Source code for monai.config.deviceconfig

# Copyright (c) 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.

from __future__ import annotations

import getpass
import os
import platform
import re
import sys
from collections import OrderedDict
from typing import TextIO

import numpy as np
import torch

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

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

USE_META_DICT = os.environ.get("USE_META_DICT", "0") == "1"  # set to True for compatibility, use meta dict.

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["ITK"] = get_package_version("itk")
    output["Nibabel"] = get_package_version("nibabel")
    output["scikit-image"] = get_package_version("skimage")
    output["scipy"] = get_package_version("scipy")
    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["tqdm"] = get_package_version("tqdm")
    output["lmdb"] = get_package_version("lmdb")
    output["psutil"] = psutil_version
    output["pandas"] = get_package_version("pandas")
    output["einops"] = get_package_version("einops")
    output["transformers"] = get_package_version("transformers")
    output["mlflow"] = get_package_version("mlflow")
    output["pynrrd"] = get_package_version("nrrd")
    output["clearml"] = get_package_version("clearml")

    return output

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) elif output["System"] == "Darwin": _dict_append(output, "Mac version", lambda: platform.mac_ver()[0]) else: with open("/etc/os-release") 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)) _dict_append(output, "NVIDIA_TF32_OVERRIDE", os.environ.get("NVIDIA_TF32_OVERRIDE")) _dict_append(output, "TORCH_ALLOW_TF32_CUBLAS_OVERRIDE", os.environ.get("TORCH_ALLOW_TF32_CUBLAS_OVERRIDE")) if cudnn_ver: _dict_append(output, "cuDNN version", lambda: cudnn_ver) if num_gpus > 0: _dict_append(output, "Current device", torch.cuda.current_device) _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", _dict_append(output, f"GPU {gpu} Is integrated", bool(gpu_info.is_integrated)) _dict_append(output, f"GPU {gpu} Is multi GPU board", bool(gpu_info.is_multi_gpu_board)) _dict_append(output, f"GPU {gpu} Multi processor count", gpu_info.multi_processor_count) _dict_append(output, f"GPU {gpu} Total memory (GB)", round(gpu_info.total_memory / 1024**3, 1)) _dict_append(output, f"GPU {gpu} CUDA capability (maj.min)", f"{gpu_info.major}.{gpu_info.minor}") return output
[docs] class IgniteInfo: """ Config information of the PyTorch ignite package. """ OPT_IMPORT_VERSION = "0.4.4"
if __name__ == "__main__": print_debug_info()