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625 lines
22 KiB
625 lines
22 KiB
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# Unlike the rest of the PyTorch this file must be python2 compliant.
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# This script outputs relevant system environment info
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# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
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import datetime
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import locale
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import re
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import subprocess
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import sys
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import os
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from collections import namedtuple
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try:
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import torch
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TORCH_AVAILABLE = True
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except (ImportError, NameError, AttributeError, OSError):
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TORCH_AVAILABLE = False
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# System Environment Information
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SystemEnv = namedtuple('SystemEnv', [
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'torch_version',
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'is_debug_build',
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'cuda_compiled_version',
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'gcc_version',
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'clang_version',
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'cmake_version',
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'os',
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'libc_version',
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'python_version',
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'python_platform',
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'is_cuda_available',
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'cuda_runtime_version',
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'cuda_module_loading',
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'nvidia_driver_version',
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'nvidia_gpu_models',
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'cudnn_version',
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'pip_version', # 'pip' or 'pip3'
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'pip_packages',
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'conda_packages',
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'hip_compiled_version',
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'hip_runtime_version',
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'miopen_runtime_version',
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'caching_allocator_config',
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'is_xnnpack_available',
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'cpu_info',
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])
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DEFAULT_CONDA_PATTERNS = {
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"torch",
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"numpy",
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"cudatoolkit",
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"soumith",
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"mkl",
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"magma",
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"triton",
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"optree",
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}
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DEFAULT_PIP_PATTERNS = {
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"torch",
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"numpy",
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"mypy",
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"flake8",
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"triton",
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"optree",
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"onnx",
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}
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def run(command):
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"""Return (return-code, stdout, stderr)."""
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shell = True if type(command) is str else False
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p = subprocess.Popen(command, stdout=subprocess.PIPE,
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stderr=subprocess.PIPE, shell=shell)
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raw_output, raw_err = p.communicate()
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rc = p.returncode
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if get_platform() == 'win32':
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enc = 'oem'
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else:
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enc = locale.getpreferredencoding()
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output = raw_output.decode(enc)
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err = raw_err.decode(enc)
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return rc, output.strip(), err.strip()
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def run_and_read_all(run_lambda, command):
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"""Run command using run_lambda; reads and returns entire output if rc is 0."""
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rc, out, _ = run_lambda(command)
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if rc != 0:
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return None
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return out
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def run_and_parse_first_match(run_lambda, command, regex):
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"""Run command using run_lambda, returns the first regex match if it exists."""
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rc, out, _ = run_lambda(command)
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if rc != 0:
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return None
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match = re.search(regex, out)
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if match is None:
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return None
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return match.group(1)
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def run_and_return_first_line(run_lambda, command):
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"""Run command using run_lambda and returns first line if output is not empty."""
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rc, out, _ = run_lambda(command)
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if rc != 0:
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return None
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return out.split('\n')[0]
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def get_conda_packages(run_lambda, patterns=None):
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if patterns is None:
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patterns = DEFAULT_CONDA_PATTERNS
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conda = os.environ.get('CONDA_EXE', 'conda')
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out = run_and_read_all(run_lambda, "{} list".format(conda))
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if out is None:
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return out
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return "\n".join(
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line
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for line in out.splitlines()
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if not line.startswith("#")
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and any(name in line for name in patterns)
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)
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def get_gcc_version(run_lambda):
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return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)')
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def get_clang_version(run_lambda):
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return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)')
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def get_cmake_version(run_lambda):
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return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)')
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def get_nvidia_driver_version(run_lambda):
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if get_platform() == 'darwin':
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cmd = 'kextstat | grep -i cuda'
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return run_and_parse_first_match(run_lambda, cmd,
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r'com[.]nvidia[.]CUDA [(](.*?)[)]')
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smi = get_nvidia_smi()
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return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ')
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def get_gpu_info(run_lambda):
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if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip') and torch.version.hip is not None):
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if TORCH_AVAILABLE and torch.cuda.is_available():
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if torch.version.hip is not None:
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prop = torch.cuda.get_device_properties(0)
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if hasattr(prop, "gcnArchName"):
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gcnArch = " ({})".format(prop.gcnArchName)
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else:
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gcnArch = "NoGCNArchNameOnOldPyTorch"
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else:
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gcnArch = ""
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return torch.cuda.get_device_name(None) + gcnArch
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return None
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smi = get_nvidia_smi()
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uuid_regex = re.compile(r' \(UUID: .+?\)')
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rc, out, _ = run_lambda(smi + ' -L')
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if rc != 0:
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return None
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# Anonymize GPUs by removing their UUID
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return re.sub(uuid_regex, '', out)
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def get_running_cuda_version(run_lambda):
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return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)')
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def get_cudnn_version(run_lambda):
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"""Return a list of libcudnn.so; it's hard to tell which one is being used."""
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if get_platform() == 'win32':
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system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
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cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%")
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where_cmd = os.path.join(system_root, 'System32', 'where')
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cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
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elif get_platform() == 'darwin':
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# CUDA libraries and drivers can be found in /usr/local/cuda/. See
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# https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
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# https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
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# Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
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cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*'
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else:
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cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
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rc, out, _ = run_lambda(cudnn_cmd)
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# find will return 1 if there are permission errors or if not found
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if len(out) == 0 or (rc != 1 and rc != 0):
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l = os.environ.get('CUDNN_LIBRARY')
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if l is not None and os.path.isfile(l):
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return os.path.realpath(l)
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return None
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files_set = set()
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for fn in out.split('\n'):
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fn = os.path.realpath(fn) # eliminate symbolic links
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if os.path.isfile(fn):
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files_set.add(fn)
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if not files_set:
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return None
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# Alphabetize the result because the order is non-deterministic otherwise
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files = sorted(files_set)
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if len(files) == 1:
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return files[0]
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result = '\n'.join(files)
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return 'Probably one of the following:\n{}'.format(result)
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def get_nvidia_smi():
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# Note: nvidia-smi is currently available only on Windows and Linux
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smi = 'nvidia-smi'
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if get_platform() == 'win32':
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system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
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program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files')
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legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi)
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new_path = os.path.join(system_root, 'System32', smi)
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smis = [new_path, legacy_path]
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for candidate_smi in smis:
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if os.path.exists(candidate_smi):
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smi = '"{}"'.format(candidate_smi)
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break
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return smi
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# example outputs of CPU infos
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# * linux
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# Architecture: x86_64
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# CPU op-mode(s): 32-bit, 64-bit
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# Address sizes: 46 bits physical, 48 bits virtual
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# Byte Order: Little Endian
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# CPU(s): 128
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# On-line CPU(s) list: 0-127
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# Vendor ID: GenuineIntel
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# Model name: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
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# CPU family: 6
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# Model: 106
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# Thread(s) per core: 2
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# Core(s) per socket: 32
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# Socket(s): 2
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# Stepping: 6
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# BogoMIPS: 5799.78
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# Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr
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# sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
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# xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16
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# pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand
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# hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced
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# fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap
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# avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1
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# xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq
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# avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
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# Virtualization features:
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# Hypervisor vendor: KVM
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# Virtualization type: full
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# Caches (sum of all):
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# L1d: 3 MiB (64 instances)
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# L1i: 2 MiB (64 instances)
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# L2: 80 MiB (64 instances)
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# L3: 108 MiB (2 instances)
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# NUMA:
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# NUMA node(s): 2
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# NUMA node0 CPU(s): 0-31,64-95
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# NUMA node1 CPU(s): 32-63,96-127
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# Vulnerabilities:
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# Itlb multihit: Not affected
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# L1tf: Not affected
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# Mds: Not affected
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# Meltdown: Not affected
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# Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
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# Retbleed: Not affected
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# Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
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# Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
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# Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
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# Srbds: Not affected
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# Tsx async abort: Not affected
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# * win32
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# Architecture=9
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# CurrentClockSpeed=2900
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# DeviceID=CPU0
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# Family=179
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# L2CacheSize=40960
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# L2CacheSpeed=
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# Manufacturer=GenuineIntel
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# MaxClockSpeed=2900
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# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
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# ProcessorType=3
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# Revision=27142
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#
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# Architecture=9
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# CurrentClockSpeed=2900
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# DeviceID=CPU1
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# Family=179
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# L2CacheSize=40960
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# L2CacheSpeed=
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# Manufacturer=GenuineIntel
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# MaxClockSpeed=2900
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# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
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# ProcessorType=3
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# Revision=27142
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def get_cpu_info(run_lambda):
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rc, out, err = 0, '', ''
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if get_platform() == 'linux':
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rc, out, err = run_lambda('lscpu')
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elif get_platform() == 'win32':
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rc, out, err = run_lambda('wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
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CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE')
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elif get_platform() == 'darwin':
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rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
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cpu_info = 'None'
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if rc == 0:
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cpu_info = out
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else:
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cpu_info = err
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return cpu_info
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def get_platform():
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if sys.platform.startswith('linux'):
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return 'linux'
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elif sys.platform.startswith('win32'):
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return 'win32'
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elif sys.platform.startswith('cygwin'):
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return 'cygwin'
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elif sys.platform.startswith('darwin'):
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return 'darwin'
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else:
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return sys.platform
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def get_mac_version(run_lambda):
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return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)')
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def get_windows_version(run_lambda):
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system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
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wmic_cmd = os.path.join(system_root, 'System32', 'Wbem', 'wmic')
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findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
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return run_and_read_all(run_lambda, '{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd))
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def get_lsb_version(run_lambda):
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return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)')
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def check_release_file(run_lambda):
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return run_and_parse_first_match(run_lambda, 'cat /etc/*-release',
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r'PRETTY_NAME="(.*)"')
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def get_os(run_lambda):
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from platform import machine
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platform = get_platform()
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if platform == 'win32' or platform == 'cygwin':
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return get_windows_version(run_lambda)
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if platform == 'darwin':
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version = get_mac_version(run_lambda)
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if version is None:
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return None
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return 'macOS {} ({})'.format(version, machine())
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if platform == 'linux':
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# Ubuntu/Debian based
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desc = get_lsb_version(run_lambda)
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if desc is not None:
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return '{} ({})'.format(desc, machine())
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# Try reading /etc/*-release
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desc = check_release_file(run_lambda)
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if desc is not None:
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return '{} ({})'.format(desc, machine())
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return '{} ({})'.format(platform, machine())
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# Unknown platform
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return platform
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def get_python_platform():
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import platform
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return platform.platform()
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def get_libc_version():
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import platform
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if get_platform() != 'linux':
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return 'N/A'
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return '-'.join(platform.libc_ver())
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def get_pip_packages(run_lambda, patterns=None):
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"""Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages."""
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if patterns is None:
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patterns = DEFAULT_PIP_PATTERNS
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# People generally have `pip` as `pip` or `pip3`
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# But here it is invoked as `python -mpip`
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def run_with_pip(pip):
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out = run_and_read_all(run_lambda, pip + ["list", "--format=freeze"])
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return "\n".join(
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line
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for line in out.splitlines()
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if any(name in line for name in patterns)
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)
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pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
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out = run_with_pip([sys.executable, '-mpip'])
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return pip_version, out
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def get_cachingallocator_config():
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ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '')
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return ca_config
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def get_cuda_module_loading_config():
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if TORCH_AVAILABLE and torch.cuda.is_available():
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torch.cuda.init()
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config = os.environ.get('CUDA_MODULE_LOADING', '')
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return config
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else:
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return "N/A"
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def is_xnnpack_available():
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if TORCH_AVAILABLE:
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import torch.backends.xnnpack
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return str(torch.backends.xnnpack.enabled) # type: ignore[attr-defined]
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else:
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return "N/A"
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def get_env_info():
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run_lambda = run
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pip_version, pip_list_output = get_pip_packages(run_lambda)
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if TORCH_AVAILABLE:
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version_str = torch.__version__
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debug_mode_str = str(torch.version.debug)
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cuda_available_str = str(torch.cuda.is_available())
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cuda_version_str = torch.version.cuda
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if not hasattr(torch.version, 'hip') or torch.version.hip is None: # cuda version
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hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
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else: # HIP version
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def get_version_or_na(cfg, prefix):
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_lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s]
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return _lst[0] if _lst else 'N/A'
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cfg = torch._C._show_config().split('\n')
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hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime')
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miopen_runtime_version = get_version_or_na(cfg, 'MIOpen')
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cuda_version_str = 'N/A'
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hip_compiled_version = torch.version.hip
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else:
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version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A'
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hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
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sys_version = sys.version.replace("\n", " ")
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conda_packages = get_conda_packages(run_lambda)
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return SystemEnv(
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torch_version=version_str,
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is_debug_build=debug_mode_str,
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python_version='{} ({}-bit runtime)'.format(sys_version, sys.maxsize.bit_length() + 1),
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python_platform=get_python_platform(),
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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()
|