How can I install PyTorch/XLA on Python 3.9

1. Ensure Python 3.9 is Supported: PyTorch/XLA supports Python versions up to 3.11, so Python 3.9 should work fine[3][7].

2. Install PyTorch: First, install PyTorch using pip. You can install a version compatible with Python 3.9:

bash
   pip install torch~=2.6.0 torchvision
   

3. Install PyTorch/XLA: Next, install PyTorch/XLA. If you are using TPUs, you can install the TPU-specific version:

bash
   pip install 'torch_xla[tpu]~=2.6.0' \
   -f https://storage.googleapis.com/libtpu-releases/index.html \
   -f https://storage.googleapis.com/libtpu-wheels/index.html
   

If you encounter issues finding the correct version, ensure that your pip is up-to-date and that you have correctly specified the version and index URLs.

4. Optional: Install Pallas Dependencies: If you need custom kernels, you might also want to install Pallas dependencies:

bash
   pip install 'torch_xla[pallas]' \
   -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html \
   -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
   

5. Verify Installation: After installation, verify that PyTorch/XLA is working by creating an XLA tensor:

python
   import torch
   import torch_xla
   import torch_xla.core.xla_model as xm

   t = torch.randn(2, 2, device=xm.xla_device())
   print(t.device)
   print(t)
   

This should help you set up PyTorch/XLA on Python 3.9 for use with TPUs or other XLA devices.