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I see, thanks. In my day job I develop hardware accurate simulations of a deep learning accelerator. This involves looking at a Spice model, simplifying it into
by p1esk 7y ago
I see, thanks. In my day job I develop hardware accurate simulations of a deep learning accelerator. This involves looking at a Spice model, simplifying it into some set of abstractions using Numpy, then accelerating this Numpy code using GPUs. Currently I'm porting a resnet-50 model from Numpy to Pytorch, and the next step is to speed up the Pytorch code (because right now I get ~1 image per second, which is about 10 times better than Numpy). Perhaps I should look into porting the model from Pytorch to TensorRT.
- m0zg 7y agoIf you're working with pytorch, porting basically means export to ONNX. Sometimes you'll run into an op that doesn't work with ONNX, but there are a lot fewer of those in TRT7. Unfortunately I have to work with TRT6, so I have to use PyTorch 1.2 and be "creative" to work around TRT6 bugs. That said, it could very well be painless for you. No reason not to try. Just export the model, and benchmark it with `trtexec`, in both fp32 and fp16. An hour of work at most.