torch.utils.cpp_extension.load(name, sources, extra_cflags=None, extra_cuda_cflags=None, extra_ldflags=None, extra_include_paths=None, build_directory=None, verbose=False, with_cuda=None, is_python_module=True, is_standalone=False, keep_intermediates=True) [source]
 
Loads a PyTorch C++ extension just-in-time (JIT). To load an extension, a Ninja build file is emitted, which is used to compile the given sources into a dynamic library. This library is subsequently loaded into the current Python process as a module and returned from this function, ready for use. By default, the directory to which the build file is emitted and the resulting library compiled to is <tmp>/torch_extensions/<name>, where <tmp> is the temporary folder on the current platform and <name> the name of the extension. This location can be overridden in two ways. First, if the TORCH_EXTENSIONS_DIR environment variable is set, it replaces <tmp>/torch_extensions and all extensions will be compiled into subfolders of this directory. Second, if the build_directory argument to this function is supplied, it overrides the entire path, i.e. the library will be compiled into that folder directly. To compile the sources, the default system compiler (c++) is used, which can be overridden by setting the CXX environment variable. To pass additional arguments to the compilation process, extra_cflags or extra_ldflags can be provided. For example, to compile your extension with optimizations, pass extra_cflags=['-O3']. You can also use extra_cflags to pass further include directories. CUDA support with mixed compilation is provided. Simply pass CUDA source files (.cu or .cuh) along with other sources. Such files will be detected and compiled with nvcc rather than the C++ compiler. This includes passing the CUDA lib64 directory as a library directory, and linking cudart. You can pass additional flags to nvcc via extra_cuda_cflags, just like with extra_cflags for C++. Various heuristics for finding the CUDA install directory are used, which usually work fine. If not, setting the CUDA_HOME environment variable is the safest option.  Parameters 
 
name – The name of the extension to build. This MUST be the same as the name of the pybind11 module! 
sources – A list of relative or absolute paths to C++ source files. 
extra_cflags – optional list of compiler flags to forward to the build. 
extra_cuda_cflags – optional list of compiler flags to forward to nvcc when building CUDA sources. 
extra_ldflags – optional list of linker flags to forward to the build. 
extra_include_paths – optional list of include directories to forward to the build. 
build_directory – optional path to use as build workspace. 
verbose – If True, turns on verbose logging of load steps. 
with_cuda – Determines whether CUDA headers and libraries are added to the build. If set to None (default), this value is automatically determined based on the existence of .cu or .cuh in sources. Set it to True` to force CUDA headers and libraries to be included. 
is_python_module – If True (default), imports the produced shared library as a Python module. If False, behavior depends on is_standalone. 
is_standalone – If False (default) loads the constructed extension into the process as a plain dynamic library. If True, build a standalone executable.   Returns 
Returns the loaded PyTorch extension as a Python module.  
If is_python_module is False and is_standalone is False: 

Returns nothing. (The shared library is loaded into the process as a side effect.)  
If is_standalone is True. 

Return the path to the executable. (On Windows, TORCH_LIB_PATH is added to the PATH environment variable as a side effect.)    Return type 
If is_python_module is True   Example >>> from torch.utils.cpp_extension import load
>>> module = load(
        name='extension',
        sources=['extension.cpp', 'extension_kernel.cu'],
        extra_cflags=['-O2'],
        verbose=True)