You may know that we are adding numpy-compitable operators to MXNet. We asked interns (most of them are undergraduate students) to implement these operators based on TVM. The feedback we got is that the documents on tvm.ai are not easy to follow for beginning developers.
About 2 months ago I started to write notebooks for these developers, by assuming them only know numpy before. Its format likes to other D2L project such as https://d2l.ai: every chapter is a runnable notebook that can be read in 10min, with all necessary background information. All these notebooks should be consistent and can be printed as a textbook.
My progress isn’t pretty fast since my time is quite limited, so far there are 17 notebooks on http://tvm.d2l.ai/. I expect the first release version should have at least 40 chapters. So it’s still a very early draft.
There aren’t new contents in the repo, most of them are already covered in tvm.ai or the topi folder. This project is more like a rewrite of these contents for beginning developers.
I’d like to hear your suggestions about this project, or it’s even better if some of you are interested to contribute.