Hello,
What workflow should one follow to optimize their Python code using TensorRT on the DRIVE AGX platform?
From what I have seen on numerous posts, there are no Python bindings/support for TensorRT on DRIVE AGX. How should we optimize the code then?
Hi raul_91,
Yes, officially TensorRT Python API isn’t supported on Drive platforms as description in the doc.
Could you convert your model into UFF/ONNX one offline, and then import and run it with C++ API on your Drive system?
BTW, Please see if “Optimizing DNN Inference Using CUDA and TensorRT on NVIDIA DRIVE AGX” webinar, mentioned in below links, is helpful.
https://news.developer.nvidia.com/how-drive-agx-cuda-and-tensorrt-achieve-fast-accurate-autonomous-vehicle-perception/
https://devtalk.nvidia.com/default/topic/1064456/general/nvidia-webinars-mdash-optimizing-dnn-inference-using-cuda-and-tensorrt-on-nvidia-drive-agx/
Thanks VickNV! I will look into converting my model into UFF/ONNX, and see if that works.
Is there an example of using a custom UFF/ONNX file with the C++ API on DRIVE AGX?
What did you mean “custom”? Could you check “TensorRT Samples Support Guide”?
I check the document and TensorRT Python supports Linux AArch64 (per Table 1).
Can you please explain what can go wrong if TensorRT Python API is executed on Drive platform?