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| from __future__ import print_function | |
| # Unlike the rest of the PyTorch this file must be python2 compliant. | |
| # This script outputs relevant system environment info | |
| # Run it with `python collect_env.py`. | |
| import datetime | |
| import locale | |
| import re | |
| import subprocess | |
| import sys | |
| import os | |
| from collections import namedtuple | |
| try: | |
| import torch | |
| TORCH_AVAILABLE = True | |
| except (ImportError, NameError, AttributeError, OSError): | |
| TORCH_AVAILABLE = False | |
| # System Environment Information | |
| SystemEnv = namedtuple('SystemEnv', [ | |
| 'torch_version', | |
| 'is_debug_build', | |
| 'cuda_compiled_version', | |
| 'gcc_version', | |
| 'clang_version', | |
| 'cmake_version', | |
| 'os', | |
| 'libc_version', | |
| 'python_version', | |
| 'python_platform', | |
| 'is_cuda_available', | |
| 'cuda_runtime_version', | |
| 'nvidia_driver_version', | |
| 'nvidia_gpu_models', | |
| 'cudnn_version', | |
| 'pip_version', # 'pip' or 'pip3' | |
| 'pip_packages', | |
| 'conda_packages', | |
| 'hip_compiled_version', | |
| 'hip_runtime_version', | |
| 'miopen_runtime_version', | |
| 'caching_allocator_config', | |
| 'is_xnnpack_available', | |
| ]) | |
| def run(command): | |
| """Returns (return-code, stdout, stderr)""" | |
| p = subprocess.Popen(command, stdout=subprocess.PIPE, | |
| stderr=subprocess.PIPE, shell=True) | |
| raw_output, raw_err = p.communicate() | |
| rc = p.returncode | |
| if get_platform() == 'win32': | |
| enc = 'oem' | |
| else: | |
| enc = locale.getpreferredencoding() | |
| output = raw_output.decode(enc) | |
| err = raw_err.decode(enc) | |
| return rc, output.strip(), err.strip() | |
| def run_and_read_all(run_lambda, command): | |
| """Runs command using run_lambda; reads and returns entire output if rc is 0""" | |
| rc, out, _ = run_lambda(command) | |
| if rc != 0: | |
| return None | |
| return out | |
| def run_and_parse_first_match(run_lambda, command, regex): | |
| """Runs command using run_lambda, returns the first regex match if it exists""" | |
| rc, out, _ = run_lambda(command) | |
| if rc != 0: | |
| return None | |
| match = re.search(regex, out) | |
| if match is None: | |
| return None | |
| return match.group(1) | |
| def run_and_return_first_line(run_lambda, command): | |
| """Runs command using run_lambda and returns first line if output is not empty""" | |
| rc, out, _ = run_lambda(command) | |
| if rc != 0: | |
| return None | |
| return out.split('\n')[0] | |
| def get_conda_packages(run_lambda): | |
| conda = os.environ.get('CONDA_EXE', 'conda') | |
| out = run_and_read_all(run_lambda, "{} list".format(conda)) | |
| if out is None: | |
| return out | |
| return "\n".join( | |
| line | |
| for line in out.splitlines() | |
| if not line.startswith("#") | |
| and any( | |
| name in line | |
| for name in { | |
| "torch", | |
| "numpy", | |
| "cudatoolkit", | |
| "soumith", | |
| "mkl", | |
| "magma", | |
| "mkl", | |
| } | |
| ) | |
| ) | |
| def get_gcc_version(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)') | |
| def get_clang_version(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)') | |
| def get_cmake_version(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)') | |
| def get_nvidia_driver_version(run_lambda): | |
| if get_platform() == 'darwin': | |
| cmd = 'kextstat | grep -i cuda' | |
| return run_and_parse_first_match(run_lambda, cmd, | |
| r'com[.]nvidia[.]CUDA [(](.*?)[)]') | |
| smi = get_nvidia_smi() | |
| return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ') | |
| def get_gpu_info(run_lambda): | |
| if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip') and torch.version.hip is not None): | |
| if TORCH_AVAILABLE and torch.cuda.is_available(): | |
| return torch.cuda.get_device_name(None) | |
| return None | |
| smi = get_nvidia_smi() | |
| uuid_regex = re.compile(r' \(UUID: .+?\)') | |
| rc, out, _ = run_lambda(smi + ' -L') | |
| if rc != 0: | |
| return None | |
| # Anonymize GPUs by removing their UUID | |
| return re.sub(uuid_regex, '', out) | |
| def get_running_cuda_version(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)') | |
| def get_cudnn_version(run_lambda): | |
| """This will return a list of libcudnn.so; it's hard to tell which one is being used""" | |
| if get_platform() == 'win32': | |
| system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows') | |
| cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%") | |
| where_cmd = os.path.join(system_root, 'System32', 'where') | |
| cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path) | |
| elif get_platform() == 'darwin': | |
| # CUDA libraries and drivers can be found in /usr/local/cuda/. See | |
| # https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install | |
| # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac | |
| # Use CUDNN_LIBRARY when cudnn library is installed elsewhere. | |
| cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*' | |
| else: | |
| cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev' | |
| rc, out, _ = run_lambda(cudnn_cmd) | |
| # find will return 1 if there are permission errors or if not found | |
| if len(out) == 0 or (rc != 1 and rc != 0): | |
| l = os.environ.get('CUDNN_LIBRARY') | |
| if l is not None and os.path.isfile(l): | |
| return os.path.realpath(l) | |
| return None | |
| files_set = set() | |
| for fn in out.split('\n'): | |
| fn = os.path.realpath(fn) # eliminate symbolic links | |
| if os.path.isfile(fn): | |
| files_set.add(fn) | |
| if not files_set: | |
| return None | |
| # Alphabetize the result because the order is non-deterministic otherwise | |
| files = list(sorted(files_set)) | |
| if len(files) == 1: | |
| return files[0] | |
| result = '\n'.join(files) | |
| return 'Probably one of the following:\n{}'.format(result) | |
| def get_nvidia_smi(): | |
| # Note: nvidia-smi is currently available only on Windows and Linux | |
| smi = 'nvidia-smi' | |
| if get_platform() == 'win32': | |
| system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows') | |
| program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files') | |
| legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi) | |
| new_path = os.path.join(system_root, 'System32', smi) | |
| smis = [new_path, legacy_path] | |
| for candidate_smi in smis: | |
| if os.path.exists(candidate_smi): | |
| smi = '"{}"'.format(candidate_smi) | |
| break | |
| return smi | |
| def get_platform(): | |
| if sys.platform.startswith('linux'): | |
| return 'linux' | |
| elif sys.platform.startswith('win32'): | |
| return 'win32' | |
| elif sys.platform.startswith('cygwin'): | |
| return 'cygwin' | |
| elif sys.platform.startswith('darwin'): | |
| return 'darwin' | |
| else: | |
| return sys.platform | |
| def get_mac_version(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)') | |
| def get_windows_version(run_lambda): | |
| system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows') | |
| wmic_cmd = os.path.join(system_root, 'System32', 'Wbem', 'wmic') | |
| findstr_cmd = os.path.join(system_root, 'System32', 'findstr') | |
| return run_and_read_all(run_lambda, '{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd)) | |
| def get_lsb_version(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)') | |
| def check_release_file(run_lambda): | |
| return run_and_parse_first_match(run_lambda, 'cat /etc/*-release', | |
| r'PRETTY_NAME="(.*)"') | |
| def get_os(run_lambda): | |
| from platform import machine | |
| platform = get_platform() | |
| if platform == 'win32' or platform == 'cygwin': | |
| return get_windows_version(run_lambda) | |
| if platform == 'darwin': | |
| version = get_mac_version(run_lambda) | |
| if version is None: | |
| return None | |
| return 'macOS {} ({})'.format(version, machine()) | |
| if platform == 'linux': | |
| # Ubuntu/Debian based | |
| desc = get_lsb_version(run_lambda) | |
| if desc is not None: | |
| return '{} ({})'.format(desc, machine()) | |
| # Try reading /etc/*-release | |
| desc = check_release_file(run_lambda) | |
| if desc is not None: | |
| return '{} ({})'.format(desc, machine()) | |
| return '{} ({})'.format(platform, machine()) | |
| # Unknown platform | |
| return platform | |
| def get_python_platform(): | |
| import platform | |
| return platform.platform() | |
| def get_libc_version(): | |
| import platform | |
| if get_platform() != 'linux': | |
| return 'N/A' | |
| return '-'.join(platform.libc_ver()) | |
| def get_pip_packages(run_lambda): | |
| """Returns `pip list` output. Note: will also find conda-installed pytorch | |
| and numpy packages.""" | |
| # People generally have `pip` as `pip` or `pip3` | |
| # But here it is incoved as `python -mpip` | |
| def run_with_pip(pip): | |
| out = run_and_read_all(run_lambda, "{} list --format=freeze".format(pip)) | |
| return "\n".join( | |
| line | |
| for line in out.splitlines() | |
| if any( | |
| name in line | |
| for name in { | |
| "torch", | |
| "numpy", | |
| "mypy", | |
| } | |
| ) | |
| ) | |
| pip_version = 'pip3' if sys.version[0] == '3' else 'pip' | |
| out = run_with_pip(sys.executable + ' -mpip') | |
| return pip_version, out | |
| def get_cachingallocator_config(): | |
| ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '') | |
| return ca_config | |
| def is_xnnpack_available(): | |
| if TORCH_AVAILABLE: | |
| import torch.backends.xnnpack | |
| return str(torch.backends.xnnpack.enabled) # type: ignore[attr-defined] | |
| else: | |
| return "N/A" | |
| def get_env_info(): | |
| run_lambda = run | |
| pip_version, pip_list_output = get_pip_packages(run_lambda) | |
| if TORCH_AVAILABLE: | |
| version_str = torch.__version__ | |
| debug_mode_str = str(torch.version.debug) | |
| cuda_available_str = str(torch.cuda.is_available()) | |
| cuda_version_str = torch.version.cuda | |
| if not hasattr(torch.version, 'hip') or torch.version.hip is None: # cuda version | |
| hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A' | |
| else: # HIP version | |
| cfg = torch._C._show_config().split('\n') | |
| hip_runtime_version = [s.rsplit(None, 1)[-1] for s in cfg if 'HIP Runtime' in s][0] | |
| miopen_runtime_version = [s.rsplit(None, 1)[-1] for s in cfg if 'MIOpen' in s][0] | |
| cuda_version_str = 'N/A' | |
| hip_compiled_version = torch.version.hip | |
| else: | |
| version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A' | |
| hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A' | |
| sys_version = sys.version.replace("\n", " ") | |
| return SystemEnv( | |
| torch_version=version_str, | |
| is_debug_build=debug_mode_str, | |
| python_version='{} ({}-bit runtime)'.format(sys_version, sys.maxsize.bit_length() + 1), | |
| python_platform=get_python_platform(), | |
| is_cuda_available=cuda_available_str, | |
| cuda_compiled_version=cuda_version_str, | |
| cuda_runtime_version=get_running_cuda_version(run_lambda), | |
| nvidia_gpu_models=get_gpu_info(run_lambda), | |
| nvidia_driver_version=get_nvidia_driver_version(run_lambda), | |
| cudnn_version=get_cudnn_version(run_lambda), | |
| hip_compiled_version=hip_compiled_version, | |
| hip_runtime_version=hip_runtime_version, | |
| miopen_runtime_version=miopen_runtime_version, | |
| pip_version=pip_version, | |
| pip_packages=pip_list_output, | |
| conda_packages=get_conda_packages(run_lambda), | |
| os=get_os(run_lambda), | |
| libc_version=get_libc_version(), | |
| gcc_version=get_gcc_version(run_lambda), | |
| clang_version=get_clang_version(run_lambda), | |
| cmake_version=get_cmake_version(run_lambda), | |
| caching_allocator_config=get_cachingallocator_config(), | |
| is_xnnpack_available=is_xnnpack_available(), | |
| ) | |
| env_info_fmt = """ | |
| PyTorch version: {torch_version} | |
| Is debug build: {is_debug_build} | |
| CUDA used to build PyTorch: {cuda_compiled_version} | |
| ROCM used to build PyTorch: {hip_compiled_version} | |
| OS: {os} | |
| GCC version: {gcc_version} | |
| Clang version: {clang_version} | |
| CMake version: {cmake_version} | |
| Libc version: {libc_version} | |
| Python version: {python_version} | |
| Python platform: {python_platform} | |
| Is CUDA available: {is_cuda_available} | |
| CUDA runtime version: {cuda_runtime_version} | |
| GPU models and configuration: {nvidia_gpu_models} | |
| Nvidia driver version: {nvidia_driver_version} | |
| cuDNN version: {cudnn_version} | |
| HIP runtime version: {hip_runtime_version} | |
| MIOpen runtime version: {miopen_runtime_version} | |
| Is XNNPACK available: {is_xnnpack_available} | |
| Versions of relevant libraries: | |
| {pip_packages} | |
| {conda_packages} | |
| """.strip() | |
| def pretty_str(envinfo): | |
| def replace_nones(dct, replacement='Could not collect'): | |
| for key in dct.keys(): | |
| if dct[key] is not None: | |
| continue | |
| dct[key] = replacement | |
| return dct | |
| def replace_bools(dct, true='Yes', false='No'): | |
| for key in dct.keys(): | |
| if dct[key] is True: | |
| dct[key] = true | |
| elif dct[key] is False: | |
| dct[key] = false | |
| return dct | |
| def prepend(text, tag='[prepend]'): | |
| lines = text.split('\n') | |
| updated_lines = [tag + line for line in lines] | |
| return '\n'.join(updated_lines) | |
| def replace_if_empty(text, replacement='No relevant packages'): | |
| if text is not None and len(text) == 0: | |
| return replacement | |
| return text | |
| def maybe_start_on_next_line(string): | |
| # If `string` is multiline, prepend a \n to it. | |
| if string is not None and len(string.split('\n')) > 1: | |
| return '\n{}\n'.format(string) | |
| return string | |
| mutable_dict = envinfo._asdict() | |
| # If nvidia_gpu_models is multiline, start on the next line | |
| mutable_dict['nvidia_gpu_models'] = \ | |
| maybe_start_on_next_line(envinfo.nvidia_gpu_models) | |
| # If the machine doesn't have CUDA, report some fields as 'No CUDA' | |
| dynamic_cuda_fields = [ | |
| 'cuda_runtime_version', | |
| 'nvidia_gpu_models', | |
| 'nvidia_driver_version', | |
| ] | |
| all_cuda_fields = dynamic_cuda_fields + ['cudnn_version'] | |
| all_dynamic_cuda_fields_missing = all( | |
| mutable_dict[field] is None for field in dynamic_cuda_fields) | |
| if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing: | |
| for field in all_cuda_fields: | |
| mutable_dict[field] = 'No CUDA' | |
| if envinfo.cuda_compiled_version is None: | |
| mutable_dict['cuda_compiled_version'] = 'None' | |
| # Replace True with Yes, False with No | |
| mutable_dict = replace_bools(mutable_dict) | |
| # Replace all None objects with 'Could not collect' | |
| mutable_dict = replace_nones(mutable_dict) | |
| # If either of these are '', replace with 'No relevant packages' | |
| mutable_dict['pip_packages'] = replace_if_empty(mutable_dict['pip_packages']) | |
| mutable_dict['conda_packages'] = replace_if_empty(mutable_dict['conda_packages']) | |
| # Tag conda and pip packages with a prefix | |
| # If they were previously None, they'll show up as ie '[conda] Could not collect' | |
| if mutable_dict['pip_packages']: | |
| mutable_dict['pip_packages'] = prepend(mutable_dict['pip_packages'], | |
| '[{}] '.format(envinfo.pip_version)) | |
| if mutable_dict['conda_packages']: | |
| mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'], | |
| '[conda] ') | |
| return env_info_fmt.format(**mutable_dict) | |
| def get_pretty_env_info(): | |
| return pretty_str(get_env_info()) | |
| def main(): | |
| print("Collecting environment information...") | |
| output = get_pretty_env_info() | |
| print(output) | |
| if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(torch.utils, '_crash_handler'): | |
| minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR | |
| if sys.platform == "linux" and os.path.exists(minidump_dir): | |
| dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)] | |
| latest = max(dumps, key=os.path.getctime) | |
| ctime = os.path.getctime(latest) | |
| creation_time = datetime.datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S') | |
| msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \ | |
| "if this is related to your bug please include it when you file a report ***" | |
| print(msg, file=sys.stderr) | |
| if __name__ == '__main__': | |
| main() | |