I installed the Jetson with Jetpack 3.2 (CUDA 9 and everything)
Than I installed Tensorflow 1.5 on it (build from source the same as the tutorials you find from JetsonHacks or Tensorflow it self)
Wheel files -> https://github.com/MatthiasRoelandts/tensorflow_jetson_tx2
Now I’m trying to do object detection with the ‘ssd_mobilenet_v1_coco’ network.
But the performance is very slow. On my host pc (no GPU) I get almost real-time performance. But on the Jetson TX2, it’s three times slower! (with ‘./sudo jetson_clock.sh’)
With ‘sudo ./tegrastats.sh’ I see that the GPU only runs at 6% during my program.
If I run a demo with TensorRT from Jetson-interference (https://github.com/dusty-nv/jetson-inference) the GPU is used 99% and this is also real-time object detection.
Unfortunately I can’t use TensorRT thanks to the fact that my network contains layers not yet supported by TensorRT.
Does anyone have any suggestions what I can do to improve the performance of my Tensorflow application?
- Is it possible that I left some kind of debug state on during the tensorflow installation?
- Is the loading of the images of the camera the bottle neck? (I already tried doing this threaded with no results)
- Am I missing a tensorflow setting to use the GPU better?