# 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` or `python -m torch.utils.collect_env` 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', 'cuda_module_loading', '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', 'cpu_info', ]) DEFAULT_CONDA_PATTERNS = { "torch", "numpy", "cudatoolkit", "soumith", "mkl", "magma", "triton", "optree", } DEFAULT_PIP_PATTERNS = { "torch", "numpy", "mypy", "flake8", "triton", "optree", "onnx", } def run(command): """Return (return-code, stdout, stderr).""" shell = True if type(command) is str else False p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell) 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): """Run 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): """Run 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): """Run 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, patterns=None): if patterns is None: patterns = DEFAULT_CONDA_PATTERNS 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 patterns) ) 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) + \ (" ({})".format(torch.cuda.get_device_properties(0).gcnArchName) if torch.version.hip is not None else "") 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): """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 = 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 # example outputs of CPU infos # * linux # Architecture: x86_64 # CPU op-mode(s): 32-bit, 64-bit # Address sizes: 46 bits physical, 48 bits virtual # Byte Order: Little Endian # CPU(s): 128 # On-line CPU(s) list: 0-127 # Vendor ID: GenuineIntel # Model name: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz # CPU family: 6 # Model: 106 # Thread(s) per core: 2 # Core(s) per socket: 32 # Socket(s): 2 # Stepping: 6 # BogoMIPS: 5799.78 # Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr # sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl # xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 # pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand # hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced # fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap # avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 # xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq # avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities # Virtualization features: # Hypervisor vendor: KVM # Virtualization type: full # Caches (sum of all): # L1d: 3 MiB (64 instances) # L1i: 2 MiB (64 instances) # L2: 80 MiB (64 instances) # L3: 108 MiB (2 instances) # NUMA: # NUMA node(s): 2 # NUMA node0 CPU(s): 0-31,64-95 # NUMA node1 CPU(s): 32-63,96-127 # Vulnerabilities: # Itlb multihit: Not affected # L1tf: Not affected # Mds: Not affected # Meltdown: Not affected # Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown # Retbleed: Not affected # Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp # Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization # Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence # Srbds: Not affected # Tsx async abort: Not affected # * win32 # Architecture=9 # CurrentClockSpeed=2900 # DeviceID=CPU0 # Family=179 # L2CacheSize=40960 # L2CacheSpeed= # Manufacturer=GenuineIntel # MaxClockSpeed=2900 # Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz # ProcessorType=3 # Revision=27142 # # Architecture=9 # CurrentClockSpeed=2900 # DeviceID=CPU1 # Family=179 # L2CacheSize=40960 # L2CacheSpeed= # Manufacturer=GenuineIntel # MaxClockSpeed=2900 # Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz # ProcessorType=3 # Revision=27142 def get_cpu_info(run_lambda): rc, out, err = 0, '', '' if get_platform() == 'linux': rc, out, err = run_lambda('lscpu') elif get_platform() == 'win32': rc, out, err = run_lambda('wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID,\ CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE') elif get_platform() == 'darwin': rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string") cpu_info = 'None' if rc == 0: cpu_info = out else: cpu_info = err return cpu_info 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, patterns=None): """Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages.""" if patterns is None: patterns = DEFAULT_PIP_PATTERNS # People generally have `pip` as `pip` or `pip3` # But here it is invoked as `python -mpip` def run_with_pip(pip): out = run_and_read_all(run_lambda, pip + ["list", "--format=freeze"]) return "\n".join( line for line in out.splitlines() if any(name in line for name in patterns) ) 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 get_cuda_module_loading_config(): if TORCH_AVAILABLE and torch.cuda.is_available(): torch.cuda.init() config = os.environ.get('CUDA_MODULE_LOADING', '') return config else: return "N/A" 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 def get_version_or_na(cfg, prefix): _lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s] return _lst[0] if _lst else 'N/A' cfg = torch._C._show_config().split('\n') hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime') miopen_runtime_version = get_version_or_na(cfg, 'MIOpen') 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", " ") conda_packages = get_conda_packages(run_lambda) 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), cuda_module_loading=get_cuda_module_loading_config(), 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=conda_packages, 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(), cpu_info=get_cpu_info(run_lambda), ) 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} CUDA_MODULE_LOADING set to: {cuda_module_loading} 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} CPU: {cpu_info} 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] ') mutable_dict['cpu_info'] = envinfo.cpu_info 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()