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Conquering Machine Learning in Web Assembly: An Unorthodox Yet Powerful Approach published 10/10/2023 | 3 min read
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Since 2022 and until today we use AI exclusively (GPT-3 until first half of 2023) to write articles on devspedia.com!
Since 2022 and until today we use AI exclusively (GPT-3 until first half of 2023) to write articles on devspedia.com!
Machine Learning (ML) implementation in web environments has been predominantly spearheaded by JavaScript, the language directly interpreted by browsers. But what if we could leverage the true potential of our hardware by running our ML algorithms in WebAssembly, a binary instruction format, designed as a portable target for the compilation of high-level languages like C, C++, and Rust. This article aims to give quick insights into the usage and benefits of using WebAssembly for ML projects.
WebAssembly: A quick overview
WebAssembly is often abbreviated as wasm, and is a binary format for a stack-based virtual machine. It provides a sandbox execution environment that can be embedded in many environments, including the web browser.
Primarily, wasm has been designed to augment JavaScript and not replace it. It provides a way to run code written in languages like C, C++, and Rust, etc., at near-native speed, providing a speed boost for performance-centric web apps such as games, computer-aided design, video editing, and ML.
#include <stdio.h>
int main() {
printf("Hello, WebAssembly!
");
return 0;
}
After installing emscripten, you could compile this C code to wasm using the command below:
$ emcc hello.c -s WASM=1 -o hello.html
Why use WebAssembly for ML?
The fundamental rule of thumb for choosing a programming paradigm is rooted in its aptness and efficiency for the task at hand:
Performance: WebAssembly code executes at near-native speed by taking advantage of common hardware capabilities available on a wide range of platforms.
Security: WebAssembly outlines a memory-safe, sandboxed execution environment that may even be implemented inside an existing JavaScript virtual machine.
Portability: Applications and platforms already building with WebAssembly capability ensure developers can readily experiment and launch ML operations quickly.
Flexibility: WebAssembly allows developers to funnel programming muscle developed in other languages like C and Rust into the web, augmenting the arena of possibilities in ML.
The road ahead with wasm for ML
Machine learning is a CPU-intensive task that significantly benefit from optimized low-level wasm code. The potential fields of applications are infinite. From the edge-deployed machine learning models of an Internet of Things (IoT) device, the malware detection in a browser, to an AI-based game taking place entirely in your browser, things are just getting started.
One of the significant advancements in this arena has been brought forward by Google's TensorFlow team with their project tfjs, a library for Machine Learning in JavaScript. The TensorFlow backend uses wasm for running on platforms where WebGL is not available.
In conclusion, WebAssembly is a perfect fit for ML tasks. It is high time that developers started incorporating this incredibly efficient tool into their arsenal.
WebAssembly, though in its nascent stages of development, is rapidly emerging as a robust candidate for executing ML tasks in web environments. As we tread the path where AI and ML are becoming intrinsic in Web Apps, WebAssembly provides the much needed performant, secure, and efficient execution environment.
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