability using Python to bridge logic and low-level performance.