Source code for data_juicer

__version__ = '1.3.3'

import os
import subprocess
import sys

from loguru import logger
# allow loading truncated images for some too large images.
from PIL import ImageFile

from data_juicer.utils.availability_utils import _is_package_available
from data_juicer.utils.lazy_loader import LazyLoader

torch = LazyLoader('torch')
ImageFile.LOAD_TRUNCATED_IMAGES = True

# For now, only INFO will be shown. Later the severity level will be changed
# when setup_logger is called to initialize the logger.
logger.remove()
logger.add(sys.stderr, level='INFO')


def _cuda_device_count():
    _torch_available = _is_package_available('torch')

    if _torch_available:
        return torch.cuda.device_count()

    try:
        nvidia_smi_output = subprocess.check_output(['nvidia-smi', '-L'],
                                                    text=True)
        all_devices = nvidia_smi_output.strip().split('\n')

        cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
        if cuda_visible_devices is not None:
            logger.warning(
                'CUDA_VISIBLE_DEVICES is ignored when torch is unavailable. '
                'All detected GPUs will be used.')

        return len(all_devices)
    except Exception:
        # nvidia-smi not found or other error
        return 0




def cuda_device_count():
    return _cuda_device_count()





def is_cuda_available():
    return cuda_device_count() > 0