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# Summary The goal of this PR is to add a doc page to list a number of environment that effect the PyTorch runtime. It will likely not be exhaustive but hopefully will be added and updated to stay relevant. Pull Request resolved: https://github.com/pytorch/pytorch/pull/119087 Approved by: https://github.com/janeyx99, https://github.com/eqy
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1.3 KiB
ReStructuredText
27 lines
1.3 KiB
ReStructuredText
.. _torch_environment_variables:
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Torch Environment Variables
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===============================
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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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.. 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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debugging_environment_variables
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miscellaneous_environment_variables
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logging
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