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Source code for mmengine.utils.dl_utils.collect_env

# Copyright (c) OpenMMLab. All rights reserved.
"""This file holding some environment constant for sharing by other files."""
import os
import os.path as osp
import subprocess
import sys
from collections import OrderedDict, defaultdict

import numpy as np
import torch

import mmengine
from mmengine.device import is_cuda_available, is_musa_available
from .parrots_wrapper import TORCH_VERSION, get_build_config, is_rocm_pytorch


def _get_cuda_home():
    if TORCH_VERSION == 'parrots':
        from parrots.utils.build_extension import CUDA_HOME
    else:
        if is_rocm_pytorch():
            from torch.utils.cpp_extension import ROCM_HOME
            CUDA_HOME = ROCM_HOME
        else:
            from torch.utils.cpp_extension import CUDA_HOME
    return CUDA_HOME


def _get_musa_home():
    return os.environ.get('MUSA_HOME')


[docs]def collect_env(): """Collect the information of the running environments. Returns: dict: The environment information. The following fields are contained. - sys.platform: The variable of ``sys.platform``. - Python: Python version. - CUDA available: Bool, indicating if CUDA is available. - GPU devices: Device type of each GPU. - CUDA_HOME (optional): The env var ``CUDA_HOME``. - NVCC (optional): NVCC version. - GCC: GCC version, "n/a" if GCC is not installed. - MSVC: Microsoft Virtual C++ Compiler version, Windows only. - PyTorch: PyTorch version. - PyTorch compiling details: The output of \ ``torch.__config__.show()``. - TorchVision (optional): TorchVision version. - OpenCV (optional): OpenCV version. - MMENGINE: MMENGINE version. """ from distutils import errors env_info = OrderedDict() env_info['sys.platform'] = sys.platform env_info['Python'] = sys.version.replace('\n', '') cuda_available = is_cuda_available() musa_available = is_musa_available() env_info['CUDA available'] = cuda_available env_info['MUSA available'] = musa_available env_info['numpy_random_seed'] = np.random.get_state()[1][0] if cuda_available: devices = defaultdict(list) for k in range(torch.cuda.device_count()): devices[torch.cuda.get_device_name(k)].append(str(k)) for name, device_ids in devices.items(): env_info['GPU ' + ','.join(device_ids)] = name CUDA_HOME = _get_cuda_home() env_info['CUDA_HOME'] = CUDA_HOME if CUDA_HOME is not None and osp.isdir(CUDA_HOME): if CUDA_HOME == '/opt/rocm': try: nvcc = osp.join(CUDA_HOME, 'hip/bin/hipcc') nvcc = subprocess.check_output( f'"{nvcc}" --version', shell=True) nvcc = nvcc.decode('utf-8').strip() release = nvcc.rfind('HIP version:') build = nvcc.rfind('') nvcc = nvcc[release:build].strip() except subprocess.SubprocessError: nvcc = 'Not Available' else: try: nvcc = osp.join(CUDA_HOME, 'bin/nvcc') nvcc = subprocess.check_output(f'"{nvcc}" -V', shell=True) nvcc = nvcc.decode('utf-8').strip() release = nvcc.rfind('Cuda compilation tools') build = nvcc.rfind('Build ') nvcc = nvcc[release:build].strip() except subprocess.SubprocessError: nvcc = 'Not Available' env_info['NVCC'] = nvcc elif musa_available: devices = defaultdict(list) for k in range(torch.musa.device_count()): devices[torch.musa.get_device_name(k)].append(str(k)) for name, device_ids in devices.items(): env_info['GPU ' + ','.join(device_ids)] = name MUSA_HOME = _get_musa_home() env_info['MUSA_HOME'] = MUSA_HOME if MUSA_HOME is not None and osp.isdir(MUSA_HOME): try: mcc = osp.join(MUSA_HOME, 'bin/mcc') subprocess.check_output(f'"{mcc}" -v', shell=True) except subprocess.SubprocessError: mcc = 'Not Available' env_info['mcc'] = mcc try: # Check C++ Compiler. # For Unix-like, sysconfig has 'CC' variable like 'gcc -pthread ...', # indicating the compiler used, we use this to get the compiler name import io import sysconfig cc = sysconfig.get_config_var('CC') if cc: cc = osp.basename(cc.split()[0]) cc_info = subprocess.check_output(f'{cc} --version', shell=True) env_info['GCC'] = cc_info.decode('utf-8').partition( '\n')[0].strip() else: # on Windows, cl.exe is not in PATH. We need to find the path. # distutils.ccompiler.new_compiler() returns a msvccompiler # object and after initialization, path to cl.exe is found. import locale import os from distutils.ccompiler import new_compiler ccompiler = new_compiler() ccompiler.initialize() cc = subprocess.check_output( f'{ccompiler.cc}', stderr=subprocess.STDOUT, shell=True) encoding = os.device_encoding( sys.stdout.fileno()) or locale.getpreferredencoding() env_info['MSVC'] = cc.decode(encoding).partition('\n')[0].strip() env_info['GCC'] = 'n/a' except (subprocess.CalledProcessError, errors.DistutilsPlatformError): env_info['GCC'] = 'n/a' except io.UnsupportedOperation as e: # JupyterLab on Windows changes sys.stdout, which has no `fileno` attr # Refer to: https://github.com/open-mmlab/mmengine/issues/931 # TODO: find a solution to get compiler info in Windows JupyterLab, # while preserving backward-compatibility in other systems. env_info['MSVC'] = f'n/a, reason: {str(e)}' env_info['PyTorch'] = torch.__version__ env_info['PyTorch compiling details'] = get_build_config() try: import torchvision env_info['TorchVision'] = torchvision.__version__ except ModuleNotFoundError: pass try: import cv2 env_info['OpenCV'] = cv2.__version__ except ImportError: pass env_info['MMEngine'] = mmengine.__version__ return env_info

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