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Fixes #155035 Pull Request resolved: https://github.com/pytorch/pytorch/pull/155523 Approved by: https://github.com/AlannaBurke, https://github.com/svekars
30 lines
1.3 KiB
Markdown
30 lines
1.3 KiB
Markdown
(torch_environment_variables)=
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# Torch Environment Variables
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PyTorch leverages environment variables for adjusting various settings that influence its runtime behavior.
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These variables offer control over key functionalities, such as displaying the C++ stack trace upon encountering errors, synchronizing the execution of CUDA kernels,
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specifying the number of threads for parallel processing tasks and many more.
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Moreover, PyTorch leverages several high-performance libraries, such as MKL and cuDNN,
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which also utilize environment variables to modify their functionality.
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This interplay of settings allows for a highly customizable development environment that can be
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optimized for efficiency, debugging, and computational resource management.
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Please note that while this documentation covers a broad spectrum of environment variables relevant to PyTorch and its associated libraries, it is not exhaustive.
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If you find anything in this documentation that is missing, incorrect, or could be improved, please let us know by filing an issue or opening a pull request.
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```{eval-rst}
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.. toctree::
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:maxdepth: 1
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threading_environment_variables
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cuda_environment_variables
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mps_environment_variables
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debugging_environment_variables
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miscellaneous_environment_variables
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logging
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torch_nccl_environment_variables
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```
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