Environment variables

Here are the environment variables CuPy uses.

CUDA_PATH

Path to the directory containing CUDA. The parent of the directory containing nvcc is used as default. When nvcc is not found, /usr/local/cuda is used. See Working with Custom CUDA Installation for details.

CUPY_CACHE_DIR

Path to the directory to store kernel cache. ${HOME}/.cupy/kernel_cache is used by default. See Overview for details.

CUPY_CACHE_SAVE_CUDA_SOURCE

If set to 1, CUDA source file will be saved along with compiled binary in the cache directory for debug purpose. It is disabled by default. Note: source file will not be saved if the compiled binary is already stored in the cache.

CUPY_CACHE_IN_MEMORY

If set to 1, CUPY_CACHE_DIR (and its default) and CUPY_CACHE_SAVE_CUDA_SOURCE will be ignored, and the cache is in memory. This env var allows reducing disk I/O, but is ignoed when nvcc is set to be the compiler backend.

CUPY_DUMP_CUDA_SOURCE_ON_ERROR

If set to 1, when CUDA kernel compilation fails, CuPy dumps CUDA kernel code to standard error. It is disabled by default.

CUPY_CUDA_COMPILE_WITH_DEBUG

If set to 1, CUDA kernel will be compiled with debug information (--device-debug and --generate-line-info). It is disabled by default.

CUPY_GPU_MEMORY_LIMIT

The amount of memory that can be allocated for each device. The value can be specified in absolute bytes or fraction (e.g., "90%") of the total memory of each GPU. See Memory Management for details. 0 (unlimited) is used by default.

CUPY_SEED

Set the seed for random number generators.

CUPY_EXPERIMENTAL_SLICE_COPY

If set to 1, the following syntax is enabled: cupy_ndarray[:] = numpy_ndarray.

CUPY_ACCELERATORS

A comma-separated string of backend names (cub or cutensor) which indicates the acceleration backends used in CuPy operations and its priority. Default is empty string (all accelerators are disabled).

CUPY_TF32

If set to 1, it allows CUDA libraries to use Tensor Cores TF32 compute for 32-bit floating point compute. The default is 0 and TF32 is not used.

NVCC

Define the compiler to use when compiling CUDA source. Note that most CuPy kernels are built with NVRTC; this environment is only effective for RawKernels/RawModules with nvcc backend or when using cub as the accelerator.

Moreover, as in any CUDA programs, all of the CUDA environment variables listed in the CUDA Toolkit Documentation will also be honored. When CUPY_ACCELERATORS or NVCC environment variables are set, g++-6 or later is required as the runtime host compiler. Please refer to Installing CuPy from Source for the details on how to install g++.

For installation

These environment variables are used during installation (building CuPy from source).

CUDA_PATH

See the description above.

CUTENSOR_PATH

Path to the cuTENSOR root directory that contains lib and include directories. (experimental)

NVCC

Define the compiler to use when compiling CUDA files.

CUPY_PYTHON_350_FORCE

Enforce CuPy to be installed against Python 3.5.0 (not recommended).

CUPY_INSTALL_USE_HIP

For building the ROCm support, see Building CuPy for ROCm for further detail.

CUPY_NVCC_GENERATE_CODE

To build CuPy for a particular CUDA architecture. For example, CUPY_NVCC_GENERATE_CODE="arch=compute_60,code=sm_60". For specifying multiple archs, concatenate the arch=... strings with semicolons (;). When this is not set, the default is to support all architectures.