benchmarker API

src/deepsparse/benchmark/api/__init__.py

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+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

src/deepsparse/benchmark/api/benchmarker.py

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+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Optional
+
+from deepsparse.benchmark.benchmark_model import benchmark_model
+from deepsparse.benchmark.benchmark_pipeline import benchmark_pipeline
+
+
+class Benchmarker:
+    def __init__(self, model, pipeline, url):
+        self.model = model
+        self.pipeline = pipeline
+        self.url = url
+
+    def __call__(self, **kwargs):
+
+        if self.model:
+            benchmark_model(model_path=self.model, **kwargs)
+
+        if self.pipeline:
+            benchmark_pipeline(model_path=self.pipeline, **kwargs)
+
+        if self.url:
+            # benchmark with url here
+            pass
+
+    @staticmethod
+    def benchmark(
+        model: Optional[str] = None,
+        pipeline: Optional[str] = None,
+        url: Optional[str] = None,
+        **kwargs,
+    ):
+        if len((model, pipeline, url)) != 1:
+            return ValueError("[api.benchmark] Only one input arg required")
+
+        if model:
+            benchmarker = Benchmarker(model=model)
+        elif pipeline:
+            benchmarker = Benchmarker(pipeline=pipeline)
+        elif url:
+            benchmarker = Benchmarker(url=url)
+
+        return benchmarker(**kwargs)