default model support for Pipeline.register

src/deepsparse/image_classification/pipelines.py

@@ -42,7 +42,13 @@
     cv2_error = cv2_import_error
 
 
-@Pipeline.register(task="image_classification")
+@Pipeline.register(
+    task="image_classification",
+    default_model_path=(
+        "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/"
+        "imagenet/pruned85_quant-none-vnni"
+    ),
+)
 class ImageClassificationPipeline(Pipeline):
     """
     Image classification pipeline for DeepSparse

src/deepsparse/pipeline.py

@@ -27,7 +27,7 @@
 from pydantic import BaseModel, Field
 
 from deepsparse import Engine, Scheduler
-from deepsparse.benchmark_model.ort_engine import ORTEngine
+from deepsparse.benchmark.ort_engine import ORTEngine
 from deepsparse.tasks import SupportedTasks
 
 
@@ -170,7 +170,7 @@ def __call__(self, *args, **kwargs) -> BaseModel:
     @staticmethod
     def create(
         task: str,
-        model_path: str,
+        model_path: str = None,
         engine_type: str = DEEPSPARSE_ENGINE,
         batch_size: int = 1,
         num_cores: int = None,
@@ -182,7 +182,7 @@ def create(
         """
         :param task: name of task to create a pipeline for
         :param model_path: path on local system or SparseZoo stub to load the model
-            from
+            from. Some tasks may have a default model path
         :param engine_type: inference engine to use. Currently supported values
             include 'deepsparse' and 'onnxruntime'. Default is 'deepsparse'
         :param batch_size: static batch size to use for inference. Default is 1
@@ -214,7 +214,22 @@ def create(
                 f"registered pipelines: {list(_REGISTERED_PIPELINES.keys())}"
             )
 
-        return _REGISTERED_PIPELINES[task](
+        pipeline_constructor = _REGISTERED_PIPELINES[task]
+
+        if (
+            model_path is None
+            and hasattr(pipeline_constructor, "default_model_path")
+            and pipeline_constructor.default_model_path
+        ):
+            model_path = pipeline_constructor.default_model_path
+
+        if model_path is None:
+            raise ValueError(
+                f"No model_path provided for pipeline {pipeline_constructor}. Must "
+                "provide a model path for pipelines that do not have a default defined"
+            )
+
+        return pipeline_constructor(
             model_path=model_path,
             engine_type=engine_type,
             batch_size=batch_size,
@@ -226,7 +241,12 @@ def create(
         )
 
     @classmethod
-    def register(cls, task: str, task_aliases: Optional[List[str]] = None):
+    def register(
+        cls,
+        task: str,
+        task_aliases: Optional[List[str]] = None,
+        default_model_path: Optional[str] = None,
+    ):
         """
         Pipeline implementer class decorator that registers the pipeline
         task name and its aliases as valid tasks that can be used to load
@@ -238,6 +258,8 @@ def register(cls, task: str, task_aliases: Optional[List[str]] = None):
         :param task: main task name of this pipeline
         :param task_aliases: list of extra task names that may be used to reference
             this pipeline. Default is None
+        :param default_model_path: path (ie zoo stub) to use as default for this
+            task if None is provided
         """
         task_names = [task]
         if task_aliases:
@@ -266,6 +288,7 @@ def _register_pipeline_tasks_decorator(pipeline_class: Pipeline):
             # set task and task_aliases as class level property
             pipeline_class.task = task
             pipeline_class.task_aliases = task_aliases
+            pipeline_class.default_model_path = default_model_path
 
             return pipeline_class
 

src/deepsparse/transformers/pipelines/question_answering.py

@@ -79,6 +79,10 @@ class QuestionAnsweringOutput(BaseModel):
 @Pipeline.register(
     task="question_answering",
     task_aliases=["qa"],
+    default_model_path=(
+        "zoo:nlp/question_answering/bert-base/pytorch/huggingface/"
+        "squad/12layer_pruned80_quant-none-vnni"
+    ),
 )
 class QuestionAnsweringPipeline(TransformersPipeline):
     """

src/deepsparse/transformers/pipelines/text_classification.py

@@ -77,6 +77,10 @@ class TextClassificationOutput(BaseModel):
 @Pipeline.register(
     task="text_classification",
     task_aliases=["glue", "sentiment_analysis"],
+    default_model_path=(
+        "zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/"
+        "sst2/12layer_pruned80_quant-none-vnni"
+    ),
 )
 class TextClassificationPipeline(TransformersPipeline):
     """

src/deepsparse/transformers/pipelines/token_classification.py

@@ -123,6 +123,10 @@ class TokenClassificationOutput(BaseModel):
 @Pipeline.register(
     task="token_classification",
     task_aliases=["ner"],
+    default_model_path=(
+        "zoo:nlp/token_classification/bert-base/pytorch/huggingface/"
+        "conll2003/12layer_pruned80_quant-none-vnni"
+    ),
 )
 class TokenClassificationPipeline(TransformersPipeline):
     """

src/deepsparse/yolo/pipelines.py

@@ -32,7 +32,12 @@
     cv2_error = cv2_import_error
 
 
-@Pipeline.register(task="yolo")
+@Pipeline.register(
+    task="yolo",
+    default_model_path=(
+        "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95"
+    ),
+)
 class YOLOPipeline(Pipeline):
     """
     Image Segmentation YOLO pipeline for DeepSparse