The authors also discuss the need to verify the environment and enable WebGPU and WebGL support, as well as the importance of installing the correct GPU drivers. They share their investigation process and the issues they faced, including the underutilization of the GPU and the need for a solution that works with the new Headless Chrome. The article concludes by emphasizing the growth of Web AI and the importance of testing client-side, browser-based AI models in a true browser environment for consistent and reliable results.
Key takeaways:
- The article discusses the challenges and solutions in automating browser testing for a TensorFlow.js model that operates on both CPUs and GPUs, crucial for maintaining consistency in machine learning model performance.
- It details the use of a Linux-based Google Colab notebook, Chrome browser, and Puppeteer for automation. It also explains how to verify the environment and enable WebGPU and WebGL support.
- The article emphasizes the importance of installing the correct GPU drivers to ensure hardware acceleration and GPU detection, providing a step-by-step guide on how to do so.
- Finally, the article highlights the exponential growth of Web AI and the increasing need for testing client-side, browser-based AI models in a scalable, automatable, and standardized hardware setup.