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run_with_accelerator.py
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from pathlib import Path
from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import (
AcceleratorDevice,
AcceleratorOptions,
PdfPipelineOptions,
TesseractCliOcrOptions,
TesseractOcrOptions,
)
from docling.datamodel.settings import settings
from docling.document_converter import DocumentConverter, PdfFormatOption
def main():
input_doc = Path("./tests/data/pdf/2206.01062.pdf")
# Explicitly set the accelerator
# accelerator_options = AcceleratorOptions(
# num_threads=8, device=AcceleratorDevice.AUTO
# )
accelerator_options = AcceleratorOptions(
num_threads=8, device=AcceleratorDevice.CPU
)
# accelerator_options = AcceleratorOptions(
# num_threads=8, device=AcceleratorDevice.MPS
# )
# accelerator_options = AcceleratorOptions(
# num_threads=8, device=AcceleratorDevice.CUDA
# )
# easyocr doesnt support cuda:N allocation, defaults to cuda:0
# accelerator_options = AcceleratorOptions(num_threads=8, device="cuda:1")
pipeline_options = PdfPipelineOptions()
pipeline_options.accelerator_options = accelerator_options
pipeline_options.do_ocr = True
pipeline_options.do_table_structure = True
pipeline_options.table_structure_options.do_cell_matching = True
converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(
pipeline_options=pipeline_options,
)
}
)
# Enable the profiling to measure the time spent
settings.debug.profile_pipeline_timings = True
# Convert the document
conversion_result = converter.convert(input_doc)
doc = conversion_result.document
# List with total time per document
doc_conversion_secs = conversion_result.timings["pipeline_total"].times
md = doc.export_to_markdown()
print(md)
print(f"Conversion secs: {doc_conversion_secs}")
if __name__ == "__main__":
main()