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b75c6461f4
* add more results and improve the example docs Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * 5070 windows timing Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add reference for cpu-only Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> --------- Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
82 lines
2.4 KiB
Python
Vendored
82 lines
2.4 KiB
Python
Vendored
# %% [markdown]
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#
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# What this example does
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# - Run a conversion using the best setup for GPU for the standard pipeline
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#
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# Requirements
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# - Python 3.9+
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# - Install Docling: `pip install docling`
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#
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# How to run
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# - `python docs/examples/gpu_standard_pipeline.py`
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#
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# This example is part of a set of GPU optimization strategies. Read more about it in [GPU support](../../usage/gpu/)
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#
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# ## Example code
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# %%
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import datetime
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import logging
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import time
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from pathlib import Path
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import numpy as np
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from pydantic import TypeAdapter
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from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
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from docling.datamodel.base_models import ConversionStatus, InputFormat
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from docling.datamodel.pipeline_options import (
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ThreadedPdfPipelineOptions,
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)
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from docling.pipeline.threaded_standard_pdf_pipeline import ThreadedStandardPdfPipeline
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from docling.utils.profiling import ProfilingItem
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_log = logging.getLogger(__name__)
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def main():
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logging.getLogger("docling").setLevel(logging.WARNING)
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_log.setLevel(logging.INFO)
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data_folder = Path(__file__).parent / "../../tests/data"
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# input_doc_path = data_folder / "pdf" / "2305.03393v1.pdf" # 14 pages
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input_doc_path = data_folder / "pdf" / "redp5110_sampled.pdf" # 18 pages
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pipeline_options = ThreadedPdfPipelineOptions(
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accelerator_options=AcceleratorOptions(
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device=AcceleratorDevice.CUDA,
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),
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ocr_batch_size=4,
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layout_batch_size=64,
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table_batch_size=4,
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)
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pipeline_options.do_ocr = False
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(
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pipeline_cls=ThreadedStandardPdfPipeline,
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pipeline_options=pipeline_options,
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)
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}
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)
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start_time = time.time()
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doc_converter.initialize_pipeline(InputFormat.PDF)
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init_runtime = time.time() - start_time
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_log.info(f"Pipeline initialized in {init_runtime:.2f} seconds.")
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start_time = time.time()
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conv_result = doc_converter.convert(input_doc_path)
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pipeline_runtime = time.time() - start_time
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assert conv_result.status == ConversionStatus.SUCCESS
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num_pages = len(conv_result.pages)
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_log.info(f"Document converted in {pipeline_runtime:.2f} seconds.")
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_log.info(f" {num_pages / pipeline_runtime:.2f} pages/second.")
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if __name__ == "__main__":
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main()
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