I noticed TensorRT is used for prediction only, but is there a way to train a model efficiently on AGX Xavier?
Use case: suppose you have a network of robots. Each robot has two major states - work and stand by. In the work mode - the robot harvests (predicts and collects) data, and in the stand by mode - the robot doesn’t use any resources, which offers a great opportunity to analyze the collected data locally. Obviously, shipping the data centrally is not very efficient especially when it comes to huge amounts of data and large network of robots, but transferring knowledge is. Is there a way to utilize the GPU on AGX Xavier efficiently in order to optimize a model parameters?