With the generated gradient information, a differentiable physical simulator can make the convergence of the machine learning process one order of magnitude faster than gradient-free algorithms, such as model-free reinforcement learning.
As you may already know, ETH Zürich is a world-class university constantly ranked among the top 1 to 5 in Europe.
On a Sunday afternoon about a couple of months ago, when Ye and I were on our way back from a long week of travel, we decided to do something to relax on the train ( to kill time). Since we happened to mention Minecraft and MagicaVoxel, we decided to do a Hackathon, where we use Taichi Lang to create a GPU path tracing voxel renderer. Soon, before we were back home, we had our prototype:
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.
In the previous blog post, we mentioned this sentence, which is a part of the zen of Python. In this post, we will show you how we simplified the code of Taichi.
In this blog post I'll briefly talk about the data containers in Taichi and Torch. As you might have already known, both Taichi and Torch have a core concept of multi-dimensional array containers, called taichi.field and torch.Tensor respectively. They, as well as numpy.arrays, share a lot in common so users might think they're exactly the same. Therefore, we want to share a few interesting differences in this blog so that new users don't get confused by similar names or usages.
"What is the advantage of Taichi over Pytorch or Tensorflow when running on GPU?" Not surprisingly, this is one of the top questions we've received in the Taichi user community. In this blog series we will walk you through some major concepts and components in Taichi and Torch, elaborating on the analogies and differences which might not be obvious at the first sight.
Ever since the Python programming language was born, its core philosophy has always been to maximize the readability and simplicity of code. In fact, the reach for readability and simplicity is so deep within Python's root, that if you type import this in a Python console, it will recite a little poem: