Pydantic field validator and comment restored.

Signed-off-by: ahn <ahn@zurich.ibm.com>

docling/datamodel/pipeline_options.py

@@ -41,7 +41,7 @@ class AcceleratorOptions(BaseSettings):
     num_threads: int = 4
     device: str = "auto"
 
-    @validator("device")
+    @field_validator("device")
     def validate_device(cls, value):
         # "auto", "cpu", "cuda", "mps", or "cuda:N"
         if value in {d.value for d in AcceleratorDevice} or re.match(
@@ -55,6 +55,15 @@ class AcceleratorOptions(BaseSettings):
     @model_validator(mode="before")
     @classmethod
     def check_alternative_envvars(cls, data: Any) -> Any:
+        r"""
+        Set num_threads from the "alternative" envvar OMP_NUM_THREADS.
+        The alternative envvar is used only if it is valid and the regular envvar is not set.
+
+        Notice: The standard pydantic settings mechanism with parameter "aliases" does not provide
+        the same functionality. In case the alias envvar is set and the user tries to override the
+        parameter in settings initialization, Pydantic treats the parameter provided in __init__()
+        as an extra input instead of simply overwriting the evvar value for that parameter.
+        """
         if isinstance(data, dict):
             input_num_threads = data.get("num_threads")
             if input_num_threads is None:

docling/models/easyocr_model.py

@@ -18,7 +18,6 @@ from docling.datamodel.settings import settings
 from docling.models.base_ocr_model import BaseOcrModel
 from docling.utils.accelerator_utils import decide_device
 from docling.utils.profiling import TimeRecorder
-from docling.utils.utils import download_url_with_progress
 
 _log = logging.getLogger(__name__)
 
@@ -56,6 +55,7 @@ class EasyOcrModel(BaseOcrModel):
                         for x in [
                             AcceleratorDevice.CUDA.value,
                             AcceleratorDevice.MPS.value,
+                            "cuda:",
                         ]
                     ]
                 )
@@ -82,40 +82,6 @@ class EasyOcrModel(BaseOcrModel):
                 verbose=False,
             )
 
-    @staticmethod
-    def download_models(
-        detection_models: List[str] = ["craft"],
-        recognition_models: List[str] = ["english_g2", "latin_g2"],
-        local_dir: Optional[Path] = None,
-        force: bool = False,
-        progress: bool = False,
-    ) -> Path:
-        # Models are located in https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/config.py
-        from easyocr.config import detection_models as det_models_dict
-        from easyocr.config import recognition_models as rec_models_dict
-
-        if local_dir is None:
-            local_dir = settings.cache_dir / "models" / EasyOcrModel._model_repo_folder
-
-        local_dir.mkdir(parents=True, exist_ok=True)
-
-        # Collect models to download
-        download_list = []
-        for model_name in detection_models:
-            if model_name in det_models_dict:
-                download_list.append(det_models_dict[model_name])
-        for model_name in recognition_models:
-            if model_name in rec_models_dict["gen2"]:
-                download_list.append(rec_models_dict["gen2"][model_name])
-
-        # Download models
-        for model_details in download_list:
-            buf = download_url_with_progress(model_details["url"], progress=progress)
-            with zipfile.ZipFile(buf, "r") as zip_ref:
-                zip_ref.extractall(local_dir)
-
-        return local_dir
-
     def __call__(
         self, conv_res: ConversionResult, page_batch: Iterable[Page]
     ) -> Iterable[Page]:

docs/examples/run_with_accelerator.py

@@ -31,9 +31,7 @@ def main():
     # )
 
     # easyocr doesnt support cuda:N allocation
-    # accelerator_options = AcceleratorOptions(
-    #     num_threads=8, device="cuda:1"
-    # )
+    # accelerator_options = AcceleratorOptions(num_threads=8, device="cuda:0")
 
     pipeline_options = PdfPipelineOptions()
     pipeline_options.accelerator_options = accelerator_options
@@ -59,8 +57,8 @@ def main():
     # List with total time per document
     doc_conversion_secs = conversion_result.timings["pipeline_total"].times
 
-    md = doc.export_to_markdown()
-    print(md)
+    # md = doc.export_to_markdown()
+    # print(md)
     print(f"Conversion secs: {doc_conversion_secs}")