Jetson Xavier NX running with Cuda and TF1.5

[AastaLLL]

Hi,

1. Although Max-N only enable two CPU, it do have higher clock rate.
If your task is GPU-intensive, it should be able to give a better performance.

2. Swap memory tends to be slower.
Would you mind to try this comment to control the memory usage of TensorFlow:

Resource Exhausted Error

Hi, Could you try to set some limitation on the TensorFlow memory usage to see if helps first? 1. Limit the maximal memory available for TensorFlow gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) 2. Allow gradually memory allocation config = tf.ConfigProto() config.gpu_options.allow_growth=True sess = tf.Session(config=config) Thanks.

3. Sorry that my comment might cause some confusing.
The way you used is called TF-TRT, which embeds TensorRT into the TensorFlow frameworks.
This approach consumes lots of memory since you need to enable TensorRT as well as TensorFlow.

It’s more recommended to use our pure TensorRT API. You can find some example here:
https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/sampleUffSSD

4. YES.

Thanks.