Warning: expandable_segments not supported on this platform

[TristonC]

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Run the example in the examples/multigpu/graphbolt folder. It warns exapnadable_segments not supported.

python node_classification.py

/usr/local/lib/python3.10/dist-packages/dgl/graphbolt/__init__.py:114: GBWarning: 
An experimental feature for CUDA allocations is turned on for better allocation
pattern resulting in better memory usage for minibatch GNN training workloads.
See https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf,
and set the environment variable `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:False`
if you want to disable it and set it True to acknowledge and disable the warning.

  gb_warning(WARNING_STR_TO_BE_SHOWN)
Training with 1 gpus.
The dataset is already preprocessed.
/usr/local/lib/python3.10/dist-packages/dgl/graphbolt/impl/ondisk_dataset.py:855: 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.
  return torch.load(graph_topology.path)
[W905 21:06:05.701783134 CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())

[Anopoke]

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