In recent years, the field of machine learning has seen remarkable advancements, enabling us to build complex and accurate models. However, running these models often requires significant computational resources and specialized hardware. Enter the WEBGPU backend, a groundbreaking development that allows machine learning models to be exported and run directly in the browser. In this blog post, I will share my experience implementing the WEBGPU backend in a machine learning framework and demonstrate the exceptional speed and efficiency achieved by running an EfficientNet model in the browser.
WebGPU EfficientNet Demo
Note: The demo currently only works through the latest version of chrome on desktop
It all started with a simple question: “Could we integrate WEBGPU into tinygrad?” I was already aware of WEBGPU and the promises it seeked to bring, but it was and still is early in its implementation. There also wasn’t too many options for running and testing code outside the browser.
I stumbled upon a WEBGPU implementation in rust with python bindings called wgpu-py designed to unleash the full potential of modern GPUs. This low-level graphics and compute API promised the speed and performance required to execute machine learning models efficiently using WGSL(WEBGPU shader language).
Implementing the WEBGPU backend into tinygrad was no easy task. It required diving deep into the documentation of wgpu-py, learning the ins and outs of WGSL, and understanding how to leverage the power of WEBGPU. But with every challenge came a valuable lesson, and I was determined to crack the code.
After overcoming numerous hurdles, I finally implemented the WEBGPU backend for tinygrad in its entirety. To test its true potential, I decided to port an Efficientnet model into WEBGPU and see what kind of performance I could get in the browser.
The results left me speechless! The model ran at lightning speed, making predictions in near real-time, all within the confines of a web browser. It was a magical moment, witnessing the culmination of my efforts as the Efficientnet model seamlessly analyzed and processed images with remarkable accuracy.
With the WEBGPU backend in place, the possibilities are endless. From running complex neural networks for image recognition to natural language processing tasks, the browser is now a powerhouse for machine learning. No more waiting for long model downloads or struggling with compatibility issues—simply open a web page, and let the models work their magic!
Implementing the WEBGPU backend to tinygrad has been an incredible journey of learning, perseverance, and awe-inspiring discoveries. Witnessing the power of machine learning come to life in the browser is nothing short of magical. As we explore this new frontier, let us embrace the possibilities and unlock a world where machine learning is accessible to all, without any barriers.
Happy coding, and may your web-powered machine learning adventures be as exhilarating as mine.