Source code for monai.config.deviceconfig

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import platform
import re
import sys
from collections import OrderedDict

import numpy as np
import torch

import monai
from monai.utils import OptionalImportError, get_package_version, optional_import

    import itk  # type: ignore

    itk_version = itk.Version.GetITKVersion()
    del itk
except (ImportError, AttributeError):
    itk_version = "NOT INSTALLED or UNKNOWN VERSION."

    _, HAS_EXT = optional_import("monai._C")
    USE_COMPILED = HAS_EXT and os.getenv("BUILD_MONAI", "0") == "1"
except (OptionalImportError, ImportError, AttributeError):

psutil, has_psutil = optional_import("psutil")
psutil_version = psutil.__version__ if has_psutil else "NOT INSTALLED or UNKNOWN VERSION."

__all__ = [

def get_config_values():
    Read the package versions into a dictionary.
    output = OrderedDict()

    output["MONAI"] = monai.__version__
    output["Numpy"] = np.version.full_version
    output["Pytorch"] = torch.__version__

    return output

def get_optional_config_values():
    Read the optional package versions into a dictionary.
    output = OrderedDict()

    output["Pytorch Ignite"] = get_package_version("ignite")
    output["Nibabel"] = get_package_version("nibabel")
    output["scikit-image"] = get_package_version("skimage")
    output["Pillow"] = get_package_version("PIL")
    output["Tensorboard"] = get_package_version("tensorboard")
    output["gdown"] = get_package_version("gdown")
    output["TorchVision"] = get_package_version("torchvision")
    output["ITK"] = itk_version
    output["tqdm"] = get_package_version("tqdm")
    output["lmdb"] = get_package_version("lmdb")
    output["psutil"] = psutil_version

    return output

def set_visible_devices(*dev_inds):
    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, dev_inds))

def _dict_append(in_dict, key, fn):
        in_dict[key] = fn() if callable(fn) else fn
    except BaseException:
        in_dict[key] = "UNKNOWN for given OS"

[docs]def get_system_info() -> OrderedDict: """ Get system info as an ordered dictionary. """ output: OrderedDict = OrderedDict() _dict_append(output, "System", platform.system) if output["System"] == "Windows": _dict_append(output, "Win32 version", platform.win32_ver) if hasattr(platform, "win32_edition"): _dict_append(output, "Win32 edition", platform.win32_edition) # type:ignore[attr-defined] elif output["System"] == "Darwin": _dict_append(output, "Mac version", lambda: platform.mac_ver()[0]) else: with open("/etc/os-release", "r") as rel_f: linux_ver ='PRETTY_NAME="(.*)"', if linux_ver: _dict_append(output, "Linux version", lambda: _dict_append(output, "Platform", platform.platform) _dict_append(output, "Processor", platform.processor) _dict_append(output, "Machine", platform.machine) _dict_append(output, "Python version", platform.python_version) if not has_psutil: _dict_append(output, "`psutil` missing", lambda: "run `pip install monai[psutil]`") else: p = psutil.Process() with p.oneshot(): _dict_append(output, "Process name", _dict_append(output, "Command", p.cmdline) _dict_append(output, "Open files", p.open_files) _dict_append(output, "Num physical CPUs", lambda: psutil.cpu_count(logical=False)) _dict_append(output, "Num logical CPUs", lambda: psutil.cpu_count(logical=True)) _dict_append(output, "Num usable CPUs", lambda: len(psutil.Process().cpu_affinity())) _dict_append(output, "CPU usage (%)", lambda: psutil.cpu_percent(percpu=True)) _dict_append(output, "CPU freq. (MHz)", lambda: round(psutil.cpu_freq(percpu=False)[0])) _dict_append( output, "Load avg. in last 1, 5, 15 mins (%)", lambda: [round(x / psutil.cpu_count() * 100, 1) for x in psutil.getloadavg()], ) _dict_append(output, "Disk usage (%)", lambda: psutil.disk_usage(os.getcwd()).percent) _dict_append( output, "Avg. sensor temp. (Celsius)", lambda: np.round( np.mean([item.current for sublist in psutil.sensors_temperatures().values() for item in sublist], 1) ), ) mem = psutil.virtual_memory() _dict_append(output, "Total physical memory (GB)", lambda: round( / 1024 ** 3, 1)) _dict_append(output, "Available memory (GB)", lambda: round(mem.available / 1024 ** 3, 1)) _dict_append(output, "Used memory (GB)", lambda: round(mem.used / 1024 ** 3, 1)) return output
def get_gpu_info() -> OrderedDict: output: OrderedDict = OrderedDict() num_gpus = torch.cuda.device_count() _dict_append(output, "Num GPUs", lambda: num_gpus) _dict_append(output, "Has CUDA", lambda: bool(torch.cuda.is_available())) if output["Has CUDA"]: _dict_append(output, "CUDA version", lambda: torch.version.cuda) cudnn_ver = torch.backends.cudnn.version() _dict_append(output, "cuDNN enabled", lambda: bool(cudnn_ver)) if cudnn_ver: _dict_append(output, "cuDNN version", lambda: cudnn_ver) if num_gpus > 0: _dict_append(output, "Current device", torch.cuda.current_device) if hasattr(torch.cuda, "get_arch_list"): # get_arch_list is new in torch 1.7.1 _dict_append(output, "Library compiled for CUDA architectures", torch.cuda.get_arch_list) for gpu in range(num_gpus): gpu_info = torch.cuda.get_device_properties(gpu) _dict_append(output, f"GPU {gpu} Name", lambda: _dict_append(output, f"GPU {gpu} Is integrated", lambda: bool(gpu_info.is_integrated)) _dict_append(output, f"GPU {gpu} Is multi GPU board", lambda: bool(gpu_info.is_multi_gpu_board)) _dict_append(output, f"GPU {gpu} Multi processor count", lambda: gpu_info.multi_processor_count) _dict_append(output, f"GPU {gpu} Total memory (GB)", lambda: round(gpu_info.total_memory / 1024 ** 3, 1)) _dict_append( output, f"GPU {gpu} Cached memory (GB)", lambda: round(torch.cuda.memory_reserved(gpu) / 1024 ** 3, 1) ) _dict_append( output, f"GPU {gpu} Allocated memory (GB)", lambda: round(torch.cuda.memory_allocated(gpu) / 1024 ** 3, 1) ) _dict_append(output, f"GPU {gpu} CUDA capability (maj.min)", lambda: f"{gpu_info.major}.{gpu_info.minor}") return output if __name__ == "__main__": print_debug_info()