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PyTorch | loss.backward() -> Missing XLA configuration

The loss is calculated from the target model created using pytorch (not TensorFlow) and when propagating, I run the code below and had trouble with the following error message.
loss.backward()
(Forward propagation can be calculated without problems.)
terminate called after throwing an instance of 'std::runtime_error'
what(): tensorflow/compiler/xla/xla_client/computation_client.cc:280 : Missing XLA configuration
Aborted
-pytorch(1.12.0+cu102)
torchvision(0.13.0+cu102) <- target model contains pre-trained CNN model which can be installed from torchvision.models
google-compute-engine
GPU (NVIDIA Tesla T4 x 1, 11.6) <- The code worked in the environment where GPU (11.2) was installed, but it does not work in the current environment. / In the current environment, the same error occurs even if the GPU is not used and the CPU is used.
TPU is not installed (I don't want to use TPU, but GPU)
The code is working locally and was also working on other GPU environments as mentioned above. It stopped working when the environment was updated.
Please help me···

I solved this problem with the command.
$ pip uninstall torch_xla
This error seemed to be caused by pytorch-ignite and torch_xla.

CPU usage is more than GPU usage on Keras, Tensorflow

I wanted to teach an image classification CNN, and use Keras for it.
The image dimensions are 300x300x3.
I have trained a CNN with 2M parameters, I used MobileNet of Keras for transfer learning, however I freeze last 63 layers and add dense layers at the bottom, the last layer has 2 unit and Softmax activation.
To make predictions, I load the h5 file and use OpenCV video capture to get video frames, for each frame I use model.predict(img_array).
When i look to the Task Manager of Windows 10 , I see that the Python script uses %80 of my processor but %2 of GPU. This CPU usage causes Lags on my laptop.
How can I reduce the CPU usage and force Keras to make computations with GPU?
I have Nvidia Rtx 2060 4GB and Intel Core i7-9750H on my laptop.
Tensorflow 2.1 and Keras 2.3.1
OpenCV 4.1
I have tried, but actually nothing changes.
tf.config.threading.set_inter_op_parallelism_threads(12)
tf.config.threading.set_intra_op_parallelism_threads(12)
with tf.device(\gpu:0):
model.predict(img_array)
Best regards.
Edit:
I reduce the CPU usage to %20 with declaring steps parameter in the predict method.

Please check your pip list or conda list.
Sometimes, we mistakenly install both tensorflow and tensorflow-gpu.
If you have both, the system will automatically go for tensorflow, which is the CPU one.
If that is the case, DELETE "tensorflow", keeping only "tensorflow-gpu".
If you do not see tensorflow-gpu in the first place, try installing it on conda using the following commands:
conda create -n [EnvironmentName] python=3.6
conda activate [EnvironmentName]
conda install -c conda-forge tensorflow-gpu==1.14
it will assess which version (CUDA,CUDNN, etc.) you require and download and install it directly to your environment. Then run your python file from this environment. Good luck ^_^

Is R Keras using GPU based on this output?

I have installed Keras with gpu support in R based on Tensorflow with gpu support. This is installed with these steps:
https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781
If I run the Bosting housing example code from the book Deep learning with R, I receive this screen:
Can I conclude that the code runs on the GPU?
Or is this line from the picture above giving an error:
GPU libraries are statically linked, skip dlopen check.
During running the code the GPU is running only on 3% of capacity while the CPU is running on 20-25%.
The code is NOT running faster than while I initially did run the code without installing GPU support.
Thank you!

Yes, tensorflow is running with GPU enabled. Boston Housing is a relatively small dataset and probably does not benefit from using the GPU to a large degree. The lines below indicate it is running on the GPU. "Created tensorflow device (/job:localhost/replica:0/task:0device:GPU:0".
From the guide at Tensorflow
You can set tf.debugging.set_log_device_placement(True) in order to explicitly see where each operation is running. THE R equivalent is below.
library(tensorflow)
tf$debugging$set_log_device_placement(TRUE)

How can I use GPU for running a tflite model (*.tflite) using tf.lite.Interpreter (in python)?

I have converted a tensorflow inference graph to tflite model file (*.tflite), according to instructions from https://www.tensorflow.org/lite/convert.
I tested the tflite model on my GPU server, which has 4 Nvidia TITAN GPUs. I used the tf.lite.Interpreter to load and run tflite model file.
It works as the former tensorflow graph, however, the problem is that the inference became too slow. When I checked out the reason, I found that the GPU utilization is simply 0% when tf.lite.Interpreter is running.
Is there any method that I can run tf.lite.Interpreter with GPU support?

https://github.com/tensorflow/tensorflow/issues/34536
CPU is kind of good enough for tflite, especially multicore.
nvidia GPU likely not updated for tflite, which is for mobile GPU platform.

Conspiracy: they (TF-NVIDIA) hand-shake to not let TFlite working on GPU ? oo easy to make one.
Steve

Run Tensorflow on Jupyter notebook but kernel dead

I want to train a 5 Layer DNN using Tensorflow on Jupyter Notebook. It perform well on normal training.
But when I want to use Cross validation to find a great dropout rate. When training process, Jupyter say the kernel is dead.
The Jupyter log:
terminate called after throwing an instance of 'std::system_error'
what(): Resource temporarily unavailable
My code is here.
I Google find out maybe it's because run out of memory. I try to reduce batch size and the error still occurred.
The code running on Ubuntu 16.04 and 32GB RAM with GPU 1080Ti. Enviroment are Python(3.5), tensorflow (1.3.0) & tensorflow-gpu (1.3.0).