Taichi & PyTorch 03: Accelerate PyTorch with Taichi - Data Preprocessing & High-performance ML Operator Customization
Our previous blogs (Taichi & PyTorch 01 and 02) pointed out that Taichi and Torch serve different application scenarios can they complement each other? And the answer is an unequivocal yes! In this blog, we will use two simple examples to explain how to use Taichi kernel to implement data preprocessing operators or custom ML operators. With Taichi, you can accelerate your ML model development with ease and get rid of the tedious low-level parallel programming (CUDA for example) for good.
Is Taichi Lang comparable to or even faster than CUDA?
In our recently published blog Is Taichi Lang able to make better use of the underlying hardware than other native, low-level programming languages? With this question in mind, we kick-started the benchmark project in an attempt to provide a comprehensive and accurate performance evaluation of Taichi Lang.
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