There are some good points that are mentioned above - having a set of good getting started guides based on different usecases would certainly be a good starting point.
One of the things that I found hard to get started with was using tvm as a user was the absence of canned frontends as at the end of the build process one just gets a set of libraries. It is then that one goes and looks at the tutorials for checking that things work just fine - further figuring out the exact version of tensorflow that works with the current trunk of tvm took a bit of time as one cannot expect tensorflow==2.0.0-alpha to work out of the box. Thus follows the question of whether we should also be documenting the versions of the various frameworks that work with TVM for a given version of TVM . I also think that it is worth also creating an issues page which details what is expected in a bug report and how a user should report a bug / what's the minimal set of things we require to see in a bug report so that someone else can reproduce it in order to drive a higher quality of bug reports over time for the community. A link to the ci system from the main website seems due. I stumbled across ci.tvm.ai by accident but it would be good to have some of this handled from the top level. And thanks for reading till here. regards Ramana -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/2469#issuecomment-490847904
