What are the common issues when running PyTorch/XLA in a Docker container

1. Docker Container Compatibility: Users often encounter difficulties when trying to run PyTorch/XLA within Docker containers on TPU VMs. Even with prebuilt environments, errors may occur when starting XLA containers, especially with certain Docker images like `nightly_3.7nightly_3.7_20220624` or `r1.12_3.7_tpuvm_20220614`[1][3].

2. Missing XLA Configuration: A common error is the "Missing XLA configuration" when running PyTorch/XLA. This can be resolved by setting the `XRT_TPU_CONFIG` environment variable properly and ensuring that the TPU instance is started with the correct scopes[4].

3. Performance Issues: PyTorch/XLA can suffer from performance degradation due to factors like dynamic tensor shapes and operations that don't have native XLA translations. Frequent recompilations can slow down training, and certain operations may require transferring data to the CPU for evaluation[2].

4. GPU and CUDA Compatibility: When using PyTorch/XLA with GPUs, issues arise because PyTorch and PyTorch/XLA cannot both use CUDA simultaneously. This requires setting `USE_CUDA=0` and using specific configurations for GPU acceleration with XLA[5][7].

5. Visibility of Devices: In Docker containers, GPU devices may not be visible unless the container is restarted after installing the NVIDIA Container Toolkit[7]. Similarly, TPU devices require proper setup and configuration to be recognized within the container.