# -*- Python -*- """Repository rule for CUDA autoconfiguration. `cuda_configure` depends on the following environment variables: * `TF_NEED_CUDA`: Whether to enable building with CUDA. * `GCC_HOST_COMPILER_PATH`: The GCC host compiler path * `TF_CUDA_CLANG`: Whether to use clang as a cuda compiler. * `CLANG_CUDA_COMPILER_PATH`: The clang compiler path that will be used for both host and device code compilation if TF_CUDA_CLANG is 1. * `TF_DOWNLOAD_CLANG`: Whether to download a recent release of clang compiler and use it to build tensorflow. When this option is set CLANG_CUDA_COMPILER_PATH is ignored. * `CUDA_TOOLKIT_PATH`: The path to the CUDA toolkit. Default is `/usr/local/cuda`. * `TF_CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then use the system default. * `TF_CUDNN_VERSION`: The version of the cuDNN library. * `CUDNN_INSTALL_PATH`: The path to the cuDNN library. Default is `/usr/local/cuda`. * `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is `3.5,5.2`. * `PYTHON_BIN_PATH`: The python binary path """ _GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH" _CLANG_CUDA_COMPILER_PATH = "CLANG_CUDA_COMPILER_PATH" _CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH" _TF_CUDA_VERSION = "TF_CUDA_VERSION" _TF_CUDNN_VERSION = "TF_CUDNN_VERSION" _CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH" _TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES" _TF_CUDA_CONFIG_REPO = "TF_CUDA_CONFIG_REPO" _TF_DOWNLOAD_CLANG = "TF_DOWNLOAD_CLANG" _PYTHON_BIN_PATH = "PYTHON_BIN_PATH" _DEFAULT_CUDA_VERSION = "" _DEFAULT_CUDNN_VERSION = "" _DEFAULT_CUDA_TOOLKIT_PATH = "/usr/local/cuda" _DEFAULT_CUDNN_INSTALL_PATH = "/usr/local/cuda" _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"] # Lookup paths for CUDA / cuDNN libraries, relative to the install directories. # # Paths will be tried out in the order listed below. The first successful path # will be used. For example, when looking for the cudart libraries, the first # attempt will be lib64/cudart inside the CUDA toolkit. CUDA_LIB_PATHS = [ "lib64/", "lib64/stubs/", "lib/powerpc64le-linux-gnu/", "lib/x86_64-linux-gnu/", "lib/x64/", "lib/", "", ] # Lookup paths for cupti.h, relative to the CUDA toolkit directory. # # On most systems, the cupti library is not installed in the same directory as # the other CUDA libraries but rather in a special extras/CUPTI directory. CUPTI_HEADER_PATHS = [ "extras/CUPTI/include/", "include/cuda/CUPTI/", "include/", ] # Lookup paths for the cupti library, relative to the # # On most systems, the cupti library is not installed in the same directory as # the other CUDA libraries but rather in a special extras/CUPTI directory. CUPTI_LIB_PATHS = [ "extras/CUPTI/lib64/", "lib/powerpc64le-linux-gnu/", "lib/x86_64-linux-gnu/", "lib64/", "extras/CUPTI/libx64/", "extras/CUPTI/lib/", "lib/", ] # Lookup paths for CUDA headers (cuda.h) relative to the CUDA toolkit directory. CUDA_INCLUDE_PATHS = [ "include/", "include/cuda/", ] # Lookup paths for cudnn.h relative to the CUDNN install directory. CUDNN_INCLUDE_PATHS = [ "", "include/", "include/cuda/", ] # Lookup paths for NVVM libdevice relative to the CUDA directory toolkit. # # libdevice implements mathematical functions for GPU kernels, and is provided # in NVVM bitcode (a subset of LLVM bitcode). NVVM_LIBDEVICE_PATHS = [ "nvvm/libdevice/", "share/cuda/", "lib/nvidia-cuda-toolkit/libdevice/", ] # Files used to detect the NVVM libdevice path. NVVM_LIBDEVICE_FILES = [ # CUDA 9.0 has a single file. "libdevice.10.bc", # CUDA 8.0 has separate files for compute versions 2.0, 3.0, 3.5 and 5.0. # Probing for one of them is sufficient. "libdevice.compute_20.10.bc", ] load("//third_party/clang_toolchain:download_clang.bzl", "download_clang") load( "@bazel_tools//tools/cpp:lib_cc_configure.bzl", "escape_string", "get_env_var", ) load( "@bazel_tools//tools/cpp:windows_cc_configure.bzl", "find_msvc_tool", "find_vc_path", "setup_vc_env_vars", ) def _get_python_bin(repository_ctx): """Gets the python bin path.""" python_bin = repository_ctx.os.environ.get(_PYTHON_BIN_PATH) if python_bin != None: return python_bin python_bin_name = "python.exe" if _is_windows(repository_ctx) else "python" python_bin_path = repository_ctx.which(python_bin_name) if python_bin_path != None: return str(python_bin_path) auto_configure_fail( "Cannot find python in PATH, please make sure " + "python is installed and add its directory in PATH, or --define " + "%s='/something/else'.\nPATH=%s" % ( _PYTHON_BIN_PATH, repository_ctx.os.environ.get("PATH", ""), )) def _get_nvcc_tmp_dir_for_windows(repository_ctx): """Return the tmp directory for nvcc to generate intermediate source files.""" escaped_tmp_dir = escape_string( get_env_var(repository_ctx, "TMP", "C:\\Windows\\Temp").replace( "\\", "\\\\"),) return escaped_tmp_dir + "\\\\nvcc_inter_files_tmp_dir" def _get_msvc_compiler(repository_ctx): vc_path = find_vc_path(repository_ctx) return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\\", "/") def _get_win_cuda_defines(repository_ctx): """Return CROSSTOOL defines for Windows""" # If we are not on Windows, return empty vaules for Windows specific fields. # This ensures the CROSSTOOL file parser is happy. if not _is_windows(repository_ctx): return { "%{msvc_env_tmp}": "", "%{msvc_env_path}": "", "%{msvc_env_include}": "", "%{msvc_env_lib}": "", "%{msvc_cl_path}": "", "%{msvc_ml_path}": "", "%{msvc_link_path}": "", "%{msvc_lib_path}": "", "%{cxx_builtin_include_directory}": "", } vc_path = find_vc_path(repository_ctx) if not vc_path: auto_configure_fail( "Visual C++ build tools not found on your machine." + "Please check your installation following https://docs.bazel.build/versions/master/windows.html#using" ) return {} env = setup_vc_env_vars(repository_ctx, vc_path) escaped_paths = escape_string(env["PATH"]) escaped_include_paths = escape_string(env["INCLUDE"]) escaped_lib_paths = escape_string(env["LIB"]) escaped_tmp_dir = escape_string( get_env_var(repository_ctx, "TMP", "C:\\Windows\\Temp").replace( "\\", "\\\\"),) msvc_cl_path = "windows/msvc_wrapper_for_nvcc.bat" msvc_ml_path = find_msvc_tool(repository_ctx, vc_path, "ml64.exe").replace( "\\", "/") msvc_link_path = find_msvc_tool(repository_ctx, vc_path, "link.exe").replace( "\\", "/") msvc_lib_path = find_msvc_tool(repository_ctx, vc_path, "lib.exe").replace( "\\", "/") # nvcc will generate some temporary source files under %{nvcc_tmp_dir} # The generated files are guranteed to have unique name, so they can share the same tmp directory escaped_cxx_include_directories = [ "cxx_builtin_include_directory: \"%s\"" % _get_nvcc_tmp_dir_for_windows(repository_ctx) ] for path in escaped_include_paths.split(";"): if path: escaped_cxx_include_directories.append( "cxx_builtin_include_directory: \"%s\"" % path) return { "%{msvc_env_tmp}": escaped_tmp_dir, "%{msvc_env_path}": escaped_paths, "%{msvc_env_include}": escaped_include_paths, "%{msvc_env_lib}": escaped_lib_paths, "%{msvc_cl_path}": msvc_cl_path, "%{msvc_ml_path}": msvc_ml_path, "%{msvc_link_path}": msvc_link_path, "%{msvc_lib_path}": msvc_lib_path, "%{cxx_builtin_include_directory}": "\n".join(escaped_cxx_include_directories), } # TODO(dzc): Once these functions have been factored out of Bazel's # cc_configure.bzl, load them from @bazel_tools instead. # BEGIN cc_configure common functions. def find_cc(repository_ctx): """Find the C++ compiler.""" if _is_windows(repository_ctx): return _get_msvc_compiler(repository_ctx) if _use_cuda_clang(repository_ctx): target_cc_name = "clang" cc_path_envvar = _CLANG_CUDA_COMPILER_PATH if _flag_enabled(repository_ctx, _TF_DOWNLOAD_CLANG): return "extra_tools/bin/clang" else: target_cc_name = "gcc" cc_path_envvar = _GCC_HOST_COMPILER_PATH cc_name = target_cc_name if cc_path_envvar in repository_ctx.os.environ: cc_name_from_env = repository_ctx.os.environ[cc_path_envvar].strip() if cc_name_from_env: cc_name = cc_name_from_env if cc_name.startswith("/"): # Absolute path, maybe we should make this supported by our which function. return cc_name cc = repository_ctx.which(cc_name) if cc == None: fail(("Cannot find {}, either correct your path or set the {}" + " environment variable").format(target_cc_name, cc_path_envvar)) return cc _INC_DIR_MARKER_BEGIN = "#include <...>" # OSX add " (framework directory)" at the end of line, strip it. _OSX_FRAMEWORK_SUFFIX = " (framework directory)" _OSX_FRAMEWORK_SUFFIX_LEN = len(_OSX_FRAMEWORK_SUFFIX) def _cxx_inc_convert(path): """Convert path returned by cc -E xc++ in a complete path.""" path = path.strip() if path.endswith(_OSX_FRAMEWORK_SUFFIX): path = path[:-_OSX_FRAMEWORK_SUFFIX_LEN].strip() return path def _normalize_include_path(repository_ctx, path): """Normalizes include paths before writing them to the crosstool. If path points inside the 'crosstool' folder of the repository, a relative path is returned. If path points outside the 'crosstool' folder, an absolute path is returned. """ path = str(repository_ctx.path(path)) crosstool_folder = str(repository_ctx.path(".").get_child("crosstool")) if path.startswith(crosstool_folder): # We drop the path to "$REPO/crosstool" and a trailing path separator. return path[len(crosstool_folder) + 1:] return path def _get_cxx_inc_directories_impl(repository_ctx, cc, lang_is_cpp): """Compute the list of default C or C++ include directories.""" if lang_is_cpp: lang = "c++" else: lang = "c" result = repository_ctx.execute([cc, "-E", "-x" + lang, "-", "-v"]) index1 = result.stderr.find(_INC_DIR_MARKER_BEGIN) if index1 == -1: return [] index1 = result.stderr.find("\n", index1) if index1 == -1: return [] index2 = result.stderr.rfind("\n ") if index2 == -1 or index2 < index1: return [] index2 = result.stderr.find("\n", index2 + 1) if index2 == -1: inc_dirs = result.stderr[index1 + 1:] else: inc_dirs = result.stderr[index1 + 1:index2].strip() return [ _normalize_include_path(repository_ctx, _cxx_inc_convert(p)) for p in inc_dirs.split("\n") ] def get_cxx_inc_directories(repository_ctx, cc): """Compute the list of default C and C++ include directories.""" # For some reason `clang -xc` sometimes returns include paths that are # different from the ones from `clang -xc++`. (Symlink and a dir) # So we run the compiler with both `-xc` and `-xc++` and merge resulting lists includes_cpp = _get_cxx_inc_directories_impl(repository_ctx, cc, True) includes_c = _get_cxx_inc_directories_impl(repository_ctx, cc, False) includes_cpp_set = depset(includes_cpp) return includes_cpp + [ inc for inc in includes_c if inc not in includes_cpp_set ] def auto_configure_fail(msg): """Output failure message when cuda configuration fails.""" red = "\033[0;31m" no_color = "\033[0m" fail("\n%sCuda Configuration Error:%s %s\n" % (red, no_color, msg)) # END cc_configure common functions (see TODO above). def _host_compiler_includes(repository_ctx, cc): """Generates the cxx_builtin_include_directory entries for gcc inc dirs. Args: repository_ctx: The repository context. cc: The path to the gcc host compiler. Returns: A string containing the cxx_builtin_include_directory for each of the gcc host compiler include directories, which can be added to the CROSSTOOL file. """ inc_dirs = get_cxx_inc_directories(repository_ctx, cc) inc_entries = [] for inc_dir in inc_dirs: inc_entries.append(" cxx_builtin_include_directory: \"%s\"" % inc_dir) return "\n".join(inc_entries) def _cuda_include_path(repository_ctx, cuda_config): """Generates the cxx_builtin_include_directory entries for cuda inc dirs. Args: repository_ctx: The repository context. cc: The path to the gcc host compiler. Returns: A string containing the cxx_builtin_include_directory for each of the gcc host compiler include directories, which can be added to the CROSSTOOL file. """ nvcc_path = repository_ctx.path("%s/bin/nvcc%s" % ( cuda_config.cuda_toolkit_path, ".exe" if cuda_config.cpu_value == "Windows" else "", )) result = repository_ctx.execute([ nvcc_path, "-v", "/dev/null", "-o", "/dev/null", ]) target_dir = "" for one_line in result.stderr.splitlines(): if one_line.startswith("#$ _TARGET_DIR_="): target_dir = ( cuda_config.cuda_toolkit_path + "/" + one_line.replace( "#$ _TARGET_DIR_=", "") + "/include") inc_entries = [] if target_dir != "": inc_entries.append(" cxx_builtin_include_directory: \"%s\"" % target_dir) default_include = cuda_config.cuda_toolkit_path + "/include" inc_entries.append( " cxx_builtin_include_directory: \"%s\"" % default_include) return "\n".join(inc_entries) def _enable_cuda(repository_ctx): if "TF_NEED_CUDA" in repository_ctx.os.environ: enable_cuda = repository_ctx.os.environ["TF_NEED_CUDA"].strip() return enable_cuda == "1" return False def cuda_toolkit_path(repository_ctx): """Finds the cuda toolkit directory. Args: repository_ctx: The repository context. Returns: A speculative real path of the cuda toolkit install directory. """ cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ: cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip() if not repository_ctx.path(cuda_toolkit_path).exists: auto_configure_fail("Cannot find cuda toolkit path.") return str(repository_ctx.path(cuda_toolkit_path).realpath) def _cudnn_install_basedir(repository_ctx): """Finds the cudnn install directory.""" cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH if _CUDNN_INSTALL_PATH in repository_ctx.os.environ: cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip() if not repository_ctx.path(cudnn_install_path).exists: auto_configure_fail("Cannot find cudnn install path.") return cudnn_install_path def matches_version(environ_version, detected_version): """Checks whether the user-specified version matches the detected version. This function performs a weak matching so that if the user specifies only the major or major and minor versions, the versions are still considered matching if the version parts match. To illustrate: environ_version detected_version result ----------------------------------------- 5.1.3 5.1.3 True 5.1 5.1.3 True 5 5.1 True 5.1.3 5.1 False 5.2.3 5.1.3 False Args: environ_version: The version specified by the user via environment variables. detected_version: The version autodetected from the CUDA installation on the system. Returns: True if user-specified version matches detected version and False otherwise. """ environ_version_parts = environ_version.split(".") detected_version_parts = detected_version.split(".") if len(detected_version_parts) < len(environ_version_parts): return False for i, part in enumerate(detected_version_parts): if i >= len(environ_version_parts): break if part != environ_version_parts[i]: return False return True _NVCC_VERSION_PREFIX = "Cuda compilation tools, release " def _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value): """Detects the version of CUDA installed on the system. Args: repository_ctx: The repository context. cuda_toolkit_path: The CUDA install directory. Returns: String containing the version of CUDA. """ # Run nvcc --version and find the line containing the CUDA version. nvcc_path = repository_ctx.path("%s/bin/nvcc%s" % ( cuda_toolkit_path, ".exe" if cpu_value == "Windows" else "", )) if not nvcc_path.exists: auto_configure_fail("Cannot find nvcc at %s" % str(nvcc_path)) result = repository_ctx.execute([str(nvcc_path), "--version"]) if result.stderr: auto_configure_fail("Error running nvcc --version: %s" % result.stderr) lines = result.stdout.splitlines() version_line = lines[len(lines) - 1] if version_line.find(_NVCC_VERSION_PREFIX) == -1: auto_configure_fail( "Could not parse CUDA version from nvcc --version. Got: %s" % result.stdout,) # Parse the CUDA version from the line containing the CUDA version. prefix_removed = version_line.replace(_NVCC_VERSION_PREFIX, "") parts = prefix_removed.split(",") if len(parts) != 2 or len(parts[0]) < 2: auto_configure_fail( "Could not parse CUDA version from nvcc --version. Got: %s" % result.stdout,) full_version = parts[1].strip() if full_version.startswith("V"): full_version = full_version[1:] # Check whether TF_CUDA_VERSION was set by the user and fail if it does not # match the detected version. environ_version = "" if _TF_CUDA_VERSION in repository_ctx.os.environ: environ_version = repository_ctx.os.environ[_TF_CUDA_VERSION].strip() if environ_version and not matches_version(environ_version, full_version): auto_configure_fail( ("CUDA version detected from nvcc (%s) does not match " + "TF_CUDA_VERSION (%s)") % (full_version, environ_version),) # We only use the version consisting of the major and minor version numbers. version_parts = full_version.split(".") if len(version_parts) < 2: auto_configure_fail("CUDA version detected from nvcc (%s) is incomplete.") if cpu_value == "Windows": version = "64_%s%s" % (version_parts[0], version_parts[1]) else: version = "%s.%s" % (version_parts[0], version_parts[1]) return version _DEFINE_CUDNN_MAJOR = "#define CUDNN_MAJOR" _DEFINE_CUDNN_MINOR = "#define CUDNN_MINOR" _DEFINE_CUDNN_PATCHLEVEL = "#define CUDNN_PATCHLEVEL" def find_cuda_define(repository_ctx, header_dir, header_file, define): """Returns the value of a #define in a header file. Greps through a header file and returns the value of the specified #define. If the #define is not found, then raise an error. Args: repository_ctx: The repository context. header_dir: The directory containing the header file. header_file: The header file name. define: The #define to search for. Returns: The value of the #define found in the header. """ # Confirm location of the header and grep for the line defining the macro. h_path = repository_ctx.path("%s/%s" % (header_dir, header_file)) if not h_path.exists: auto_configure_fail("Cannot find %s at %s" % (header_file, str(h_path))) result = repository_ctx.execute( # Grep one more lines as some #defines are splitted into two lines. ["grep", "--color=never", "-A1", "-E", define, str(h_path)],) if result.stderr: auto_configure_fail("Error reading %s: %s" % (str(h_path), result.stderr)) # Parse the version from the line defining the macro. if result.stdout.find(define) == -1: auto_configure_fail( "Cannot find line containing '%s' in %s" % (define, h_path)) # Split results to lines lines = result.stdout.split("\n") num_lines = len(lines) for l in range(num_lines): line = lines[l] if define in line: # Find the line with define version = line if l != num_lines - 1 and line[-1] == "\\": # Add next line, if multiline version = version[:-1] + lines[l + 1] break # Remove any comments version = version.split("//")[0] # Remove define name version = version.replace(define, "").strip() # Remove the code after the version number. version_end = version.find(" ") if version_end != -1: if version_end == 0: auto_configure_fail( "Cannot extract the version from line containing '%s' in %s" % (define, str(h_path)),) version = version[:version_end].strip() return version def _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value): """Detects the version of cuDNN installed on the system. Args: repository_ctx: The repository context. cpu_value: The name of the host operating system. cudnn_install_basedir: The cuDNN install directory. Returns: A string containing the version of cuDNN. """ cudnn_header_dir = _find_cudnn_header_dir( repository_ctx, cudnn_install_basedir, ) major_version = find_cuda_define( repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_MAJOR, ) minor_version = find_cuda_define( repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_MINOR, ) patch_version = find_cuda_define( repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_PATCHLEVEL, ) full_version = "%s.%s.%s" % (major_version, minor_version, patch_version) # Check whether TF_CUDNN_VERSION was set by the user and fail if it does not # match the detected version. environ_version = "" if _TF_CUDNN_VERSION in repository_ctx.os.environ: environ_version = repository_ctx.os.environ[_TF_CUDNN_VERSION].strip() if environ_version and not matches_version(environ_version, full_version): cudnn_h_path = repository_ctx.path( "%s/include/cudnn.h" % cudnn_install_basedir) auto_configure_fail(("cuDNN version detected from %s (%s) does not match " + "TF_CUDNN_VERSION (%s)") % (str(cudnn_h_path), full_version, environ_version),) # We only use the major version since we use the libcudnn libraries that are # only versioned with the major version (e.g. libcudnn.so.5). version = major_version if cpu_value == "Windows": version = "64_" + version return version def compute_capabilities(repository_ctx): """Returns a list of strings representing cuda compute capabilities.""" if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ: return _DEFAULT_CUDA_COMPUTE_CAPABILITIES capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES] capabilities = capabilities_str.split(",") for capability in capabilities: # Workaround for Skylark's lack of support for regex. This check should # be equivalent to checking: # if re.match("[0-9]+.[0-9]+", capability) == None: parts = capability.split(".") if len(parts) != 2 or not parts[0].isdigit() or not parts[1].isdigit(): auto_configure_fail("Invalid compute capability: %s" % capability) return capabilities def get_cpu_value(repository_ctx): """Returns the name of the host operating system. Args: repository_ctx: The repository context. Returns: A string containing the name of the host operating system. """ os_name = repository_ctx.os.name.lower() if os_name.startswith("mac os"): return "Darwin" if os_name.find("windows") != -1: return "Windows" result = repository_ctx.execute(["uname", "-s"]) return result.stdout.strip() def _is_windows(repository_ctx): """Returns true if the host operating system is windows.""" return get_cpu_value(repository_ctx) == "Windows" def _lib_name(lib, cpu_value, version = "", static = False): """Constructs the platform-specific name of a library. Args: lib: The name of the library, such as "cudart" cpu_value: The name of the host operating system. version: The version of the library. static: True the library is static or False if it is a shared object. Returns: The platform-specific name of the library. """ if cpu_value in ("Linux", "FreeBSD"): if static: return "lib%s.a" % lib else: if version: version = ".%s" % version return "lib%s.so%s" % (lib, version) elif cpu_value == "Windows": return "%s.lib" % lib elif cpu_value == "Darwin": if static: return "lib%s.a" % lib elif version: version = ".%s" % version return "lib%s%s.dylib" % (lib, version) else: auto_configure_fail("Invalid cpu_value: %s" % cpu_value) def _find_cuda_lib( lib, repository_ctx, cpu_value, basedir, version = "", static = False): """Finds the given CUDA or cuDNN library on the system. Args: lib: The name of the library, such as "cudart" repository_ctx: The repository context. cpu_value: The name of the host operating system. basedir: The install directory of CUDA or cuDNN. version: The version of the library. static: True if static library, False if shared object. Returns: Returns a struct with the following fields: file_name: The basename of the library found on the system. path: The full path to the library. """ file_name = _lib_name(lib, cpu_value, version, static) for relative_path in CUDA_LIB_PATHS: path = repository_ctx.path("%s/%s%s" % (basedir, relative_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) auto_configure_fail("Cannot find cuda library %s" % file_name) def _find_cupti_header_dir(repository_ctx, cuda_config): """Returns the path to the directory containing cupti.h On most systems, the cupti library is not installed in the same directory as the other CUDA libraries but rather in a special extras/CUPTI directory. Args: repository_ctx: The repository context. cuda_config: The CUDA config as returned by _get_cuda_config Returns: The path of the directory containing the cupti header. """ cuda_toolkit_path = cuda_config.cuda_toolkit_path for relative_path in CUPTI_HEADER_PATHS: if repository_ctx.path( "%s/%scupti.h" % (cuda_toolkit_path, relative_path)).exists: return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] auto_configure_fail("Cannot find cupti.h under %s" % ", ".join( [cuda_toolkit_path + "/" + s for s in CUPTI_HEADER_PATHS])) def _find_cupti_lib(repository_ctx, cuda_config): """Finds the cupti library on the system. On most systems, the cupti library is not installed in the same directory as the other CUDA libraries but rather in a special extras/CUPTI directory. Args: repository_ctx: The repository context. cuda_config: The cuda configuration as returned by _get_cuda_config. Returns: Returns a struct with the following fields: file_name: The basename of the library found on the system. path: The full path to the library. """ file_name = _lib_name( "cupti", cuda_config.cpu_value, cuda_config.cuda_version, ) cuda_toolkit_path = cuda_config.cuda_toolkit_path for relative_path in CUPTI_LIB_PATHS: path = repository_ctx.path( "%s/%s%s" % (cuda_toolkit_path, relative_path, file_name),) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) auto_configure_fail("Cannot find cupti library %s" % file_name) def _find_libs(repository_ctx, cuda_config): """Returns the CUDA and cuDNN libraries on the system. Args: repository_ctx: The repository context. cuda_config: The CUDA config as returned by _get_cuda_config Returns: Map of library names to structs of filename and path. """ cpu_value = cuda_config.cpu_value return { "cuda": _find_cuda_lib("cuda", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path), "cudart": _find_cuda_lib( "cudart", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, ), "cudart_static": _find_cuda_lib( "cudart_static", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, static=True, ), "cublas": _find_cuda_lib( "cublas", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, ), "cusolver": _find_cuda_lib( "cusolver", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, ), "curand": _find_cuda_lib( "curand", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, ), "cufft": _find_cuda_lib( "cufft", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, ), "cudnn": _find_cuda_lib( "cudnn", repository_ctx, cpu_value, cuda_config.cudnn_install_basedir, cuda_config.cudnn_version, ), "cupti": _find_cupti_lib(repository_ctx, cuda_config), } def _find_cuda_include_path(repository_ctx, cuda_config): """Returns the path to the directory containing cuda.h Args: repository_ctx: The repository context. cuda_config: The CUDA config as returned by _get_cuda_config Returns: The path of the directory containing the CUDA headers. """ cuda_toolkit_path = cuda_config.cuda_toolkit_path for relative_path in CUDA_INCLUDE_PATHS: if repository_ctx.path( "%s/%scuda.h" % (cuda_toolkit_path, relative_path)).exists: return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] auto_configure_fail("Cannot find cuda.h under %s" % cuda_toolkit_path) def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir): """Returns the path to the directory containing cudnn.h Args: repository_ctx: The repository context. cudnn_install_basedir: The cudnn install directory as returned by _cudnn_install_basedir. Returns: The path of the directory containing the cudnn header. """ for relative_path in CUDA_INCLUDE_PATHS: if repository_ctx.path( "%s/%scudnn.h" % (cudnn_install_basedir, relative_path)).exists: return ("%s/%s" % (cudnn_install_basedir, relative_path))[:-1] if repository_ctx.path("/usr/include/cudnn.h").exists: return "/usr/include" auto_configure_fail("Cannot find cudnn.h under %s" % cudnn_install_basedir) def _find_nvvm_libdevice_dir(repository_ctx, cuda_config): """Returns the path to the directory containing libdevice in bitcode format. Args: repository_ctx: The repository context. cuda_config: The CUDA config as returned by _get_cuda_config Returns: The path of the directory containing the CUDA headers. """ cuda_toolkit_path = cuda_config.cuda_toolkit_path for libdevice_file in NVVM_LIBDEVICE_FILES: for relative_path in NVVM_LIBDEVICE_PATHS: if repository_ctx.path("%s/%s%s" % (cuda_toolkit_path, relative_path, libdevice_file)).exists: return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] auto_configure_fail( "Cannot find libdevice*.bc files under %s" % cuda_toolkit_path) def _cudart_static_linkopt(cpu_value): """Returns additional platform-specific linkopts for cudart.""" return "" if cpu_value == "Darwin" else "\"-lrt\"," def _get_cuda_config(repository_ctx): """Detects and returns information about the CUDA installation on the system. Args: repository_ctx: The repository context. Returns: A struct containing the following fields: cuda_toolkit_path: The CUDA toolkit installation directory. cudnn_install_basedir: The cuDNN installation directory. cuda_version: The version of CUDA on the system. cudnn_version: The version of cuDNN on the system. compute_capabilities: A list of the system's CUDA compute capabilities. cpu_value: The name of the host operating system. """ cpu_value = get_cpu_value(repository_ctx) toolkit_path = cuda_toolkit_path(repository_ctx) cuda_version = _cuda_version(repository_ctx, toolkit_path, cpu_value) cudnn_install_basedir = _cudnn_install_basedir(repository_ctx) cudnn_version = _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value) return struct( cuda_toolkit_path=toolkit_path, cudnn_install_basedir=cudnn_install_basedir, cuda_version=cuda_version, cudnn_version=cudnn_version, compute_capabilities=compute_capabilities(repository_ctx), cpu_value=cpu_value, ) def _tpl(repository_ctx, tpl, substitutions = {}, out = None): if not out: out = tpl.replace(":", "/") repository_ctx.template( out, Label("//third_party/gpus/%s.tpl" % tpl), substitutions, ) def _file(repository_ctx, label): repository_ctx.template( label.replace(":", "/"), Label("//third_party/gpus/%s.tpl" % label), {}, ) _DUMMY_CROSSTOOL_BZL_FILE = """ def error_gpu_disabled(): fail("ERROR: Building with --config=cuda but TensorFlow is not configured " + "to build with GPU support. Please re-run ./configure and enter 'Y' " + "at the prompt to build with GPU support.") native.genrule( name = "error_gen_crosstool", outs = ["CROSSTOOL"], cmd = "echo 'Should not be run.' && exit 1", ) native.filegroup( name = "crosstool", srcs = [":CROSSTOOL"], output_licenses = ["unencumbered"], ) """ _DUMMY_CROSSTOOL_BUILD_FILE = """ load("//crosstool:error_gpu_disabled.bzl", "error_gpu_disabled") error_gpu_disabled() """ def _create_dummy_repository(repository_ctx): cpu_value = get_cpu_value(repository_ctx) # Set up BUILD file for cuda/. _tpl( repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "False", "%{cuda_extra_copts}": "[]", }, ) _tpl( repository_ctx, "cuda:BUILD", { "%{cuda_driver_lib}": _lib_name("cuda", cpu_value), "%{cudart_static_lib}": _lib_name( "cudart_static", cpu_value, static=True, ), "%{cudart_static_linkopt}": _cudart_static_linkopt(cpu_value), "%{cudart_lib}": _lib_name("cudart", cpu_value), "%{cublas_lib}": _lib_name("cublas", cpu_value), "%{cusolver_lib}": _lib_name("cusolver", cpu_value), "%{cudnn_lib}": _lib_name("cudnn", cpu_value), "%{cufft_lib}": _lib_name("cufft", cpu_value), "%{curand_lib}": _lib_name("curand", cpu_value), "%{cupti_lib}": _lib_name("cupti", cpu_value), "%{cuda_include_genrules}": "", "%{cuda_headers}": "", }, ) # Create dummy files for the CUDA toolkit since they are still required by # tensorflow/core/platform/default/build_config:cuda. repository_ctx.file("cuda/cuda/include/cuda.h", "") repository_ctx.file("cuda/cuda/include/cublas.h", "") repository_ctx.file("cuda/cuda/include/cudnn.h", "") repository_ctx.file("cuda/cuda/extras/CUPTI/include/cupti.h", "") repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cuda", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart", cpu_value)) repository_ctx.file( "cuda/cuda/lib/%s" % _lib_name("cudart_static", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cublas", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cusolver", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudnn", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("curand", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cufft", cpu_value)) repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cupti", cpu_value)) # Set up cuda_config.h, which is used by # tensorflow/stream_executor/dso_loader.cc. _tpl( repository_ctx, "cuda:cuda_config.h", { "%{cuda_version}": _DEFAULT_CUDA_VERSION, "%{cudnn_version}": _DEFAULT_CUDNN_VERSION, "%{cuda_compute_capabilities}": ",".join([ "CudaVersion(\"%s\")" % c for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES ]), "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH, }, "cuda/cuda/cuda_config.h", ) # If cuda_configure is not configured to build with GPU support, and the user # attempts to build with --config=cuda, add a dummy build rule to intercept # this and fail with an actionable error message. repository_ctx.file( "crosstool/error_gpu_disabled.bzl", _DUMMY_CROSSTOOL_BZL_FILE, ) repository_ctx.file("crosstool/BUILD", _DUMMY_CROSSTOOL_BUILD_FILE) def _execute( repository_ctx, cmdline, error_msg = None, error_details = None, empty_stdout_fine = False): """Executes an arbitrary shell command. Args: repository_ctx: the repository_ctx object cmdline: list of strings, the command to execute error_msg: string, a summary of the error if the command fails error_details: string, details about the error or steps to fix it empty_stdout_fine: bool, if True, an empty stdout result is fine, otherwise it's an error Return: the result of repository_ctx.execute(cmdline) """ result = repository_ctx.execute(cmdline) if result.stderr or not (empty_stdout_fine or result.stdout): auto_configure_fail( "\n".join([ error_msg.strip() if error_msg else "Repository command failed", result.stderr.strip(), error_details if error_details else "", ]),) return result def _norm_path(path): """Returns a path with '/' and remove the trailing slash.""" path = path.replace("\\", "/") if path[-1] == "/": path = path[:-1] return path def symlink_genrule_for_dir( repository_ctx, src_dir, dest_dir, genrule_name, src_files = [], dest_files = []): """Returns a genrule to symlink(or copy if on Windows) a set of files. If src_dir is passed, files will be read from the given directory; otherwise we assume files are in src_files and dest_files """ if src_dir != None: src_dir = _norm_path(src_dir) dest_dir = _norm_path(dest_dir) files = "\n".join(sorted(_read_dir(repository_ctx, src_dir).splitlines())) # Create a list with the src_dir stripped to use for outputs. dest_files = files.replace(src_dir, "").splitlines() src_files = files.splitlines() command = [] if not _is_windows(repository_ctx): # We clear folders that might have been generated previously to avoid # undesired inclusions command.append('if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi') command.append('if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi') command.append('if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi') command.append('if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi') outs = [] for i in range(len(dest_files)): if dest_files[i] != "": # If we have only one file to link we do not want to use the dest_dir, as # $(@D) will include the full path to the file. dest = "$(@D)/" + dest_dir + dest_files[i] if len( dest_files) != 1 else "$(@D)/" + dest_files[i] # Copy the headers to create a sandboxable setup. cmd = "cp -f" command.append(cmd + ' "%s" "%s"' % (src_files[i], dest)) outs.append(' "' + dest_dir + dest_files[i] + '",') genrule = _genrule( src_dir, genrule_name, " && ".join(command), "\n".join(outs), ) return genrule def _genrule(src_dir, genrule_name, command, outs): """Returns a string with a genrule. Genrule executes the given command and produces the given outputs. """ return ( "genrule(\n" + ' name = "' + genrule_name + '",\n' + " outs = [\n" + outs + "\n ],\n" + ' cmd = """\n' + command + '\n """,\n' + ")\n") def _read_dir(repository_ctx, src_dir): """Returns a string with all files in a directory. Finds all files inside a directory, traversing subfolders and following symlinks. The returned string contains the full path of all files separated by line breaks. """ if _is_windows(repository_ctx): src_dir = src_dir.replace("/", "\\") find_result = _execute( repository_ctx, ["cmd.exe", "/c", "dir", src_dir, "/b", "/s", "/a-d"], empty_stdout_fine=True, ) # src_files will be used in genrule.outs where the paths must # use forward slashes. result = find_result.stdout.replace("\\", "/") else: find_result = _execute( repository_ctx, ["find", src_dir, "-follow", "-type", "f"], empty_stdout_fine=True, ) result = find_result.stdout return result def _flag_enabled(repository_ctx, flag_name): if flag_name in repository_ctx.os.environ: value = repository_ctx.os.environ[flag_name].strip() return value == "1" return False def _use_cuda_clang(repository_ctx): return _flag_enabled(repository_ctx, "TF_CUDA_CLANG") def _compute_cuda_extra_copts(repository_ctx, compute_capabilities): if _use_cuda_clang(repository_ctx): capability_flags = [ "--cuda-gpu-arch=sm_" + cap.replace(".", "") for cap in compute_capabilities ] else: # Capabilities are handled in the "crosstool_wrapper_driver_is_not_gcc" for nvcc # TODO(csigg): Make this consistent with cuda clang and pass to crosstool. capability_flags = [] return str(capability_flags) def _create_local_cuda_repository(repository_ctx): """Creates the repository containing files set up to build with CUDA.""" cuda_config = _get_cuda_config(repository_ctx) cuda_include_path = _find_cuda_include_path(repository_ctx, cuda_config) cudnn_header_dir = _find_cudnn_header_dir( repository_ctx, cuda_config.cudnn_install_basedir, ) cupti_header_dir = _find_cupti_header_dir(repository_ctx, cuda_config) nvvm_libdevice_dir = _find_nvvm_libdevice_dir(repository_ctx, cuda_config) # Set up symbolic links for the cuda toolkit by creating genrules to do # symlinking. We create one genrule for each directory we want to track under # cuda_toolkit_path cuda_toolkit_path = cuda_config.cuda_toolkit_path genrules = [ symlink_genrule_for_dir( repository_ctx, cuda_include_path, "cuda/include", "cuda-include", ) ] genrules.append( symlink_genrule_for_dir( repository_ctx, nvvm_libdevice_dir, "cuda/nvvm/libdevice", "cuda-nvvm", )) genrules.append( symlink_genrule_for_dir( repository_ctx, cupti_header_dir, "cuda/extras/CUPTI/include", "cuda-extras", )) cuda_libs = _find_libs(repository_ctx, cuda_config) cuda_lib_src = [] cuda_lib_dest = [] for lib in cuda_libs.values(): cuda_lib_src.append(lib.path) cuda_lib_dest.append("cuda/lib/" + lib.file_name) genrules.append( symlink_genrule_for_dir( repository_ctx, None, "", "cuda-lib", cuda_lib_src, cuda_lib_dest, )) # Set up the symbolic links for cudnn if cndnn was not installed to # CUDA_TOOLKIT_PATH. included_files = _read_dir(repository_ctx, cuda_include_path).replace( cuda_include_path, "", ).splitlines() if "/cudnn.h" not in included_files: genrules.append( symlink_genrule_for_dir( repository_ctx, None, "cuda/include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"], ["cudnn.h"], )) else: genrules.append( "filegroup(\n" + ' name = "cudnn-include",\n' + " srcs = [],\n" + ")\n",) # Set up BUILD file for cuda/ _tpl( repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "True", "%{cuda_extra_copts}": _compute_cuda_extra_copts( repository_ctx, cuda_config.compute_capabilities, ), }, ) _tpl( repository_ctx, "cuda:BUILD.windows" if _is_windows(repository_ctx) else "cuda:BUILD", { "%{cuda_driver_lib}": cuda_libs["cuda"].file_name, "%{cudart_static_lib}": cuda_libs["cudart_static"].file_name, "%{cudart_static_linkopt}": _cudart_static_linkopt(cuda_config.cpu_value,), "%{cudart_lib}": cuda_libs["cudart"].file_name, "%{cublas_lib}": cuda_libs["cublas"].file_name, "%{cusolver_lib}": cuda_libs["cusolver"].file_name, "%{cudnn_lib}": cuda_libs["cudnn"].file_name, "%{cufft_lib}": cuda_libs["cufft"].file_name, "%{curand_lib}": cuda_libs["curand"].file_name, "%{cupti_lib}": cuda_libs["cupti"].file_name, "%{cuda_include_genrules}": "\n".join(genrules), "%{cuda_headers}": ('":cuda-include",\n' + ' ":cudnn-include",' ), }, "cuda/BUILD", ) is_cuda_clang = _use_cuda_clang(repository_ctx) should_download_clang = is_cuda_clang and _flag_enabled( repository_ctx, _TF_DOWNLOAD_CLANG, ) if should_download_clang: download_clang(repository_ctx, "crosstool/extra_tools") # Set up crosstool/ cc = find_cc(repository_ctx) cc_fullpath = cc if not should_download_clang else "crosstool/" + cc host_compiler_includes = _host_compiler_includes(repository_ctx, cc_fullpath) cuda_defines = {} # Bazel sets '-B/usr/bin' flag to workaround build errors on RHEL (see # https://github.com/bazelbuild/bazel/issues/760). # However, this stops our custom clang toolchain from picking the provided # LLD linker, so we're only adding '-B/usr/bin' when using non-downloaded # toolchain. # TODO: when bazel stops adding '-B/usr/bin' by default, remove this # flag from the CROSSTOOL completely (see # https://github.com/bazelbuild/bazel/issues/5634) if should_download_clang: cuda_defines["%{linker_bin_path_flag}"] = "" else: cuda_defines["%{linker_bin_path_flag}"] = 'flag: "-B/usr/bin"' if is_cuda_clang: cuda_defines["%{host_compiler_path}"] = str(cc) cuda_defines["%{host_compiler_warnings}"] = """ # Some parts of the codebase set -Werror and hit this warning, so # switch it off for now. flag: "-Wno-invalid-partial-specialization" """ cuda_defines["%{host_compiler_includes}"] = host_compiler_includes _tpl(repository_ctx, "crosstool:BUILD", { "%{linker_files}": ":empty", "%{win_linker_files}": ":empty" }) repository_ctx.file( "crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc", "") repository_ctx.file("crosstool/windows/msvc_wrapper_for_nvcc.py", "") repository_ctx.file("crosstool/windows/msvc_wrapper_for_nvcc.bat", "") else: cuda_defines[ "%{host_compiler_path}"] = "clang/bin/crosstool_wrapper_driver_is_not_gcc" cuda_defines["%{host_compiler_warnings}"] = "" # nvcc has the system include paths built in and will automatically # search them; we cannot work around that, so we add the relevant cuda # system paths to the allowed compiler specific include paths. cuda_defines["%{host_compiler_includes}"] = ( host_compiler_includes + "\n" + _cuda_include_path( repository_ctx, cuda_config) + "\n cxx_builtin_include_directory: \"%s\"" % cupti_header_dir + "\n cxx_builtin_include_directory: \"%s\"" % cudnn_header_dir) nvcc_path = str( repository_ctx.path("%s/bin/nvcc%s" % ( cuda_config.cuda_toolkit_path, ".exe" if _is_windows(repository_ctx) else "", ))) _tpl( repository_ctx, "crosstool:BUILD", { "%{linker_files}": ":crosstool_wrapper_driver_is_not_gcc", "%{win_linker_files}": ":windows_msvc_wrapper_files", }, ) wrapper_defines = { "%{cpu_compiler}": str(cc), "%{cuda_version}": cuda_config.cuda_version, "%{nvcc_path}": nvcc_path, "%{gcc_host_compiler_path}": str(cc), "%{cuda_compute_capabilities}": ", ".join( ["\"%s\"" % c for c in cuda_config.compute_capabilities],), "%{nvcc_tmp_dir}": _get_nvcc_tmp_dir_for_windows(repository_ctx), } _tpl( repository_ctx, "crosstool:clang/bin/crosstool_wrapper_driver_is_not_gcc", wrapper_defines, ) _tpl( repository_ctx, "crosstool:windows/msvc_wrapper_for_nvcc.py", wrapper_defines, ) _tpl( repository_ctx, "crosstool:windows/msvc_wrapper_for_nvcc.bat", { "%{python_binary}": _get_python_bin(repository_ctx), }, ) _tpl( repository_ctx, "crosstool:CROSSTOOL", cuda_defines + _get_win_cuda_defines(repository_ctx), out="crosstool/CROSSTOOL", ) # Set up cuda_config.h, which is used by # tensorflow/stream_executor/dso_loader.cc. _tpl( repository_ctx, "cuda:cuda_config.h", { "%{cuda_version}": cuda_config.cuda_version, "%{cudnn_version}": cuda_config.cudnn_version, "%{cuda_compute_capabilities}": ",".join([ "CudaVersion(\"%s\")" % c for c in cuda_config.compute_capabilities ],), "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path, }, "cuda/cuda/cuda_config.h", ) def _create_remote_cuda_repository(repository_ctx, remote_config_repo): """Creates pointers to a remotely configured repo set up to build with CUDA.""" _tpl( repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "True", "%{cuda_extra_copts}": _compute_cuda_extra_copts( repository_ctx, compute_capabilities(repository_ctx), ), }, ) _tpl( repository_ctx, "cuda:remote.BUILD", { "%{remote_cuda_repo}": remote_config_repo, }, "cuda/BUILD", ) _tpl(repository_ctx, "crosstool:remote.BUILD", { "%{remote_cuda_repo}": remote_config_repo, }, "crosstool/BUILD") def _cuda_autoconf_impl(repository_ctx): """Implementation of the cuda_autoconf repository rule.""" if not _enable_cuda(repository_ctx): _create_dummy_repository(repository_ctx) elif _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ: _create_remote_cuda_repository( repository_ctx, repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO], ) else: _create_local_cuda_repository(repository_ctx) cuda_configure = repository_rule( implementation = _cuda_autoconf_impl, environ = [ _GCC_HOST_COMPILER_PATH, _CLANG_CUDA_COMPILER_PATH, "TF_NEED_CUDA", "TF_CUDA_CLANG", _TF_DOWNLOAD_CLANG, _CUDA_TOOLKIT_PATH, _CUDNN_INSTALL_PATH, _TF_CUDA_VERSION, _TF_CUDNN_VERSION, _TF_CUDA_COMPUTE_CAPABILITIES, _TF_CUDA_CONFIG_REPO, "NVVMIR_LIBRARY_DIR", _PYTHON_BIN_PATH, ], ) """Detects and configures the local CUDA toolchain. Add the following to your WORKSPACE FILE: ```python cuda_configure(name = "local_config_cuda") ``` Args: name: A unique name for this workspace rule. """