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* Update .py examples with clearer guidance, update out of date imports and calls Signed-off-by: Mingxuan Zhao <43148277+mingxzhao@users.noreply.github.com> * Fix minimal.py string error, fix ruff format error Signed-off-by: Mingxuan Zhao <43148277+mingxzhao@users.noreply.github.com> * fix more CI issues Signed-off-by: Mingxuan Zhao <43148277+mingxzhao@users.noreply.github.com> --------- Signed-off-by: Mingxuan Zhao <43148277+mingxzhao@users.noreply.github.com>
83 lines
2.7 KiB
Python
Vendored
83 lines
2.7 KiB
Python
Vendored
# %% [markdown]
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# Run conversion with an explicit accelerator configuration (CPU/MPS/CUDA).
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#
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# What this example does
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# - Shows how to select the accelerator device and thread count.
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# - Enables OCR and table structure to exercise compute paths, and prints timings.
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#
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# How to run
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# - From the repo root: `python docs/examples/run_with_accelerator.py`.
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# - Toggle the commented `AcceleratorOptions` examples to try AUTO/MPS/CUDA.
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#
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# Notes
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# - EasyOCR does not support `cuda:N` device selection (defaults to `cuda:0`).
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# - `settings.debug.profile_pipeline_timings = True` prints profiling details.
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# - `AcceleratorDevice.MPS` is macOS-only; `CUDA` requires a compatible GPU and
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# CUDA-enabled PyTorch build. CPU mode works everywhere.
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# %%
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from pathlib import Path
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from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import (
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PdfPipelineOptions,
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)
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from docling.datamodel.settings import settings
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from docling.document_converter import DocumentConverter, PdfFormatOption
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def main():
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data_folder = Path(__file__).parent / "../../tests/data"
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input_doc_path = data_folder / "pdf/2206.01062.pdf"
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# Explicitly set the accelerator
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# accelerator_options = AcceleratorOptions(
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# num_threads=8, device=AcceleratorDevice.AUTO
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# )
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accelerator_options = AcceleratorOptions(
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num_threads=8, device=AcceleratorDevice.CPU
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)
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# accelerator_options = AcceleratorOptions(
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# num_threads=8, device=AcceleratorDevice.MPS
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# )
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# accelerator_options = AcceleratorOptions(
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# num_threads=8, device=AcceleratorDevice.CUDA
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# )
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# easyocr doesnt support cuda:N allocation, defaults to cuda:0
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# accelerator_options = AcceleratorOptions(num_threads=8, device="cuda:1")
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pipeline_options = PdfPipelineOptions()
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pipeline_options.accelerator_options = accelerator_options
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pipeline_options.do_ocr = True
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pipeline_options.do_table_structure = True
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pipeline_options.table_structure_options.do_cell_matching = True
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converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(
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pipeline_options=pipeline_options,
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)
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}
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)
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# Enable the profiling to measure the time spent
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settings.debug.profile_pipeline_timings = True
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# Convert the document
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conversion_result = converter.convert(input_doc_path)
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doc = conversion_result.document
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# List with total time per document
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doc_conversion_secs = conversion_result.timings["pipeline_total"].times
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md = doc.export_to_markdown()
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print(md)
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print(f"Conversion secs: {doc_conversion_secs}")
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if __name__ == "__main__":
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main()
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