# 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 def is_rocm_pytorch() -> bool: """Check whether the PyTorch is compiled on ROCm.""" is_rocm = False if TORCH_VERSION != "parrots": try: from torch.utils.cpp_extension import ROCM_HOME is_rocm = True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False except ImportError: pass return is_rocm TORCH_VERSION = torch.__version__ def get_build_config(): """Obtain the build information of PyTorch or Parrots.""" if TORCH_VERSION == "parrots": from parrots.config import get_build_info return get_build_info() else: return torch.__config__.show() try: import torch_musa # noqa: F401 IS_MUSA_AVAILABLE = True except Exception: IS_MUSA_AVAILABLE = False def is_musa_available() -> bool: return IS_MUSA_AVAILABLE def is_cuda_available() -> bool: """Returns True if cuda devices exist.""" return torch.cuda.is_available() 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") 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. """ 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 return env_info if __name__ == "__main__": for name, val in collect_env().items(): print(f"{name}: {val}")