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) &lt;- 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) &lt;- 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.
