(_cuda_environment_variables)=

CUDA Environment Variables

For more information on CUDA runtime environment variables, see CUDA Environment Variables.

PyTorch Environment Variables

Variable	Description
PYTORCH_NO_CUDA_MEMORY_CACHING	If set to 1, disables caching of memory allocations in CUDA. This can be useful for debugging.
PYTORCH_ALLOC_CONF	For a more in depth explanation of this environment variable, see {ref}cuda-memory-management. PYTORCH_CUDA_ALLOC_CONF is its alias and is provided only for backward compatibility.
PYTORCH_NVML_BASED_CUDA_CHECK	If set to 1, before importing PyTorch modules that check if CUDA is available, PyTorch will use NVML to check if the CUDA driver is functional instead of using the CUDA runtime. This can be helpful if forked processes fail with a CUDA initialization error.
TORCH_CUDNN_V8_API_LRU_CACHE_LIMIT	The cache limit for the cuDNN v8 API. This is used to limit the memory used by the cuDNN v8 API. The default value is 10000, which roughly corresponds to 2GiB assuming 200KiB per ExecutionPlan. Set to 0 for no limit or a negative value for no caching.
TORCH_CUDNN_V8_API_DISABLED	If set to 1, disables the cuDNN v8 API. And will fall back to the cuDNN v7 API.
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE	If set to 1, forces TF32 enablement, overrides set_float32_matmul_precision setting.
TORCH_NCCL_USE_COMM_NONBLOCKING	If set to 1, enables non-blocking error handling in NCCL.
TORCH_NCCL_AVOID_RECORD_STREAMS	If set to 0, enables fallback to record streams-based synchronization behavior in NCCL.
TORCH_CUDNN_V8_API_DEBUG	If set to 1, sanity check whether cuDNN V8 is being used.

CUDA Runtime and Libraries Environment Variables

Variable	Description
CUDA_VISIBLE_DEVICES	Comma-separated list of GPU device IDs that should be made available to CUDA runtime. If set to -1, no GPUs are made available.
CUDA_LAUNCH_BLOCKING	If set to 1, makes CUDA calls synchronous. This can be useful for debugging.
CUBLAS_WORKSPACE_CONFIG	This environment variable is used to set the workspace configuration for cuBLAS per allocation. The format is :[SIZE]:[COUNT]. As an example, the default workspace size per allocation is CUBLAS_WORKSPACE_CONFIG=:4096:2:16:8 which specifies a total size of 2 * 4096 + 8 * 16 KiB. To force cuBLAS to avoid using workspaces, set CUBLAS_WORKSPACE_CONFIG=:0:0.
CUDNN_CONV_WSCAP_DBG	Similar to CUBLAS_WORKSPACE_CONFIG, this environment variable is used to set the workspace configuration for cuDNN per allocation.
CUBLASLT_WORKSPACE_SIZE	Similar to CUBLAS_WORKSPACE_CONFIG, this environment variable is used to set the workspace size for cuBLASLT.
CUDNN_ERRATA_JSON_FILE	Can be set to a file path for an errata filter that can be passed to cuDNN to avoid specific engine configs, used primarily for debugging or to hardcode autotuning.
NVIDIA_TF32_OVERRIDE	If set to 0, disables TF32 globally across all kernels, overriding all PyTorch settings.