| .. _torch_environment_variables: |
| |
| Torch Environment Variables |
| =============================== |
| |
| PyTorch leverages environment variables for adjusting various settings that influence its runtime behavior. |
| These variables offer control over key functionalities, such as displaying the C++ stack trace upon encountering errors, synchronizing the execution of CUDA kernels, |
| specifying the number of threads for parallel processing tasks and many more. |
| |
| Moreover, PyTorch leverages several high-performance libraries, such as MKL and cuDNN, |
| which also utilize environment variables to modify their functionality. |
| This interplay of settings allows for a highly customizable development environment that can be |
| optimized for efficiency, debugging, and computational resource management. |
| |
| Please note that while this documentation covers a broad spectrum of environment variables relevant to PyTorch and its associated libraries, it is not exhaustive. |
| 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. |
| |
| |
| .. toctree:: |
| :maxdepth: 1 |
| |
| threading_environment_variables |
| cuda_environment_variables |
| mps_environment_variables |
| debugging_environment_variables |
| miscellaneous_environment_variables |
| logging |
| torch_nccl_environment_variables |
