TVM aims to optimize deep models pretrained by any ML upstream frameworks. But as a popular compiler, users have many additional works to make one given model to work over TVM. For example:
So, we were thinking what if we can make TVM as a backend acceleration to those ML framework? When users still use consistent development and deployment with ML upstream frameworks but they can enjoy the best TVM performance. With this goal we are building an automated ML Infer Booster that just tricks the ML framework into thinking it still takes over everything, but essentially, the underlying TVM backend helps boost this ML inference automatically.