Are there any specific dependencies needed for PyTorch Lightning to work with TPUs

1. PyTorch XLA: This library is crucial for interfacing PyTorch with TPUs. You need to install the PyTorch XLA wheel, which is typically done using commands like `!pip install cloud-tpu-client==0.10` followed by installing the appropriate PyTorch XLA wheel from Google's storage, such as `torch_xla-1.9-cp37-cp37m-linux_x86_64.whl`[3][5].

2. Cloud TPU Client: Installing `cloud-tpu-client==0.10` is necessary for interacting with TPUs in environments like Google Colab[3][7].

3. PyTorch and Torchvision: Ensure that PyTorch and Torchvision are installed and compatible with the TPU environment. This often involves reinstalling these packages after setting up the TPU environment[1][7].

4. PyTorch Lightning: Install PyTorch Lightning after setting up the TPU environment. Ensure that the version is compatible with your setup[1][7].

5. XRT TPU Device Configuration: For Cloud TPU VMs, you need to set the XRT TPU device configuration using commands like `export XRT_TPU_CONFIG="localservice;0;localhost:51011"`[5].

6. Distributed Training: PyTorch Lightning automatically handles distributed training on TPUs, so no additional configuration for distributed samplers is typically needed[5]. However, you must specify the number of TPU cores to use when initializing the `Trainer`, such as `num_tpu_cores=8` or `tpu_cores=8`[1][5].