JWebAssembly VS NumPy

For those that don't know. Wasm is a bytecode that aims to improve performance in browsers. Instead of having to interpret quite complex high-level code, the wasm interpreter takes a specialized bytecode and runs that on a simulated cpu. This not only promises to be a lot more performant than JS but it gives us another big advantage: In theory any language can be compiled into. The biggest supported languages atm are C++, Rust, JS, Ruby, Go and Python. Kotlin and Java are not officially supported, but Kotlin supports native compilation to WASM and Java has the JWebAssembly project.

JWebAssembly

Done

https://github.com/i-net-software/JWebAssembly

> you wouldn't write your Tensorflow code in Java

Why?

Kernel modules and device drivers are probably the only example where you need to pick another tool.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]