The subsequent trace can be either used to run the current PyTorch model more efficiently (by running optimization passes on it), or be converted to the ONNX format to be shipped to other frameworks such as Caffe2, MXNet, TensorFlow and others or directly to the hardware accelerated libraries like CoreML or TensorRT. Over the next year, you will hear more about the JIT compiler for performance improvements.
Users being funny :)
Our users express their support in funny ways, made us laugh, thanks for this :)
I've been using PyTorch a few months now and I've never felt better. I have more energy. My skin is clearer. My eye sight has improved.— Andrej Karpathy (@karpathy) May 26, 2017
