use problem decomposition to identify all errors :  (base) ubuntu@ubuntu-virtual-machine:~/DECA$ python demos/demo_reconstruct.py -i TestSamples/examples --saveDepth True --saveObj True
/home/ubuntu/mambaforge/lib/python3.12/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
/home/ubuntu/mambaforge/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
  warnings.warn(
/home/ubuntu/mambaforge/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
  warnings.warn(msg)
creating the FLAME Decoder
trained model found. load /home/ubuntu/DECA/data/deca_model.tar
/home/ubuntu/DECA/decalib/deca.py:89: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
Traceback (most recent call last):
  File "/home/ubuntu/DECA/demos/demo_reconstruct.py", line 131, in <module>
    main(parser.parse_args())
  File "/home/ubuntu/DECA/demos/demo_reconstruct.py", line 46, in main
    deca = DECA(config = deca_cfg, device=device)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/DECA/decalib/deca.py", line 50, in __init__
    self._setup_renderer(self.cfg.model)
  File "/home/ubuntu/DECA/decalib/deca.py", line 53, in _setup_renderer
    set_rasterizer(self.cfg.rasterizer_type)
  File "/home/ubuntu/DECA/decalib/utils/renderer.py", line 39, in set_rasterizer
    load(name='standard_rasterize_cuda', 
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/mambaforge/lib/python3.12/site-packages/torch/utils/cpp_extension.py", line 1312, in load
    return _jit_compile(
           ^^^^^^^^^^^^^
  File "/home/ubuntu/mambaforge/lib/python3.12/site-packages/torch/utils/cpp_extension.py", line 1722, in _jit_compile
    _write_ninja_file_and_build_library(
  File "/home/ubuntu/mambaforge/lib/python3.12/site-packages/torch/utils/cpp_extension.py", line 1811, in _write_ninja_file_and_build_library
    extra_ldflags = _prepare_ldflags(
                    ^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/mambaforge/lib/python3.12/site-packages/torch/utils/cpp_extension.py", line 1900, in _prepare_ldflags
    if (not os.path.exists(_join_cuda_home(extra_lib_dir)) and
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/mambaforge/lib/python3.12/site-packages/torch/utils/cpp_extension.py", line 2416, in _join_cuda_home
    raise OSError('CUDA_HOME environment variable is not set. '
OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
(base) ubuntu@ubuntu-virtual-machine:~/DECA$